Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO) algorithms are commonly deployed to learn a policy that maximizes cumulative reward over an episode (e.g., a timed penetration test). The "deep" aspect allows the agent to abstract high-level strategies from raw network data, such as recognizing that discovering a web server often precedes SQL injection attempts.
Developed by the at the Japan Advanced Institute of Science and Technology (JAIST), this tool represents a shift from static security scripts to dynamic, AI-driven offensive security. What is AutoPentest-DRL? autopentest-drl
: A 2026 survey that lists AutoPentest-DRL (referred to as "AutoPen") as a major tool in the field of automated penetration testing and network intrusion. Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO)
Additionally, will be critical. Future agents will be pre-trained on millions of synthetic network topologies (using graph neural networks to encode network structure), then fine-tuned on a specific enterprise network in less than 100 episodes. This would solve the sample efficiency bottleneck. What is AutoPentest-DRL
Developed at the Japan Advanced Institute of Science and Technology (JAIST) , this tool is primarily designed for . It helps students and researchers understand how attackers move laterally through a network by comparing the AI's output path with the generated attack graphs . README.md - crond-jaist/AutoPentest-DRL - GitHub
Artificial Intelligence for Cybersecurity Education and Training : This paper introduces the AutoPentest-DRL