Autopentest-drl ((full))

[Reconnaissance] → [Attack Planner (DRL Agent)] → [Exploit Executor] → [State Tracker] ↑ | └─────────────────── Reward Signal ────────────────────────┘

: Simulates attacks on hypothetical network topologies to study theoretical vulnerabilities without touching actual hardware .

The AI entity tasked with navigating the network. autopentest-drl

[ Manual Pen Testing ] ──> [ Automated Scanners ] ──> [ Autopentest-DRL ] - Highly skilled - Fast & repeatable - Intelligent & adaptive - Slow & unscalable - Static & noisy - Discovers complex paths - Human dependent - Misses logical flaws - Continuous learning

): The agent's current knowledge of the network. This includes discovered IP addresses, open ports, identified operating systems, and active user privileges. The Action Space ( of this framework or explore how it compares

: By learning from past "games" (simulated pentests), it avoids noisy or ineffective techniques that would get a human hacker caught. The Big Picture: Offensive AI

Evaluating the overall security posture of corporate IT networks. This includes discovered IP addresses

of this framework or explore how it compares to other AI-driven pentesting tools like PentestGPT