Yawning Titan
An open-source, graph-based cyber-security simulation environment for training intelligent agents in autonomous cyber defense operations.
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for Yawning Titan, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.
Overview
Yawning Titan is an abstract, graph-based cyber-security simulation environment designed to facilitate the training of intelligent agents for autonomous cyber operations. Developed by the Defence Science and Technology Laboratory (Dstl), it focuses on enabling defensive autonomous agents to counter probabilistic red (attacker) agents within simulated network environments. Built on OpenAI's Gym framework, Yawning Titan supports a wide range of reinforcement learning algorithms and offers flexible environment configurations, making it a valuable tool for research and development in cyber defense strategies.
Key features
- cyber-security simulation
- reinforcement learning
- autonomous agents
- OpenAI Gym
- network defense
Use cases
- Training reinforcement learning agents for autonomous cyber defense.
- Simulating cyber-security scenarios to test defensive strategies.
- Developing and evaluating AI-driven responses to network intrusions.
- Researching agent generalization across varying network topologies.