Adversarial Security Agents
Automates red team vs. blue team AI competitions to discover and patch vulnerabilities in open-source ecosystems using LLM-driven agents.
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This project implements a Kubernetes-native framework for autonomous adversarial security testing. It combines LLM-driven decision-making with offensive security tooling and retrieval-augmented generation (RAG) knowledge bases, enabling a Red Team agent to autonomously discover vulnerabilities. The long-term vision is to establish dual-LLM adversarial competitions, where Red Team and Blue Team agents compete to secure the open-source ecosystem by proactively finding and patching vulnerabilities before human attackers.
Características Principales
- Adaptive OODA loop for attack strategy formulation and execution
- BlackArch toolkit integration providing 2000+ offensive security tools
- RAG knowledge base with offensive security documentation (GTFOBins, Atomic Red Team, HackTricks)
- Autonomous LLM-guided decision making for security testing
- 0 GitHub stars
- Kubernetes-native declarative network and resource isolation for agents
Casos de Uso
- Automated vulnerability discovery in newly published open-source packages
- Research into autonomous adversarial security testing methodologies
- Identification of genuine zero-day vulnerabilities before public exploitation
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