Poster: A Case Study on Automated Vulnerability Repair Using Pre-Trained Language Models

Pre-trained language models (PLMs) have recently emerged as a powerful tool in Automated Vulnerability Repair (AVR), showing great potential in automating the generation of vulnerability patches. By leveraging their learned contextual understanding of code, these models are increasingly fine-tuned for complex tasks such as vulnerability detection and repair, making them valuable assets for improving software security. In this paper, we present a case study exploring the effectiveness of state-of-the-art (SOTA) AVR techniques that utilize PLMs for vulnerability repair. Our evaluation aims to determine whether these models genuinely understand security vulnerabilities and can generate appropriate fixes. Our findings highlight key challenges, including overfitting and unrealistic train/test data splits, which hinder the generalization of current approaches. These limitations underscore the need for more rigorous evaluation methodologies and improvements in model design to enhance the real-world applicability of AVR systems.