Adversarial Training for Robustness Enhancement in LLM-Based Code Vulnerability Detection

This paper proposes a new approach to code vulnerability detection that uses large language models (LLMs) and incorporates adversarial training techniques. It aims to enhance the robustness of the vulnerability detection model and improve the performance of vulnerability detection. The effect of adversarial training on model performance enhancement is explored through experimental validation. It is proved experimentally that the method proposed we propose can significantly improve the robustness and accuracy and of the vulnerability detection model. It provides effective technical support for security problems in software development.