Autopentest-drl May 2026

AutoPentest-DRL is an automated penetration testing framework that uses Deep Reinforcement Learning (DRL) to plan and execute attack paths on computer networks. It was developed by the Cyber Range Organization and Design (CROND) Japan Advanced Institute of Science and Technology (JAIST) Framework Overview

Artificial Intelligence for Cybersecurity Education and Training : This paper introduces the AutoPentest-DRL autopentest-drl

Training Mode

: Users can retrain the DRL agent on custom network topologies to improve its adaptability and efficiency in specific environments. Why Use DRL for Pentesting? 2022)] OWASP AutoPentest DRL Project

Untrained agents might execute destructive exploits (e.g., EternalBlue on a production SQL server). EternalBlue on a production SQL server).

• CybORG GitHub Repository
• [Paper: “Deep Reinforcement Learning for Autonomous Cyber Operations” (Nguyen et al., 2022)]
• OWASP AutoPentest DRL Project

Cyber Range Training

: Enhancing Capture-the-Flag (CTF) exercises by providing an automated, "smart" adversary that students can defend against.

6.1 Advantages Over Traditional Methods

at the Japan Advanced Institute of Science and Technology (JAIST), it is primarily designed as an educational tool to help users study the mechanisms of cyber attacks in a controlled environment. Core Functionality

) by actively exploring how vulnerabilities can be chained together to compromise a system. iSchool | Syracuse University source code