Context
ALIBI shows LLM-based vulnerability detectors & code reviewers are highly vulnerable to adversarial source-code comments that steer detector reasoning without changing program behavior (attack success >90% across 125 real-world null-pointer vulns, 100% on one system). Most effective: comments steering reasoning or fabricating external-tool results (e.g. claiming a sanitizer check already passed).
Key finding: prompt-level defenses provide limited robustness against adaptive attacks; architectural isolation and pre-detector comment sanitization substantially improve resilience.
Proposal
References
Context
ALIBI shows LLM-based vulnerability detectors & code reviewers are highly vulnerable to adversarial source-code comments that steer detector reasoning without changing program behavior (attack success >90% across 125 real-world null-pointer vulns, 100% on one system). Most effective: comments steering reasoning or fabricating external-tool results (e.g. claiming a sanitizer check already passed).
Key finding: prompt-level defenses provide limited robustness against adaptive attacks; architectural isolation and pre-detector comment sanitization substantially improve resilience.
Proposal
sanitize-commentsmode in the review pipeline: strip comments from diff context before sending to the LLMReferences