Code Vulnerability

Code vulnerability research focuses on automatically identifying and mitigating security flaws in software source code, aiming to improve software security and reduce cyberattacks. Current research heavily utilizes large language models (LLMs) and other deep learning techniques, such as graph neural networks and bidirectional LSTMs, often integrated with static and dynamic analysis methods like fuzz testing and SAST tools, to detect vulnerabilities with varying degrees of accuracy and false positive rates. These advancements are significant because they offer the potential for more efficient and automated vulnerability detection and repair, ultimately enhancing software security and reducing the risk of exploitation. The field is actively exploring methods to improve model generalization, reduce false positives, and provide explainable AI for better understanding of model predictions.