Training an Autonomous Pentester with Deep RL
summary
A prototype autonomous pentester uses deep reinforcement learning to pilot Metasploit via a custom environment called Metasploit Gym and trains a DQN agent against simulated networks modeled as partially observable Markov decision processes. Training on Metasploitable2 yields average reward 35 versus 11 for random, achieves root in 100% of episodes versus ~20% for random, and the real VM setup collects 256 time steps every 7 minutes so simulation is required for scale. Works as proof of concept but needs more exploits, diverse topologies, and cautious reward alignment.
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chapters (19)
- 0:00Start
- 1:02What Pen Testing Is
- 2:01What Pen Testing Is
- 3:39Remote Code Execution
- 8:55Reward Signal
- 10:26Detractors
- 11:05Reward Hacking
- 12:07Pen Testing
- 13:50Metasploit
- 14:07Handle Scanning
- 14:29Privilege Escalations
- 16:32The State Space
- 21:09Reward
- 22:14Metasploitable
- 22:52Training Speed
- 25:02Conditional Transition Probabilities
- 26:51Demo
- 29:14Port Scan
- 32:43Takeaways
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