Leveraging Graph of Thoughts and Large Language Models for Advanced Vulnerability Detection

Vulnerability Detection aims to automate the analysis of software systems to discover security flaws and defects, called vulnerabilities. In recent years, many studies have explored using LLMs in this task, leveraging their knowledge and reasoning skills acquired through training on large text and source code datasets. Despite the potential highlighted by these works, LLMs often struggle to correctly explain the root causes of vulnerabilities, raising questions about their effectiveness. This project aims to improve Large Language Models' classification and explainability capabilities by adopting a novel reasoning methodology from the literature known as the Graph of Thoughts. Although this methodology has shown promising results in logical and mathematical tasks, it has never been applied to vulnerability detection. Testing and evaluating this new vulnerability detection technique has demonstrated its potential to improve LLMs' classification and reasoning capabilities.