Autopentest-drl

Once the action is executed, the environment changes. If the action succeeds (e.g., a root shell is gained), the agent receives a high reward. If it fails or gets blocked by an Intrusion Detection System (IDS), it receives a penalty. The framework uses this feedback to update its neural network weights, ensuring it becomes smarter with every execution. Key Advantages of Autopentest-DRL Over Traditional Methods Traditional Manual Pen Testing Legacy Automated Scanners Autopentest-DRL Annual or bi-annual basis Scheduled/Continuous Continuous & Real-time Contextual Awareness High (Human intelligence) Low (Static vulnerability list) High (Dynamic adaptability) Lateral Movement Yes (Manual pivoting) No (Scans single hosts statically) Yes (Autonomous multi-step pivoting) Scalability Poor (Requires more humans) High (Software-based) High (Scales dynamically with AI) False Positive Rate Low (Validates flaws via exploitation) Context-Aware Lateral Movement

It helps in designing against evolving threats. If you'd like, I can provide:

: Raw scan data feeds into MulVAL (Multi-host, Multi-stage Vulnerability Analysis), an open-source logic-based security analyzer. MulVAL synthesizes vulnerability data and topology rules to produce a comprehensive attack tree.

: A Python-based RPC API that allows the framework to communicate with and control Metasploit. Deep Reinforcement Learning Engine : Typically utilizes Deep Q-Networks (DQN)

Used to execute the planned penetration attacks on a real network. Operational Modes According to the official documentation , the tool offers two main modes of operation: Logical Attack Mode: autopentest-drl

@pytest.fixture def env(): return gym.make('CartPole-v1')

Used to determine potential attack trees for the logical target network. Scanning and Execution Tools:

In the context of Autopentest-DRL, these components translate directly to network hacking: The Autopentest-DRL hacking engine.

Operates via rigorous math, mathematical optimization, and raw trial-and-error. It excels at discovering completely novel, highly complex sequential attack paths that humans might miss. However, it requires intensive training environments and cannot naturally parse text-heavy data. Once the action is executed, the environment changes

is an automated testing framework that integrates deep reinforcement learning (DRL) to generate, prioritize, and execute test cases for software systems. It aims to improve test coverage, find complex bugs, and optimize testing efficiency by learning testing strategies from interactions with the application under test (AUT).

The entire plan relies on MulVAL to generate the attack tree. MulVAL is ; it knows potential vulnerabilities but struggles to handle the dynamic nature of a live network.

| Scenario | Hosts | Vulnerabilities | Goal | |----------|-------|----------------|------| | Simple | 3 | EternalBlue, weak SSH creds | Compromise host 3 | | Medium | 7 | 15 (mix of web, SMB, SQLi) | Root access on database server | | Complex | 12 | 28 (including pivoting) | Domain controller compromise |

The framework utilizes a for agent training. The framework uses this feedback to update its

: When referencing, use: AutoPentest-DRL: Continuous Red-Teaming via Deep Reinforcement Learning. Security Arch. Lab, 2026.

: The agent views the network as a "local view," seeing only what a real-world attacker would discover through scanning at each step. 2. The Decision Engine

The framework provides a base for research into autonomous systems, such as developing that can handle uncertainty and dynamically reconfigure attacks in real time.

AutoPentest-DRL is primarily developed on but can work on similar distributions. The setup is technical and requires installing several dependencies:

The increasing complexity of modern network infrastructures renders traditional manual penetration testing labor-intensive, error-prone, and non-scalable. This paper proposes , a novel framework that leverages Deep Reinforcement Learning (DRL) to automate the process of network penetration testing. By modeling the attacker’s actions, network states, and reward mechanisms as a Markov Decision Process (MDP), our framework enables an autonomous agent to learn optimal attack paths, prioritize high-value targets, and adapt to dynamic network environments. Experimental results on virtualized network topologies demonstrate that AutoPenTest-DRL achieves higher coverage of vulnerabilities (up to 92%) and reduces testing time by 67% compared to rule-based automated scanners like OpenVAS and Metasploit’s autopwn. This work highlights DRL’s potential to revolutionize cybersecurity assessments through intelligent, goal-driven decision-making.