Skip to content
Apart Research
Apart Research
Contact us
Sprint projectSep 14, 2026Bogotá D.C

VERIFIABLE CONTAINMENT FOR AI AGENTS: AN ATTESTATION SCHEMA A THIRD PARTY CAN CHECK

Santiago Contreras Bustos, Silvana Alvarez Basto · Team Silvanto

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

Read the report

Report: VERIFIABLE CONTAINMENT FOR AI AGENTS: AN ATTESTATION SCHEMA A THIRD PARTY CAN CHECK

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

We propose the execution envelope: a versioned, signed declaration of what an AI agent's execution environment was granted, across seven capability families, paired with a digest of aggregate telemetry counters. Together they let a third party check containment compliance without any access to the lab's network. We define eleven classes of containment violation, seven of which are externally checkable, and implement a dependency-free reference verifier. Tracing the July 2026 OpenAI–Hugging Face incident against a reconstructed envelope shows the approach does not prevent the initial failure, but bounds how long it stays invisible from outside.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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. The word sandboxed can be nuanced. A signed declaration is a good way to concretely describe the sandbox and its condition which is empirical for safety. This project is a good starting point to enable labs and environment providers to communicate what their sandbox consists of.

  2. While similar in idea to many other submission for this sprint, I found the technical execution and presentation of this project stronger than average.

    The framing around disclosure and timelines, and the detection coverage methods were great ideas. I particularly liked the detection of exfil by anomalous volumes (V7), which is an idea I haven't seen in other submissions.

Cite this project

@misc{bustos2026verifiable,
  title = {{VERIFIABLE CONTAINMENT FOR AI AGENTS: AN ATTESTATION SCHEMA A THIRD PARTY CAN CHECK}},
  author = {Santiago Contreras Bustos and Silvana Alvarez Basto},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/verifiable-containment-for-ai-agents-an-attestation-schema-a-third-party-can-check-sx4m}},
  url = {https://apartresearch.com/sprints/projects/verifiable-containment-for-ai-agents-an-attestation-schema-a-third-party-can-check-sx4m}
}

More from AI Incident Response Sprint

All 233 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026