Agentic Generative Red Teaming: Automated Attack Chain Orchestration via LangGraph

Traditional red teaming and vulnerability assessment methodologies often struggle to keep pace with the dynamic and rapidly evolving nature of modern cyber threats. This paper proposes a novel framework for Agentic Generative Red Teaming, which utilizes LangGraph as the core orchestration engine to automate complex, multi-stage attack chains. Unlike traditional static scripts or linear chains, the proposed system employs a stateful, directed cyclic graph (DCG) architecture. This allows a multi-agent swarm comprising specialized LLM-powered agents for reconnaissance, payload generation, and lateral movement to maintain a persistent state and adaptively pivot based on real-time environment feedback. By modeling the penetration testing lifecycle through LangGraph’s state-management capabilities, the framework ensures rigorous style consistency and autonomous decision-making. Our implementation demonstrates that this agentic approach significantly enhances the efficiency of identifying critical vulnerabilities by simulating sophisticated, non-linear adversary behaviors in real-time.