Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents

• URL: http://arxiv.org/abs/2505.10961v1
• Date: Fri, 16 May 2025 07:54:10 GMT
• Title: Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents
• Authors: Ratnadira Widyasari, Martin Weyssow, Ivana Clairine Irsan, Han Wei Ang, Frank Liauw, Eng Lieh Ouh, Lwin Khin Shar, Hong Jin Kang, David Lo,
• Abstract summary: VulTrial is a courtroom-inspired framework designed to enhance automated vulnerability detection.<n>It employs four role-specific agents, which are security researcher, code author, moderator, and review board.<n>Using GPT-3.5 and GPT-4o, VulTrial improves the performance by 102.39% and 84.17% over its respective baselines.
• Score: 10.378745306569053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting vulnerabilities in source code remains a critical yet challenging task, especially when benign and vulnerable functions share significant similarities. In this work, we introduce VulTrial, a courtroom-inspired multi-agent framework designed to enhance automated vulnerability detection. It employs four role-specific agents, which are security researcher, code author, moderator, and review board. Through extensive experiments using GPT-3.5 and GPT-4o we demonstrate that Vultrial outperforms single-agent and multi-agent baselines. Using GPT-4o, VulTrial improves the performance by 102.39% and 84.17% over its respective baseline. Additionally, we show that role-specific instruction tuning in multi-agent with small data (50 pair samples) improves the performance of VulTrial further by 139.89% and 118.30%. Furthermore, we analyze the impact of increasing the number of agent interactions on VulTrial's overall performance. While multi-agent setups inherently incur higher costs due to increased token usage, our findings reveal that applying VulTrial to a cost-effective model like GPT-3.5 can improve its performance by 69.89% compared to GPT-4o in a single-agent setting, at a lower overall cost.

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