Vulnerability Detection

Vulnerability detection in software aims to automatically identify security flaws in code, improving software security and reducing the risk of exploitation. Current research heavily utilizes machine learning, particularly deep learning models like transformers and graph neural networks, often leveraging large language models (LLMs) and exploring both supervised and anomaly-based detection approaches. Challenges remain in addressing issues like data quality, overfitting, and the need for robust, explainable models that generalize well across different codebases and programming languages. Improved vulnerability detection methods have significant implications for enhancing software security and reducing the impact of cyberattacks.