Security in a Workflow: Exploring Role-Based Agentic Architectures for Vulnerability Handling

2026-06-12

AI Summary

This work addresses the limitations of existing large language models, which are typically confined to isolated tasks and struggle to integrate into industrial-scale, multi-stage security workflows. To bridge this gap, the authors propose the first role-based multi-agent framework tailored to the entire vulnerability lifecycle, incorporating specialized agents—Planner, Analyzer, Fixer, and Verifier—augmented with CodeQL static analysis for enhanced precision. By introducing a role-oriented multi-agent architecture into end-to-end vulnerability management, this approach effectively aligns the capabilities of large models with real-world security engineering demands. Evaluated on 25 real-world C/C++ vulnerabilities, the system achieves a detection accuracy of 44%—comparable to GPT-5.5—and a repair accuracy of 19%, offering a practical and collaborative paradigm for intelligent security operations.

Abstract

Secure software engineering in practice is a multi-stage workflow involving vulnerability analysis, remediation, and fix verification. However, current LLM-based software security approaches often focus on isolated tasks such as detection or patch generation, with limited attention to agentic architectures reflecting industrial workflow. This creates a gap between existing LLM-based vulnerability-handling methods and real-world practices. In this paper, we study a role-based agentic workflow for vulnerability analysis and mitigation consisting of Planner, Analyzer, Fixer, and Verifier roles. To explore the effect of static analysis tool, the analyzer agent was integrated with the CodeQL in one of the workflows. The models used include nemotron-cascade-2:30b, qwen3-coder-next, and gpt-oss:120b. Our evaluation uses 25 real-world C/C++ vulnerabilities. The study reports 44% vulnerability detection accuracy comparable to GPT 5.5 and 19% fix accuracy. We also list implications from this study in context of software security practitioners.

Problem

• workflow
• vulnerability handling
• role-based agentic architecture
• secure software engineering
• LLM-based security

Innovation

• role-based agentic architecture
• vulnerability handling workflow
• CodeQL integration
• LLM-based software security
• multi-agent collaboration