LLM code reviewers trigger false alarms on indirect crypto API misuse

A study by Sunny Chokshi found that large language models used for code review often generate false alarms when cryptographic API misuse is written indirectly. The models struggled to correctly identify secure code when values were represented through aliases, constants, or default configurations, leading to a significant increase in false positives. This suggests that LLM code reviewers may be overly sensitive to unfamiliar spellings or indirect representations rather than the actual functionality of the code. AI

IMPACT LLM code reviewers may require further development to accurately assess code functionality over superficial representations.

RANK_REASON Research paper detailing findings on LLM code reviewer performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• CryptoBench
• GitHub
• Institute of Electrical and Electronics Engineers
• Open Models
• Python
• Sunny Chokshi
• University of the Cumberlands
• Zenodo

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