Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories

• URL: http://arxiv.org/abs/2503.03586v2
• Date: Tue, 18 Mar 2025 05:30:00 GMT
• Title: Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories
• Authors: Alperen Yildiz, Sin G. Teo, Yiling Lou, Yebo Feng, Chong Wang, Dinil M. Divakaran,
• Abstract summary: We introduce JitVul, a vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits.<n>We show that ReAct Agents, leveraging thought-action-observation and interprocedural context, perform better than LLMs in distinguishing vulnerable from benign code.
• Score: 8.583591493627276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown promise in software vulnerability detection, particularly on function-level benchmarks like Devign and BigVul. However, real-world detection requires interprocedural analysis, as vulnerabilities often emerge through multi-hop function calls rather than isolated functions. While repository-level benchmarks like ReposVul and VulEval introduce interprocedural context, they remain computationally expensive, lack pairwise evaluation of vulnerability fixes, and explore limited context retrieval, limiting their practicality. We introduce JitVul, a JIT vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits. Built from 879 CVEs spanning 91 vulnerability types, JitVul enables comprehensive evaluation of detection capabilities. Our results show that ReAct Agents, leveraging thought-action-observation and interprocedural context, perform better than LLMs in distinguishing vulnerable from benign code. While prompting strategies like Chain-of-Thought help LLMs, ReAct Agents require further refinement. Both methods show inconsistencies, either misidentifying vulnerabilities or over-analyzing security guards, indicating significant room for improvement.

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