The tech behind Semgrep Assistant’s triage and remediation guidance

Semgrep Assistant utilizes AI and large language models (LLMs) to streamline the process of identifying and fixing code vulnerabilities by providing detailed remediation guidance and code suggestions, significantly reducing the time and effort required by developers. Through complex prompt chains and evaluation loops that incorporate project-specific data, including dependencies and previous fixes, the Assistant generates pull request comments with step-by-step instructions for addressing findings. The system employs a feedback loop mechanism, where self-evaluation chains assess the validity of autofixes, ensuring high-quality outputs. Additionally, it leverages a vector database for information retrieval, using insights from OWASP documentation and previous successful fixes to enhance rule generation and tailor results to specific projects. By integrating dependency information and dataflow traces, the Assistant offers targeted and contextually relevant guidance, improving the precision and reliability of vulnerability remediation.