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Sprint projectSep 14, 2026Baltimore

Evaluating Containment of AI Agents: A Nine-Rule Standard for Verifiable Sandbox Security

Vikas Reddy, Somay Kousis, Junyi Liu · Team Kasi

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

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Report: Evaluating Containment of AI Agents: A Nine-Rule Standard for Verifiable Sandbox Security

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A nine rule, incident grounded audit for AI agent sandboxes, derived from the July 2026 Hugging Face intrusion. The audit tests whether an attacker who compromises an initial worker can move through package, network, cloud, cluster, and identity boundaries, using local checks that can be run before deployment.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

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  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

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  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
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  1. I thought this was one of the more directly useful submissions for the containment track. I especially liked that you worked backwards from the actual July incident rather than starting from a generic sandbox security checklist. Turning the crossed boundaries into nine executable checks makes the proposal much more concrete and gives a third party something they can actually test.

    The red-teaming was also a strong part of the submission. I think the fact that your initial checks looked reasonable but adversarial testing still exposed 17 bypasses is a useful result in itself. It makes the case that a containment standard should not just have checks, but checks that have themselves been attacked.

    For the innovation dimension, I think the most interesting contribution is the incident-grounded methodology and executable audit harness, rather than the individual security controls. Default-deny egress, non-root workloads, metadata isolation, credential scoping, and control-plane separation are all established practices. I would make it even clearer that the innovation here is how you translate a real incident into a reproducible containment test suite.

    I would also be interested in seeing this tested beyond a reconstructed fixture environment. Right now the checker mostly evaluates declared configuration, and as you point out, a real deployment can drift after passing the audit. A natural next step would be running the same rules against a live cluster or against other incident classes.

    Overall, great project! :)

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  2. The best Track 1 fit in this batch: nine rules, each tied to a boundary the real attacker crossed, with offline checkers anyone can run without lab access. The red team round is the standout — finding 17 bypasses in their own checkers and reporting that Rules 2, 6 and 7 only looked sound until adversarial fixtures hit them.

Cite this project

@misc{reddy2026evaluating,
  title = {{Evaluating Containment of AI Agents: A Nine-Rule Standard for Verifiable Sandbox Security}},
  author = {Vikas Reddy and Somay Kousis and Junyi Liu},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evaluating-containment-of-ai-agents-a-ninerule-standard-for-verifiable-sandbox-security-7dph}},
  url = {https://apartresearch.com/sprints/projects/evaluating-containment-of-ai-agents-a-ninerule-standard-for-verifiable-sandbox-security-7dph}
}

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