Procedurally varied
Every run stands up a new enterprise: different topology, credentials, and blind spots. Nothing to memorize, nothing to overfit — the environment is the curriculum.
Train and validate LLMs and AI agents on offensive and defensive security tasks — inside high-fidelity, procedurally-varied enterprise environments built on real cloud infrastructure, real endpoints, and real security tooling.
Every run stands up a new enterprise: different topology, credentials, and blind spots. Nothing to memorize, nothing to overfit — the environment is the curriculum.
Attack chains from initial foothold to exfiltration, and the defensive side of the same coin: detection, triage, and response. Train and test both sides of the range.
Every boundary is enforced for real, and every outcome is scored against the environment's ground truth — never the agent's self-report.
Turn scenarios into benchmarks: objectives, scoring rules, and partial credit for each step of a task — from a single skill to a multi-stage operation.
Run any LLM or agent through the same benchmark and compare them side by side — capability, reliability, and cost on the tasks that actually matter.
Environments reset to a clean state between runs, so results are reproducible and comparable across models, versions, and time.
Evaluate model security capabilities in realistic enterprise scenarios, and generate the post-training data that improves them.
Training and evaluation infrastructure for agentic products — AI SOC, pentesting, cloud security — before they meet a customer's environment.
Independently validate internal agents and vendor security claims against your own kind of environment — not a vendor's demo.
Tell us what you're training, testing, or trying to trust — we'll set up a range for it.
Notes from building security environments and evaluating agents inside them.
Every model is rated against publicly available security benchmarks — so the right model can be chosen for every task.
These rankings are computed on public, verified-open benchmarks. Benchmark data belongs to its original authors — the sources and licenses below are our attribution obligations, not decoration. Please honor each dataset's license.