AI-Assisted Secure Coding (Appendix C) · Adversarial AI Detection in Inbound Contributions
Profile PRs for LLM-generated code
Verify that PR analytics include structural AST profiling and stylometric or entropy-based heuristics tuned to identify LLM-generated code patterns. Detection in this category is still maturing, so compensating controls are accepted in place of high-precision automated detection: mandatory human review on flagged PRs, sandboxed execution of suspect payloads, and deferred merge until additional signals accrue.
OWASP AISVS v1.01, requirement AC.13.6
In plain English
Profile PRs for patterns typical of LLM-generated code. Detection here is still immature, so mandatory human review, sandboxed execution and delayed merging are acceptable instead.
What good looks like
- Some form of profiling or heuristic runs.
- Flagged PRs get mandatory review.
- Suspect code runs only in a sandbox.
How teams usually do it
- Code profiling tools plus review and sandbox rules for flagged PRs.
Evidence an auditor would accept
- Configuration and flagged examples.
Common mistakes
- Treating detection scores as proof of intent.
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Requirement text from the OWASP AI Security Verification Standard, licensed under CC BY-SA 4.0. This guidance is shared under the same licence.