Enhancing Security Resilience in DevSecOps: A Multi-agent Framework for Vulnerability Remediation and Attack Scenario Analysis

Abstract

In the contemporary landscape of cloud-native and high-velocity software engineering, integrating robust security checkpoints within Continuous Integration and Continuous Deployment (CI/CD) pipelines collectively known as DevSecOps faces severe operational bottlenecks. Traditional Static Application Security Testing (SAST) utilities exhibit exorbitant false-positive rates and focus predominantly on vulnerability identification rather than autonomous remediation. This diagnostic limitation culminates in severe security technical debt and prolonged Mean Time to Repair (MTTR). To overcome this challenge, this paper proposes an advanced multi-agent Large Language Model (LLM) framework engineered to automate the end-to-end lifecycle of vulnerability identification, localized software patching, and empirical validation.

The core methodology leverages the compact yet structurally capable Qwen2.5-Coder-1.5B model, structurally optimized via Low-Rank Adaptation (LoRA) on a curated Common Weakness Enumeration (CWE) dataset derived from the CIRCL repository. Accelerated by Groq’s high-throughput Language Processing Unit (LPU) infrastructure, the inference engine is tightly coupled with Semgrep SAST tools to establish a continuous, multi-agent self-healing feedback loop comprising Remediation, Verification, and Analysis agents. Experimental evaluations conducted on a highly specialized verification suite of real-world vulnerabilities demonstrate substantial quantitative enhancements. The fine-tuned multi-agent system significantly outperformed the zero-shot baseline model, improving the token-level character-based F-score (chrF metric) from 0.3012 to 0.3275 and drastically elevating the generation of syntactically compilable, secure code blocks.

Most notably, this autonomous orchestration reduced the operational MTTR for High and Critical vulnerabilities from a traditional manual baseline of 48 hours to under 35 minutes, successfully eliminating the regressions typically caused by standalone LLMs. Beyond automated source-code repair, the framework synthesizes predictive attack scenario simulations to facilitate proactive infrastructure hardening (e.g., WAF/IPS rule generation), offering a highly resilient and empirically validated blueprint for securing mission-critical DevSecOps pipelines.