Autopentest-drl -

The agent moves through the network, leveraging credentials or further vulnerabilities to reach the goal.

: It analyzes a network's topology (using description files) to determine the most efficient multi-stage attack path without actually launching any exploits. It often utilizes

Traditional automated penetration testing tools follow static, rule-based decision trees (e.g., Metasploit, OpenVAS). While efficient for known vulnerabilities, they fail to adapt to dynamic, multi-stage attack surfaces. This article introduces , a novel framework that models the penetration testing process as a Markov Decision Process (MDP) and optimizes attack paths using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO).

For cybersecurity students and researchers, it offers an excellent . For professional red teams, it highlights where automation can save time—namely in path analysis—while clearly showing the need for human oversight in actual attack execution. autopentest-drl

The core of the framework, which uses a Deep Q-Network (DQN) to navigate complex network topologies. It takes a matrix representation of an attack tree as input and outputs the most viable attack path. MulVAL Attack Graph Generator:

Known software vulnerabilities associated with those services.

The framework uses Nmap to scan a real target network, identifying its topology and active vulnerabilities. Attack Graph Generation (MulVAL): The agent moves through the network, leveraging credentials

. Developed by the Cyber Range Organization and Design (CROND) chair at the Japan Advanced Institute of Science and Technology (JAIST) , this tool shifts offensive security away from manual script execution toward goal-oriented, self-learning artificial intelligence. By modeling a computer network as an interactive environment, it trains a neural-network-backed agent to think like a human hacker, identifying the most efficient vector to compromise target systems. The Evolution of Offensive Security Automation

Early attempts at automation utilized classic Search and Graph Theory (such as depth-first search or attack trees). However, as networks grew, these methods suffered from the "state-space explosion" problem, becoming computationally impossible to calculate. AutoPentest-DRL solves this by integrating . The deep neural network acts as an advanced function approximator, allowing the automation framework to handle massive, high-dimensional network environments that would paralyze traditional algorithms. Core Architecture and Mechanics

: Integrates MulVAL (Multi-stage Vulnerability Analysis Language) to produce potential attack trees based on the discovered network topology. While efficient for known vulnerabilities, they fail to

The agent can choose from a library of actions at each time step. These mirror real-world attacker methodologies, spanning from initial reconnaissance to full exploit execution:

The framework consists of four core modules: