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CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

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arxiv 2410.16324 v1 pith:VSGGNIOZ submitted 2024-10-18 cs.CR cs.AI

CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

Harry Emerson , Liz Bates , Chris Hicks , Vasilios Mavroudis This is my paper
classification cs.CR cs.AI
keywords cyborgagentscagedefenceenhancedenvironmentfasterintroduces
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing several software bugs from its predecessor, CybORG++ introduces MiniCAGE, a lightweight version of CAGE 2, which improves performance dramatically, up to 1000x faster execution in parallel iterations, without sacrificing accuracy or core functionality. CybORG++ serves as a robust platform for developing and evaluating defensive agents, making it a valuable resource for advancing enterprise network defence research.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies

    cs.NI 2026-06 unverdicted novelty 7.0

    COHORT automates mitigation generation for network attacks via collaborative LLMs on emulated topologies with offensive replay evaluation, reporting 46.7% success rate that is 4.4 times higher than a single-agent baseline.

  2. Dynamic Cyber Ranges

    cs.CR 2026-04 unverdicted novelty 7.0

    Dynamic Cyber Ranges with LLM defender agents reduce attacker success to 0-55% and preserve evaluation headroom as models advance by using comparable capabilities on both sides.

  3. Building Better Environments for Autonomous Cyber Defence

    cs.CR 2026-04 conditional novelty 5.0

    A workshop synthesis provides a decomposition framework for RL-cyber environment interfaces and best-practice guidelines for training and evaluating autonomous cyber defence agents.