NAVIGATE: Multi-Agent Graph-Based Vulnerability Detection with Adversarial Severity Assessment

作者：Yang Guo · 发表于：2026 International Conference on Generative Artificial Intelligence and Information Security (GAIIS) · 年份：2026 · DOI：10.1109/gaiis69281.2026.11519278

Automated vulnerability severity assessment is critical for prioritizing security remediation efforts, yet existing approaches face significant challenges. Traditional text-based methods rely solely on vulnerability descriptions, lacking the technical depth present in source code. Meanwhile, Large Language Models (LLMs) have shown promise in code understanding but struggle with long input sequences and lack structured semantic information, leading to unreliable severity predictions. This paper presents NAVIGATE, a multi-agent graph-centric framework that synergizes Code Property Graphs (CPGs) with LLM-powered reasoning for automated vulnerability severity assessment. A key innovation is the deployment of an autonomous ReAct (Reasoning + Acting) agent with a documentation subagent for dynamic query generation, enabling the discovery of complex vulnerability patterns without relying on predefined static rules. Another core contribution is the adversarial multi-agent debate mechanism using AG2 (AutoGen 2), where Security Analyst, Red Team Expert, Blue Team Expert, and Severity Judge agents engage in structured deliberation to achieve consensus-based severity ratings. Our approach employs a multi-stage pipeline comprising: (1) CPG-based code representation, (2) dual-agent vulnerability discovery with context compression, (3) dataflow-guided slicing with Tree-sitter-based syntactic reconstruction, (4) LLM-based path filtering with Top-K selection, and (5) adversarial debate for CV...