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Argus, a novel multi-agent framework, reorchestrates static application security testing (SAST) by leveraging LLMs for vulnerability detection. It integrates comprehensive supply chain analysis, collaborative multi-agent workflows, and retrieval-augmented generation (RAG) to mitigate hallucinations and improve reasoning depth. Empirical results show Argus surpasses existing methods, detecting more true vulnerabilities, reducing false positives, and identifying zero-day vulnerabilities.
LLMs can move beyond simply assisting static analysis to orchestrating it, enabling the discovery of critical zero-day vulnerabilities.
Recent advancements in Large Language Models (LLMs) have sparked interest in their application to Static Application Security Testing (SAST), primarily due to their superior contextual reasoning capabilities compared to traditional symbolic or rule-based methods. However, existing LLM-based approaches typically attempt to replace human experts directly without integrating effectively with existing SAST tools. This lack of integration results in ineffectiveness, including high rates of false positives, hallucinations, limited reasoning depth, and excessive token usage, making them impractical for industrial deployment. To overcome these limitations, we present a paradigm shift that reorchestrates the SAST workflow from current LLM-assisted structure to a new LLM-centered workflow. We introduce Argus (Agentic and Retrieval-Augmented Guarding System), the first multi-agent framework designed specifically for vulnerability detection. Argus incorporates three key novelties: comprehensive supply chain analysis, collaborative multi-agent workflows, and the integration of state-of-the-art techniques such as Retrieval-Augmented Generation (RAG) and ReAct to minimize hallucinations and enhance reasoning. Extensive empirical evaluation demonstrates that Argus significantly outperforms existing methods by detecting a higher volume of true vulnerabilities while simultaneously reducing false positives and operational costs. Notably, Argus has identified several critical zero-day vulnerabilities with CVE assignments.
