An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors

원문 초록

Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security analysis tools struggle with poor generalization, high false positive rates, and coarse detection granularity. Large Language Models (LLMs) have been considered promising candidates for addressing those challenges. In this study, we conducted an empirical study to explore the potential of LLMs in detecting security defects during code review. Specifically, we evaluated the performance of six LLMs under five different prompts and compared them with state-of-the-art static analysis tools. We also performed linguistic and regression analyses for the best-performing LLM to identify quality problems in its responses and factors influencing its performance. Our findings showthat: (1) existing pre-trained LLMs have limited capability in security code review but significantly outperformthe state-of-the-art static analysis tools. (2) GPT-4 performs best among all LLMs when provided with a CWE list for reference. (3) GPT-4 frequently generates verbose or non-compliant responses with the task requirements given in the prompts. (4) GPT-4 is more adept at identifying security defects in code files with fewer tokens, containing functional logic, or written by developers with less involvement in the project.