EvalSVA: Multi-Agent Evaluators for Next-Gen Software Vulnerability Assessment
Xin-Cheng Wen, Jiaxin Ye, Cuiyun Gao, Lianwei Wu, Qing Liao

TL;DR
EvalSVA introduces a multi-agent framework utilizing multiple Large Language Models to autonomously evaluate software vulnerabilities, improving accuracy and providing human-like explanations in vulnerability assessment tasks.
Contribution
The paper presents a novel multi-agent-based framework with diverse communication strategies and a new multilingual dataset for SV assessment, enhancing effectiveness with limited data.
Findings
EvalSVA outperforms previous methods by 44.12% in accuracy.
It achieves 43.29% F1 score in SV assessment.
Provides human-like explanations and detailed reasoning for assessments.
Abstract
Software Vulnerability (SV) assessment is a crucial process of determining different aspects of SVs (e.g., attack vectors and scope) for developers to effectively prioritize efforts in vulnerability mitigation. It presents a challenging and laborious process due to the complexity of SVs and the scarcity of labeled data. To mitigate the above challenges, we introduce EvalSVA, a multi-agent evaluators team to autonomously deliberate and evaluate various aspects of SV assessment. Specifically, we propose a multi-agent-based framework to simulate vulnerability assessment strategies in real-world scenarios, which employs multiple Large Language Models (LLMs) into an integrated group to enhance the effectiveness of SV assessment in the limited data. We also design diverse communication strategies to autonomously discuss and assess different aspects of SV. Furthermore, we construct a…
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Taxonomy
TopicsSoftware Reliability and Analysis Research · Software Engineering Research · Information and Cyber Security
