Snyk vs Semgrep vs SonarQube: One Missed a Critical Vuln

I ran the same vulnerable codebase through Snyk, Semgrep, and SonarQube. One found a critical deserialization bug. The other two missed it.

That’s not a benchmark — it’s a Tuesday. But it’s also the three-way comparison nobody else has bothered to do. Every other article on snyk vs semgrep vs sonarqube is pairwise — Snyk vs SonarQube, Snyk vs Semgrep — or a feature laundry list. I wanted real findings, real false positives, a real verdict. No “it depends.”

So which tool caught it? And which two missed?

The Test: Same Code, Three Scanners

I built a deliberately vulnerable Django/Node app with five planted bugs: SQL injection, hardcoded secrets, insecure deserialization (pickle.loads on a user-supplied cookie), path traversal, and broken access control. Then I ran each tool with default rules, default configs, latest versions as of May 2026.

Snyk: Code + Open Source. Semgrep: Pro engine. SonarQube: Developer Edition with security rules enabled. This is a static analysis AI comparison run the way a normal team would actually use these tools — out of the box, no custom tuning.

What I measured: true positives (did it find the planted vuln?), false positives (did it cry wolf on safe code?), and time-to-triage.

Disclosure upfront — one codebase isn’t a benchmark suite. I’m pointing to EASE 2024’s academic results where they line up with what I saw. Treat my numbers as one data point, not gospel.

So what did the scanners actually find?

The Results: Findings, False Positives, and the Critical Miss

Semgrep caught the insecure deserialization that Snyk and SonarQube both missed. The vuln was pickle.loads() on a user-supplied cookie — the kind of thing that turns into RCE the moment anyone notices. In any semgrep ai review, the pattern-matching engine’s strength on deserialization bugs shows up consistently. Snyk’s DeepCode AI flagged a generic “untrusted input” warning nearby but didn’t connect it to deserialization. SonarQube didn’t say a word.

Snyk’s redemption was on the dependency side. Among snyk code security features, SCA is the one that actually earns its keep. It flagged an outdated lxml with a known CVE that Semgrep and SonarQube both ignored. Not a fair fight — SCA is Snyk’s core competency, not theirs. It’s also why most security-mature teams end up paying for Snyk’s open-source scanner regardless of what they pick for SAST.

SonarQube found code smells around the vulnerable paths, flagged some style issues, and would have failed the build at the quality gate — for unrelated reasons. Two of the three SAST vulns it had rules for, it missed.

This isn’t an outlier result. EASE 2024’s benchmark put Semgrep CE at 14.3% detection vs Snyk Code’s 11.2% on a much larger corpus. My run was sharper because I planted the vulns, but the order held.

The bigger surprise wasn’t the misses. It was what triaging cost on the wins.

The False Positive Tax Nobody Talks About

G2 rates Snyk’s noise at 6.8/10. Mid-pack on paper, heavier in practice.

The numbers from my run: Snyk surfaced about 22 findings. Twelve needed triage. Nine were noise — patterns that looked exploitable but weren’t, usually because Snyk’s taint analysis didn’t model the framework’s sanitizers correctly. At 10 minutes of senior dev time per triage, that’s 90 minutes per scan you can’t bill or ship. The hidden cost of ai code scanning false positives is the part nobody puts in the product comparison.

Semgrep’s noise was lower because pattern-based rules are tighter by construction. But the moment you start writing your own rules to cover edge cases, suppressing false positives becomes a part-time job. Someone owns it.

SonarQube’s noise leaned the other way — code-quality nits surfaced under the security tab. The cost there isn’t triage time. It’s developers learning to ignore the dashboard entirely.

Speaking of code that nobody’s checking carefully:

The AI-Generated Code Wrinkle

Half the code my consulting clients ship now started life as an LLM completion. That creates failure modes the scanners weren’t designed for: copypasta vulns (the same insecure pattern duplicated across 30 files), over-trusted outputs (calling an LLM and passing the result straight to subprocess), and prompt strings smuggled into code paths. Most ai code security tools 2026 editions aren’t built for this yet.

Semgrep handles these best. I wrote a five-line rule for “any function that calls openai.* and pipes the result into shell or eval” — the exact mistake LLM-generated code makes. Neither of the other tools lets you do that cheaply. For catching these at runtime — before users hit them — production monitoring platforms like LangSmith fill the gap static scanners can’t.

Snyk’s DeepCode AI flagged some patterns but treated them as generic taint flows. It doesn’t know what “LLM output” means.

SonarQube didn’t pick up AI-specific patterns at all. Its ruleset isn’t shaped for 2026’s codebase, and SonarQube AI code quality detection is still mostly the same code-smell engine it’s been for years.

So which one do you actually buy?

Snyk vs Semgrep vs SonarQube: Pricing Reality Check at 10, 50, and 200 Devs

Snyk’s enterprise pricing comes from Vendr’s contract data — public list prices don’t match what buyers actually pay. Worse, SSO sits behind the Ignite tier at $1,260/dev/yr. Real budgeting gotcha.

SonarQube’s price advantage is real, but it assumes you mainly want quality gates. If security depth is the goal, you’ll pay Semgrep or Snyk regardless — SonarQube Community has no taint analysis at all.

Semgrep’s free tier for teams under 10 contributors is the most generous offer in the category. It’s also the one I recommend most often.

Which brings me to the verdict.

The Verdict: Stop Saying “It Depends”

Under 25 devs, SAST-first? Semgrep. The free tier covers you, custom rules let you encode your own threat model, and the detection rate beats the other two on both the public benchmark and my run.

25 to 100 devs, need SCA plus SAST? Snyk for dependency coverage, paired with Semgrep CE for SAST. Yes, run both. The triage cost of Snyk’s false positives is lower than the cost of missing a pickle.loads.

Over 100 devs, code quality is half the goal? SonarQube Enterprise for the gates and price-at-scale. Layer Semgrep on top for actual security depth. SonarQube alone leaves vulnerabilities on the floor — that’s the best AI vulnerability scanner answer if you split the question between quality and security instead of asking for one tool to do both.

The deserialization bug from the opening? Semgrep caught it. If you only buy one scanner and security is the point, that’s your answer. In this snyk vs semgrep vs sonarqube comparison, the detection gap was real — not theoretical. A similar split shows up across AI code review tools — the tool that catches the bug isn’t always the one with the bigger logo.