# CybORG: A Gym for the Development of Autonomous Cyber Agents

arXiv: https://arxiv.org/abs/2108.09118 · HTML: https://mlsanity.com/paper/2108.09118/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2021.

## Built on by (most cited first, 30 of 60)

- [Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation](https://mlsanity.com/paper/2207.12355/) (2022)
- [Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies](https://mlsanity.com/paper/2211.15557/) (2022)
- [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)
- [Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games](https://mlsanity.com/paper/2403.17674/) (2024)
- [Intrusion Prevention through Optimal Stopping](https://mlsanity.com/paper/2111.00289/) (2021)
- [Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments](https://mlsanity.com/paper/2308.12086/) (2023)
- [Inroads into Autonomous Network Defence using Explained Reinforcement Learning](https://mlsanity.com/paper/2306.09318/) (2023)
- [Learning Near-Optimal Intrusion Responses Against Dynamic Attackers](https://mlsanity.com/paper/2301.06085/) (2023)
- [Autonomous Network Defence using Reinforcement Learning](https://mlsanity.com/paper/2409.18197/) (2024)
- [Large Language Models are Autonomous Cyber Defenders](https://mlsanity.com/paper/2505.04843/) (2025)
- [Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.05939/) (2023)
- [Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search](https://mlsanity.com/paper/2407.11070/) (2024)
- [CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents](https://mlsanity.com/paper/2410.16324/) (2024)
- [Entity-based Reinforcement Learning for Autonomous Cyber Defence](https://mlsanity.com/paper/2410.17647/) (2024)
- [Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination](https://mlsanity.com/paper/2508.05188/) (2025)
- [Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning](https://mlsanity.com/paper/2312.04940/) (2023)
- [A Multiagent CyberBattleSim for RL Cyber Operation Agents](https://mlsanity.com/paper/2304.11052/) (2023)
- [Structural Generalization in Autonomous Cyber Incident Response with Message-Passing Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2407.05775/) (2024)
- [Learning Security Strategies through Game Play and Optimal Stopping](https://mlsanity.com/paper/2205.14694/) (2022)
- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [CSLE: A Reinforcement Learning Platform for Autonomous Security Management](https://mlsanity.com/paper/2604.15590/) (2026)
- [Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception](https://mlsanity.com/paper/2111.11932/) (2021)
- [Designing Robust Cyber-Defense Agents with Evolving Behavior Trees](https://mlsanity.com/paper/2410.16383/) (2024)
- [SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.04299/) (2023)
- [Enabling A Network AI Gym for Autonomous Cyber Agents](https://mlsanity.com/paper/2304.01366/) (2023)
- [Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations](https://mlsanity.com/paper/2304.11084/) (2023)
- [The Path To Autonomous Cyber Defense](https://mlsanity.com/paper/2404.10788/) (2024)
- [Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN](https://mlsanity.com/paper/2407.15656/) (2024)
- [Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents](https://mlsanity.com/paper/2412.02875/) (2024)

## Abstract (arXiv)

Autonomous Cyber Operations (ACO) involves the development of blue team (defender) and red team (attacker) decision-making agents in adversarial scenarios. To support the application of machine learning algorithms to solve this problem, and to encourage researchers in this field to attend to problems in the ACO setting, we introduce CybORG, a work-in-progress gym for ACO research. CybORG features a simulation and emulation environment with a common interface to facilitate the rapid training of autonomous agents that can then be tested on real-world systems. Initial testing demonstrates the feasibility of this approach.