Software Vulnerability

Software vulnerabilities, unintentional flaws in source code leading to security breaches, are a major focus of research aiming to improve automated detection and remediation. Current efforts utilize various machine learning models, including deep learning architectures like transformers, recurrent neural networks (RNNs), and graph neural networks (GNNs), often applied to intermediate representations like LLVM IR or code property graphs, to identify vulnerabilities and even suggest fixes. These advancements are crucial for enhancing software security, improving the efficiency of code review processes, and mitigating the significant financial and societal costs associated with software exploits. The effectiveness of these methods, however, is still being actively evaluated and refined, particularly concerning false positives and the ability to generalize across different programming languages and codebases.