Cybersecurity AI (github.com)

Cybersecurity AI (CAI) is an open-source, lightweight framework released to let security professionals build and deploy AI-driven offensive and defensive automation. It uses an agent-based, modular architecture and integrates logging/tracing via Phoenix, built-in reconnaissance/exploitation/privilege-escalation tools, and multi-layered guardrails to mitigate prompt-injection and dangerous command execution. CAI supports 300+ models (OpenAI, Anthropic, DeepSeek, Ollama, LiteLLM-backed weights like Qwen and Claude, GPT-4o, etc.), is battle-tested in CTFs and bug bounties, and bills itself as “bug-bounty ready.” The project is research-oriented (CAI Fluency report) and designed to be lightweight and extensible; installation is as simple as pip install cai-framework for research use, with commercial licensing available for enterprise on-prem deployments. The significance is twofold: operationally, CAI democratizes powerful AI-assisted vulnerability discovery and red/blue automation—examples include PoCs that exposed critical flaws in Ecoforest heat pumps, ROS message-injection attacks on MiR robots, API enumeration at Mercado Libre, and unauthenticated MQTT broker manipulation in OT networks. Technically, the authors report systematic LLM evaluations showing gaps between vendor claims and real-world security performance and provide an empirically validated defense stack against prompt injection. That openness accelerates community benchmarking and defensive hardening but raises clear misuse risks; the team emphasizes ethical, lawful use and frames CAI as a tool to augment human researchers and scale automated security testing.