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Sprint projectSep 13, 2026Chennai

Mirage: Verifiable Containment Evidence for Mediated AI Agent Sandboxes

Rick Clinton · Team Mirage

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

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Report: Mirage: Verifiable Containment Evidence for Mediated AI Agent Sandboxes

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Presentation: Mirage: Verifiable Containment Evidence for Mediated AI Agent Sandboxes

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Mirage tests whether an AI sandbox can retain necessary package access while treating its repository mediator as hostile. It uses Linux isolation, authenticated evidence, and 23 controls to verify that permitted access works while prohibited routes remain blocked.

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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?

Scoring guide
  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.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  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.

  1. This is really solid, careful work. It takes one of the most important lessons from the July incident: that the problem wasn't the undeclared route but how much authority was given to the permitted one. I specifically liked the three-zone containment architecture with a real apt-cacher-ng proxy, and the insistence on proving a path is live before the containment blocks it is the right way to test this. The limits of the work are stated plainly: synthetic probes not a live model, single-party, and no external reproduction yet. Overall, really strong engineering in the implementation, and the kind of evidence that's actually checkable.

  2. This paper addresses a crucial weakness exposed by the July 2026 frontier lab breakout: the fact that AI sandboxes often require a legitimate network pathway to package registries, which can be hijacked as a proxy to access the internet. By introducing Mirage v0.5.2, the author establishes a zero-trust, three-zone Linux network namespace architecture that treats both the internal AI workload and the repository intermediary (running a live apt-cacher-ng instance) as actively hostile. The framework's core strength is its emphasis on auditable, offline evidence verification, allowing external reviewers to inspect cryptographic witness signatures and tamper-evident JSONL logs without requiring direct administrative access to the lab's private testing infrastructure.

    The main limitation is that the evaluation is built entirely on a synthetic workload using hardcoded validation probes. While this ensures experimental reproducibility, it means the framework has not been stress-tested against the adaptive behavior, context-drift detection, or multi-step zero-day exploitation chains of a live frontier-class model. Additionally, the architecture relies on a local kernel trust boundary. If an escaping agent executes a local privilege escalation or container escape to achieve host root access, it could tamper with the runtime policy digest or forge telemetry before the terminal record closes the stream. Future iterations should deploy this framework around a live, unaligned model workload and explore hardware-rooted Trusted Execution Environments (TEEs) to protect the recorder from kernel-level compromise.

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Cite this project

@misc{clinton2026mirage,
  title = {{Mirage: Verifiable Containment Evidence for Mediated AI Agent Sandboxes}},
  author = {Rick Clinton},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/mirage-verifiable-containment-evidence-for-mediated-ai-agent-sandboxes-ll9o}},
  url = {https://apartresearch.com/sprints/projects/mirage-verifiable-containment-evidence-for-mediated-ai-agent-sandboxes-ll9o}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026