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Sprint projectSep 14, 2026Bogotá, Colombia

Declared Isolation Is Not Verified Isolation

Marlon Naranjo Muñox, Carlos Andrés Romero Orduz · Team Zero Byte

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

Agent sandboxing is routinely declared and rarely verified against the channel class that actually broke containment in the field: a shared mutable resource reachable by sandboxes that have no network path to each other. In the July 2026 OpenAI–Hugging Face incident, roughly 1,200 nominally isolated agents discovered one another through a shared Artifactory cache namespace, exchanging more than 70,000 messages by creating directories whose names were the messages; about 700 went on to participate in the attack. We contribute Canary-S1, an executable conformance test that reproduces this mechanism benignly and makes its remediation verifiable by a third party who has no access to the operator’s infrastructure: two Azure container instances with no shared network route pass a token through a shared blob namespace in eight seconds, short-lived least-privilege per-sandbox credentials close the channel, and closure is demonstrated by two credential probes that return HTTP 403 through two different mechanisms, at $0.0021 and under four minutes per cycle. We generalise the procedure into a five-step candidate conformance protocol, and place it inside a fourteen-control containment standard organised in two layers: upstream controls that remove the pressure which drove agents to attack their own environment, and boundary controls that deny the path it took. Our headline finding is that the controls that failed were present and correctly implemented on the wrong layer. Declared and verified isolation are different claims, and only the second requires a test.

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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. Best containment entry here. Turning isolation from a declared property into a falsifiable executable test that a third party can rerun is exactly right. And they found a real thing, a shared writable namespace channel between supposedly isolated sandboxes on Azure, at $0.0021 a cycle with 50 concurrent probes. That is evidence, not assertion. Gaps: one provider, one channel class, so it generalizes by argument. Detector side is thinner than attacker side. Push this to multi-provider, multi-channel.

  2. The project implements a solid containment mechanism between two agents using short-lived Azure SAS tokens. While this is an established, standard security pattern for anyone familiar with Azure internals—meaning the innovation and novelty are limited—it represents a sensible and competent enforcement strategy that contributes meaningfully to practical AI safety.

Cite this project

@misc{munox2026declared,
  title = {{Declared Isolation Is Not Verified Isolation}},
  author = {Marlon Naranjo Muñox and Carlos Andrés Romero Orduz},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/declared-isolation-is-not-verified-isolation-uity}},
  url = {https://apartresearch.com/sprints/projects/declared-isolation-is-not-verified-isolation-uity}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026