LLM Agentic Workflow for Automated Vulnerability Detection and Remediation in Infrastructure-as-Code

Abstract

This paper presents a multi-agent, AI-driven strategy employing Large Language Models (LLMs), retrieval-augmented generation, and a continuously updated knowledge base for the detection and remediation of security vulnerabilities win cloud frameworks. By examining Infrastructure as Code (IaC) templates alongside pertinent best-practice snippets, the system discerns context-specific misconfigurations commonly overlooked by static tools, achieving a detection rate of 85% with some occurrences of false positives. Automated remediation guidance, anchored in current security standards, provides actionable solutions that seamlessly integrate into standard continuous integration/continuous development (CI/CD) workflows. Experimental results indicate the solution’s efficacy and scalability, heralding a proactive, context-aware approach to IaC security.

Keywords

• Infrastructure-as-code
• large language models
• vulnerability detection
• security automation
• CI/CD
• LLM workflows