Towards Explainable Vulnerability Detection with Large Language Models

Understand

Software vulnerabilities pose significant risks to the security and integrity of software systems.

• Although prior studies have explored vulnerability detection using deep learning and pre-trained models, these approaches often fail to provide the detailed explanations necessary for developers to understand and remediate vulnerabilities effectively.
• The advent of large language models (LLMs) has introduced transformative potential due to their advanced generative capabilities and ability to comprehend complex contexts, offering new possibilities for addressing these challenges.
• In this paper, we propose LLMVulExp, an automated framework designed to specialize LLMs for the dual tasks of vulnerability detection and explanation.