Leveraging Large Language Models for Security-Focused Code Reviews

This study investigates the potential application of Large Language Models (LLMs) in enhancing software security through automated vulnerability detection during the code review process.

The research examines the efficacy of LLMs in identifying security vulnerabilities that human reviewers, particularly those without extensive security backgrounds, might overlook. Through analysis of historically significant Common Vulnerabilities and Exposures (CVEs) in popular open-source projects, including frameworks such as Django and Log4j, this research evaluates the capability of LLMs to detect subtle security flaws within complex codebases. The methodology employs a phased approach to LLM prompting, progressing from general code analysis to targeted vulnerability identification while maintaining controlled conditions by isolating vulnerable code segments. By comparing LLM performance against traditional human code reviews and automated security scanning tools, this study provides crucial insights into the potential role of artificial intelligence in augmenting software security practices.

The findings suggest implications for the evolution of code review methodologies and the integration of AI-assisted security analysis within software development lifecycles.