LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection

TL;DR

The paper tackles vulnerability detection with Large Language Models (LLMs), identifying challenges such as hallucinations and limited context. It proposes LLMpatronous, a robust AI-driven approach that combines Retrieval-Augmented Generation (RAG) for grounding knowledge and a Mixture-of-Agents (MoA) architecture for collaborative verification. Through experiments on the Vuldroid Android dataset, it shows that basic single-LLM prompting struggles with accuracy and false positives, while the RAG+MoA framework significantly mitigates hallucinations and improves reliability, even leveraging open-source LLMs. The work demonstrates a practical path toward dependable AI-powered vulnerability analysis that can integrate into secure software development workflows.

Abstract

Despite the transformative impact of Artificial Intelligence (AI) across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and superficial code comprehension. While generative AI offers promising automation capabilities for software development, leveraging Large Language Models (LLMs) for vulnerability detection presents unique challenges. This paper explores the potential and limitations of LLMs in identifying vulnerabilities, acknowledging inherent weaknesses such as hallucinations, limited context length, and knowledge cut-offs. Previous attempts employing machine learning models for vulnerability detection have proven ineffective due to limited real-world applicability, feature engineering challenges, lack of contextual understanding, and the complexities of training models to keep pace with the evolving threat landscape. Therefore, we propose a robust AI-driven approach focused on mitigating these limitations and ensuring the quality and reliability of LLM based vulnerability detection. Through innovative methodologies combining Retrieval-Augmented Generation (RAG) and Mixtureof-Agents (MoA), this research seeks to leverage the strengths of LLMs while addressing their weaknesses, ultimately paving the way for dependable and efficient AI-powered solutions in securing the ever-evolving software landscape.