New framework audits prompt sensitivity in LLM vulnerability detection

Researchers have developed PromptAudit, a framework to assess how prompt variations affect Large Language Models (LLMs) used for vulnerability detection. Their study, which tested five prompting strategies on five open-weight models using 1,000 CVEs across 16 programming languages, revealed that standard chain-of-thought prompting yielded the best results. The findings indicate that prompt sensitivity is a critical factor in LLM performance for vulnerability detection and should be a key consideration during evaluation and deployment. AI

IMPACT Highlights the critical role of prompt engineering in ensuring the reliability and accuracy of LLMs for security applications.

RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results for evaluating LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]

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• chain-of-thought prompting
• few-shot prompting
• LLM
• PromptAudit

• paper
• safety

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