Original Post: The tech behind Semgrep Assistant’s triage and remediation guidance
Assistant enhances vulnerability remediation by providing detailed, step-by-step instructions and suggested code fixes, reducing hours of work to merely 10 minutes of validation. Utilizing AI and Large Language Models (LLMs), Assistant processes complex prompt chains and feedback loops, incorporating inputs such as project-specific data and results from Semgrep’s static analysis engine.
Feedback loops check the validity of auto-generated fixes, evaluating attributes like issue presence and code malformation. Assistant employs a self-evaluation process, facilitated by error handling in Python, to improve model outputs. Retrieval Augmented Generation (RAG) compiles various insights, such as OWASP mitigations, project dependencies, and previous fixes, to tailor solutions.
Key features include:
– Vector Database: Uses static information and thresholds for relevance.
– Dependency-Based Remediation: Suggests functions or methods for remediation based on project dependencies.
– Fixing via Previous Commits: Utilizes past commits to inform new fixes, enhancing confidence and accuracy.
– Dataflow Traces: Provides detailed context to improve the specificity and relevance of the guidance.
In essence, Assistant leverages AI-driven processes and feedback mechanisms to streamline and enhance cybersecurity remediation efforts.
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