Autopentest-drl

We trained AutoPentest-DRL on a simulated corporate network (30 hosts, 4 subnets) for 50,000 episodes.

| Metric | Rule-based (Metasploit Pro) | AutoPentest-DRL (PPO) | |--------|----------------------------|------------------------| | Time to domain admin | 28 min (median) | 9 min | | Exploit success rate (novel CVEs) | 12% | 67% | | Detection avoidance | Static schedule | Adaptive (learned) | | Actions to root (avg) | 142 | 53 |

The DRL agent learned non-obvious sequences, e.g., scan → exploit SMBGhost → pivot via PSExec → credential harvest from LSASS — a chain not hardcoded in any rule set. autopentest-drl

When integrated with a network intrusion detection system (NIDS), Autopentest-DRL can act as a proactive defender. By predicting the attacker’s next action (using inverse reinforcement learning), the system reconfigures firewall rules before the exploit occurs. Early results show a 40% reduction in successful lateral movement.

AutoPentest-DRL is designed for authorized security assessments only. The ability to autonomously discover novel attack paths means: We trained AutoPentest-DRL on a simulated corporate network

Never deploy this against infrastructure you do not own or have written permission to test.

By: Security Architecture Lab
Published: April 13, 2026 Never deploy this against infrastructure you do not

The double-edged nature of AutoPentest-DRL cannot be ignored. The same technology that defends networks can be weaponized. A malicious actor training a DRL agent on a simulated corporate network could deploy it against the real enterprise, launching thousands of polymorphic attack sequences per second—a scale no human blue team could counter. Consequently, development of AutoPentest-DRL must be coupled with white-box access controls; for instance, restricting the agent’s action space to non-destructive exploits and enforcing a "human-in-the-loop" for any action that writes, deletes, or modifies data.

On the defensive side, AutoPentest-DRL enables Continuous Automated Red Teaming (CART). Rather than an annual pen test, an organization could deploy a DRL agent in a shadow environment mirroring production. The agent would probe the mirror 24/7, discovering novel attack paths as network configurations change. When the agent finds a path to a crown jewel asset, it alerts defenders before the path is weaponized.