Adversarial comments bypass LLM code vulnerability detectors

Researchers have developed ALIBI, a novel attack framework that inserts adversarial natural-language comments into source code to bypass LLM-based vulnerability detectors. This technique successfully manipulates over 90% of tested detectors, including advanced multi-agent systems, by steering their reasoning or mimicking tool outputs. The attack exploits the LLM's trust in comments as context, a vulnerability similar to prompt injection in chat interfaces, and highlights a gap in current static analysis tools. AI

IMPACT Highlights a new attack vector against LLM-based code analysis tools, potentially impacting software development security.

RANK_REASON Research paper detailing a new attack technique against LLM vulnerability detectors. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• ALIBI
• LLM
• multi-agent system
• prompt injection
• SAST tools
• static analyzers

• safety
• product

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