arXiv AI
The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphs
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arXiv AI
Sep 16
A Cyber Range Evaluation of Autonomous Network Incident Response Agents
The study evaluates autonomous agents that respond to network intrusions within a cyber range designed for human operator training. Using an emulated network with variable topology, red‑team attacks, and simulated users, the agents aim to block unauthorized access while minimizing defensive costs. Experiments compare heuristic policies with reinforcement‑learning‑derived policies, finding that the latter generally defend more efficiently, though performance varies with adversary strategy and user simulation.
arXiv AI
Jun 12
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
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.
arXiv AI
Jun 17
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
arXiv:2606. 18223v1 Announce Type: cross Abstract: With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL).
Hugging Face Trending Papers
Aug 5
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
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. Meanwhile, recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have improved LLM reasoning, but their integration into cybersecurity remains elusive due to the absence of suitable benchmark environments and interaction datasets.
arXiv AI
2d ago
Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
arXiv:2610.00590v1 Announce Type: cross Abstract: An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability...