Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

• URL: http://arxiv.org/abs/2311.16169v2
• Date: Sun, 9 Jun 2024 18:12:48 GMT
• Title: Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
• Authors: Avishree Khare, Saikat Dutta, Ziyang Li, Alaia Solko-Breslin, Rajeev Alur, Mayur Naik,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable performance on code-related tasks. We evaluate whether pre-trained LLMs can detect security vulnerabilities and address the limitations of existing tools.
• Score: 12.82645410161464
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Security vulnerabilities in modern software are prevalent and harmful. While automated vulnerability detection tools have made promising progress, their scalability and applicability remain challenging. Recently, Large Language Models (LLMs), such as GPT-4 and CodeLlama, have demonstrated remarkable performance on code-related tasks. However, it is unknown whether such LLMs can do complex reasoning over code. In this work, we explore whether pre-trained LLMs can detect security vulnerabilities and address the limitations of existing tools. We evaluate the effectiveness of pre-trained LLMs, in terms of performance, explainability, and robustness, on a set of five diverse security benchmarks spanning two languages, Java and C/C++, and covering both synthetic and real-world projects. Overall, all LLMs show modest effectiveness in end-to-end reasoning about vulnerabilities, obtaining an average of 60% accuracy across all datasets. However, we observe that LLMs show promising abilities at performing parts of the analysis correctly, such as identifying vulnerability-related specifications (e.g., sources and sinks) and leveraging natural language information to understand code behavior (e.g., to check if code is sanitized). Further, LLMs are relatively much better at detecting simpler vulnerabilities that typically only need local reasoning (e.g., Integer Overflows and NULL pointer dereference). We find that advanced prompting strategies that involve step-by-step analysis significantly improve performance of LLMs on real-world datasets (improving F1 score by up to 0.25 on average). Finally, we share our insights and recommendations for future work on leveraging LLMs for vulnerability detection.

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