Untrained agents might execute destructive exploits (e.g., EternalBlue on a production SQL server).
AutoPentest-DRL demonstrates that deep reinforcement learning can outperform static pentest automation in time-to-compromise and adaptability. While not ready for fully unattended red-team operations, it serves as a powerful augmentation for human pentesters — suggesting high-value attack paths that rigid scanners would miss. autopentest-drl
Baselines:
: This is the simplest mode, intended for educational purposes. It determines the optimal attack path for a simulated network topology without performing actual exploits, allowing users to study attack mechanisms safely. Untrained agents might execute destructive exploits (e