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19/08/2026 4:07 pm
TL;DR: GPT-5.5 shows a low vulnerability density of 75 per mLOC and a flat severity distribution in Sonar’s evaluation, but concurrency bugs at 170 per mLOC and sparse documentation still raise the cost of human verification. The practical lesson is that security can improve while review burden shifts upward, so teams need stronger testing and analysis gates, not looser trust.
NHIMG editorial — based on content published by Sonar: GPT-5.5 security metrics and verification debt in Sonar’s LLM evaluation
By the numbers:
- Comments made up only 2.0% of GPT-5.5’s generated code output.
- GPT-5.5’s vulnerability density was 75 per mLOC in Sonar’s evaluation.
Questions worth separating out
Q: How should teams review AI-generated code that handles authentication or secrets?
A: Treat it as controlled security code, not ordinary productivity output.
Q: Why do concurrency bugs remain a problem even when model security looks strong?
A: Because concurrency defects depend on timing, shared state, and execution context, which are often hard to reproduce in review.
Q: How do organisations know whether AI-generated code is creating verification debt?
A: Look for rising review time, more findings per line of code, and repeated defects in the same technical areas.
Practitioner guidance
- Set acceptance thresholds by code risk class Require stricter review and test coverage for generated code that handles concurrency, shared state, authentication, or secrets handling.
- Add concurrency-specific verification gates Run race-condition tests, integration tests, and load-sensitive checks on any generated code that uses async workflows or shared state.
- Track verification effort, not only defect counts Measure how long it takes reviewers to understand and validate generated code, especially when comments are sparse.
What's in the full report
Sonar’s full analysis covers the benchmarking method and the detailed defect patterns this post intentionally leaves at the summary level:
- The task mix, run configuration, and SonarQube analysis method used to evaluate GPT-5.5 across 4,444 Java tasks
- The full severity and category breakdowns for bugs, vulnerabilities, and code smells across the benchmark
- The per-metric charts that show how concurrency, cryptography, and maintainability issues compare across the output
- The broader Sonar LLM Leaderboard context for comparing GPT-5.5 against other evaluated models
👉 Read Sonar’s evaluation of GPT-5.5 security, complexity, and verification debt →
GPT-5.5 code security metrics: what should teams verify first?
Explore further
19/08/2026 4:40 pm
Security metrics alone do not equal secure adoption. GPT-5.5’s vulnerability profile is comparatively strong, but that does not change the governance burden created by high-volume generated code. Security teams should separate defect density from verification capacity, because the latter is what determines whether issues are caught before release. The practical conclusion is that acceptable model output still needs enforceable review thresholds.
A question worth separating out:
Q: Who should own the risk when AI-generated code changes security-sensitive flows?
A: The team that owns the affected control should own the risk. If generated code touches authentication, privilege decisions, secrets, or service-to-service trust, then IAM, security engineering, and platform owners need explicit accountability for review criteria, test requirements, and release approval.
👉 Read our full editorial: GPT-5.5 security is strong, but verification debt still matters
