Detecting Code Vulnerabilities using LLMs

Abstract

Large language models (LLMs) have emerged as a promising tool for detecting code vulnerabilities, potentially offering advantages over traditional rule-based methods. This paper proposes an enhanced framework for vulnerability detection using LLMs, incorporating various prompt engineering strategies to improve performance. We evaluate several techniques, including role-based prompting, zero-shot chain-of-Thought, and structured prompting approaches, on the DiverseVul dataset of C/C++ vulnerabilities. Our experiments assess the framework's performance across different code structures, contextual information levels, and LLM capabilities. Our results show that using our dynamic prompt engineeri..