Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

Researchers have made breakthroughs in applying reinforcement learning to penetration testing, a complex sequential decision-making task with partial observability and large action spaces.

Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, is well-suited for reinforcement learning (RL) automation, according to a study published on arXiv^[1]. The task is inherently partially observable, presenting a major challenge for RL applications. To address this, researchers investigated various Proximal Policy Optimization (PPO) variants designed to mitigate partial observability. They found that history aggregation greatly benefits the task, converging up to four times faster than other approaches^[1]. The study used vanilla PPO as a baseline and compared it with variants that incorporate techniques such as frame-stacking, historical information augmentation, and LSTM or TrXL architectures. Another study on arXiv^[1] introduced NASimJax, a tool that enables experimentation on larger networks under fixed compute budgets, previously infeasible. The researchers also found that training on sparser topologies yields an implicit curriculum that improves out-of-distribution generalization, even on topologies denser than those seen during training^[1].