arXiv Machine Learning June 26, 2026 By Raphael Simon, Jos\'e Carrasquel, Wim Mees, Pieter Libin

NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing

Read the original on arXiv Machine Learning →

arXiv:2603. 19864v2 Announce Type: replace Abstract: Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inherently partially observable and features large action spaces.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 26

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

arXiv:2509. 20008v2 Announce Type: replace Abstract: Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation.

By Raphael Simon, Pieter Libin, Wim Mees

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 15

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

arXiv:2604. 09523v2 Announce Type: replace Abstract: Training reinforcement-learning agents for cyber defense requires an environment that reflects the operational setting: noisy, partial observations, several defenders coordinating across a network, and an adaptive adversary realized through self-play.

By Igor Jankowski

rag agents reinforcement-learning

arXiv AI

Aug 6

Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)

arXiv:2608. 04317v1 Announce Type: cross Abstract: Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, heuristic red agents, leaving their robustness against adaptive threats critically understudied.

By Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi, Sanggeon Yun, Hyunwoo Oh, SungHeon Jeong, Nathaniel D. Bastian, Mahdi Imani, Mohsen Imani

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 24

PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses

arXiv:2603. 13026v2 Announce Type: replace Abstract: Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents.

By Chenlong Yin, Runpeng Geng, Yanting Wang, Jinyuan Jia

llms agents reinforcement-learning efficiency benchmarks safety

arXiv AI

1d ago

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

arXiv:2608. 15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands.

By Yiran Gao, Juntao Chen, Tao Li

llms agents fine-tuning

arXiv Machine Learning

6d ago

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.

By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang

llms safety

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Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)

PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

Grounding Large Language Models as Generalizable Policies in Network Control