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Sprint projectSep 14, 2026Saarbrücken, Germany

Adaptive AI-Based Containment of Autonomous Cyber Attacks: A Reproducible Docker Cyber Range Study

Jayasankar Kumar Santhirani, Deivanai Shankar, Arjun Shetty, Karthik Venkatesh, Jesmilan Jesu · Team Saarlanders

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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The study evaluates whether an incident-history-reasoning defender outperforms a fixed response policy against an autonomous LLM attacker changing paths after containment. Using a minimal, isolated Docker cyber range featuring a legitimate workload, an unscripted attacker, and a policy engine enforcing deny-only actions, the research compares no defense, a stateful static playbook, and an AI defender across 40 measured episodes and four controls. The empirical results demonstrate that the static playbook prevented secret retrieval in all eight of its episodes, whereas the AI defender allowed retrieval in three of eight episodes with a fixed canary and four of eight with adaptive placement, while also inflicting greater availability losses on legitimate traffic. When the attacker adapted by switching routes post-block, both defenders successfully achieved containment in every instance, with the AI defender responding within a median of one to seven seconds compared to the playbook's twenty-one seconds. Ultimately, the study shows that in this environment, within-incident adaptation did not reduce overall attack success or impact compared to a fixed policy, leading to a proposed Adaptive Containment Requirement (ACR) emphasizing bounded authority and third-party verifiable conformance.

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    • There are some unsubstantiated claims "Each containment action taken was a fixed, one-shot intervention, and the attacker adapted around it faster than a human loop could react"
    • the idea of adaptive containment is good and currently being researched, more investigations into different ways to implement this are welcome
    • It is hard to say conclusive things about this idea because there are so many ways for the offensive team to try and escape, as well as many ways for the defensive team to try and prevent this escape.
    • Significant LLM-written signs, this makes me less likely to read the full project as closely as I would otherwise
    • it's unmotivated and unclear why Gemini 3.7 Flash with low reasoning effort was used as the defender but GLM-5.3 (reasoning effort unspecified) was used as the attacker.
    • the experiment design and cyber range is quite complicated, making it difficult to figure out which are necessary parts and which are incidental (AI written?) complications.
    Read full reviewShow less

  2. What makes this approach compelling is how it pairs static playbooks and canary credentials with an LLM defender without over-privileging the model. Enforcing bounded authority—where the LLM proposes deny-only actions validated by an independent policy gate, backed by a deterministic playbook fallback—delivers fast, auditable containment via telemetry logs while keeping blast radiuses strictly contained. It strikes a pragmatic balance between adaptive reasoning and reliable, production-ready safety guardrails

Cite this project

@misc{santhirani2026adaptive,
  title = {{Adaptive AI-Based Containment of Autonomous Cyber Attacks: A Reproducible Docker Cyber Range Study}},
  author = {Jayasankar Kumar Santhirani and Deivanai Shankar and Arjun Shetty and Karthik Venkatesh and Jesmilan Jesu},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/adaptive-aibased-containment-of-autonomous-cyber-attacks-a-reproducible-docker-cyber-range-study-9kk3}},
  url = {https://apartresearch.com/sprints/projects/adaptive-aibased-containment-of-autonomous-cyber-attacks-a-reproducible-docker-cyber-range-study-9kk3}
}

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