mlsanityData to 14 Oct 2026

Reinforcement Learning for Automated Cybersecurity Penetration Testing

arXiv 2507.02969 ↗First posted 30 Jun 2025cs.CR cs.AIcited by 1

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

This paper aims to provide an innovative machine learning-based solution to automate security testing tasks for web applications, ensuring the correct functioning of all components while reducing project maintenance costs. Reinforcement Learning is proposed to select and prioritize tools and optimize the testing path. The presented approach utilizes a simulated webpage along with its network topology to train the agent. Additionally, the model leverages Geometric Deep Learning to create priors that reduce the search space and improve learning convergence. The validation and testing process was conducted on real-world vulnerable web pages commonly used by human hackers for learning. As a result of this study, a reinforcement learning algorithm was developed that maximizes the number of vulnerabilities found while minimizing the number of steps required

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 24 references that are in the map. First posted on arXiv 30 Jun 2025.

Builds on

9 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents18 Oct

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  5. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  6. 2019Autonomous Penetration Testing using Reinforcement Learningcited by 33
  7. 2018Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integrationcited by 17
  8. 2021Using Cyber Terrain in Reinforcement Learning for Penetration Testingcited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SubstratumGraphEnv: Reinforcement Learning Environment (RLE) for Modeling System Attack Pathscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
  2. 2024Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN
  3. 2024Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine
  4. 2025Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning
  5. 2021Automating Privilege Escalation with Deep Reinforcement Learning
  6. 2025Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation
  7. 2024Towards Type Agnostic Cyber Defense Agents
  8. 2024Exploring reinforcement learning for incident response in autonomous military vehicles