Autopentest-drl Jun 2026

In essence, AutoPentest-DRL models the pentest process as a . The agent sees the current state of a target network (open ports, services, user privileges), takes an action (run a scanner, launch an exploit, attempt privilege escalation), and receives a reward (e.g., +100 for compromising a host, -1 for a failed exploit attempt). Over thousands of simulated episodes, it learns attack policies that maximize cumulative reward—i.e., the most efficient path to compromise a target.