# Autonomous Penetration Testing using Reinforcement Learning

arXiv: https://arxiv.org/abs/1905.05965 · HTML: https://mlsanity.com/paper/1905.05965/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 30 of 33)

- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios](https://mlsanity.com/paper/2305.17246/) (2023)
- [Deep hierarchical reinforcement agents for automated penetration testing](https://mlsanity.com/paper/2109.06449/) (2021)
- [Reward Shaping for Happier Autonomous Cyber Security Agents](https://mlsanity.com/paper/2310.13565/) (2023)
- [Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments](https://mlsanity.com/paper/2308.12086/) (2023)
- [Using Cyber Terrain in Reinforcement Learning for Penetration Testing](https://mlsanity.com/paper/2108.07124/) (2021)
- [Crown Jewels Analysis using Reinforcement Learning with Attack Graphs](https://mlsanity.com/paper/2108.09358/) (2021)
- [Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs](https://mlsanity.com/paper/2201.12416/) (2022)
- [A Survey of Large Language Models in Cybersecurity](https://mlsanity.com/paper/2402.16968/) (2024)
- [Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2202.10630/) (2022)
- [Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey](https://mlsanity.com/paper/2310.07745/) (2023)
- [PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design](https://mlsanity.com/paper/2512.14233/) (2025)
- [Automated Penetration Testing with LLM Agents and Classical Planning](https://mlsanity.com/paper/2512.11143/) (2025)
- [Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception](https://mlsanity.com/paper/2111.11932/) (2021)
- [Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space](https://mlsanity.com/paper/2407.15168/) (2024)
- [Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain](https://mlsanity.com/paper/2211.03027/) (2022)
- [Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine](https://mlsanity.com/paper/2405.15908/) (2024)
- [The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks](https://mlsanity.com/paper/2410.15396/) (2024)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents](https://mlsanity.com/paper/2304.01244/) (2023)
- [Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization](https://mlsanity.com/paper/2407.05511/) (2024)
- [A Unified Modeling Framework for Automated Penetration Testing](https://mlsanity.com/paper/2502.11588/) (2025)
- [Reinforcement Learning for Automated Cybersecurity Penetration Testing](https://mlsanity.com/paper/2507.02969/) (2025)
- [Diverse Planning with Simulators via Linear Temporal Logic](https://mlsanity.com/paper/2510.17418/) (2025)
- [AEGIS: White-Box Attack Path Generation using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises](https://mlsanity.com/paper/2601.22720/) (2026)
- [GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2204.01975/) (2022)
- [Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks](https://mlsanity.com/paper/2205.13763/) (2022)
- [On the importance of data collection for training general goal-reaching policies](https://mlsanity.com/paper/2211.03521/) (2022)

## Abstract (arXiv)

Penetration testing (pentesting) involves performing a controlled attack on a computer system in order to assess it's security. Although an effective method for testing security, pentesting requires highly skilled practitioners and currently there is a growing shortage of skilled cyber security professionals. One avenue for alleviating this problem is automate the pentesting process using artificial intelligence techniques. Current approaches to automated pentesting have relied on model-based planning, however the cyber security landscape is rapidly changing making maintaining up-to-date models of exploits a challenge. This project investigated the application of model-free Reinforcement Learning (RL) to automated pentesting. Model-free RL has the key advantage over model-based planning of not requiring a model of the environment, instead learning the best policy through interaction with the environment. We first designed and built a fast, low compute simulator for training and testing autonomous pentesting agents. We did this by framing pentesting as a Markov Decision Process with the known configuration of the network as states, the available scans and exploits as actions, the reward determined by the value of machines on the network. We then used this simulator to investigate the application of model-free RL to pentesting. We tested the standard Q-learning algorithm using both tabular and neural network based implementations. We found that within the simulated environment both tabular and neural network implementations were able to find optimal attack paths for a range of different network topologies and sizes without having a model of action behaviour. However, the implemented algorithms were only practical for smaller networks and numbers of actions. Further work is needed in developing scalable RL algorithms and testing these algorithms in larger and higher fidelity environments.