Yawning Titan
Overview
An archived research environment for training and evaluating cyber-defence agents in simulated networks.
Yawning Titan represents a network as a graph and simulates attacker and defender actions. Researchers configure topology, game rules and rewards, then train or evaluate policies through repeatable episodes. Visualizations help inspect how the simulated network changes during an experiment.
The repository was archived in March 2025 and remains available as research code. It does not monitor or defend a real production network. Adoption requires a compatible Python environment and research expertise; teams using it should expect to maintain their own setup. The screenshot predates repository archival.
Product overview
Capabilities, pricing and fit
An archived research environment for training and evaluating cyber-defence agents in simulated networks.
Information as of October 16, 2026
What it can do
- Workflow
Yawning Titan represents a network as a graph and simulates attacker and defender actions. Researchers configure topology, game rules and rewards, then train or evaluate policies through repeatable episodes. Visualizations help inspect how the simulated network changes during an experiment.[1], [2]
Where it fits best
Sources (2)
- github.com — product detailsOfficial site · checked 2026-10-16
- dstl.github.io — product detailsOfficial site · checked 2026-10-16
Autonomy level
81/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of Yawning Titan:
- Comparing cyber-defence policies in simulation
- Studying reinforcement learning on network graphs
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Popularity level: 67%
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