Autopentest-drl

Autopentest-DRL bridges the gap between "dumb fast scanners" and "slow brilliant humans." In recent benchmarks (e.g., CyBERTed, 2023 MAS framework), DRL agents achieved a 94% success rate on vulnerable Docker environments (like VulnHub’s “HackTheBox” sims) compared to 62% for static rule-based bots.

While powerful, the use of autonomous offensive AI brings significant hurdles. autopentest-drl

AutoPentest-DRL is an automated penetration testing framework that uses Deep Reinforcement Learning (DRL) Autopentest-DRL bridges the gap between "dumb fast scanners"

| Action | Reward | |--------|--------| | New service discovered | +0.1 | | New low-priv shell | +1.0 | | Privilege escalation to root | +10.0 | | Compromise domain controller | +100.0 | | Detection / Honeypot triggered | -5.0 | | Crash a critical service | -20.0 | 2023 MAS framework)