Dataset 01 · Cybersecurity
Cybersecurity RL tasks for LLM training
~3,000 tasksDockerfile / Compose + prompt + graderReinforcement learning
A handcrafted post-training dataset for cybersecurity. Each task is a live, isolated environment with an automatic grader — the policy receives a prompt, acts against a real service, and is scored with a binary, verifiable reward. Built to drop straight into reinforcement-learning loops (GRPO, PPO, RLOO) with no human preference model in the inner loop.
- Scale
- ~3,000 tasks, still growing
- Unit format
- Dockerfile / Compose + prompt + grader
- Domains
- Internal-network · blockchain · web
- Design bar
- Novel bugs, not public 1-days
- Frontier result
- Claude Opus & GLM 5.3 → pass@3 = 0
- Reward
- Automatic, binary, environment-checked
- Integration
- Attachable to an RL run (GRPO / PPO / RLOO)
task/
├─ prompt.md objective + topology, given to the policy
├─ docker-compose.yml isolated live environment
├─ grader/ deterministic check → reward.txt ∈ {0,1}
├─ services/ agent workspace + target services
└─ solve/ reference solution (held out from the agent)