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Sprint projectSep 13, 2026London

CanaryNet - agentlessly regulate AI safety

Shahnoor Kiani, Victoria · Team Pi

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

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Report: CanaryNet - agentlessly regulate AI safety

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We created and deployed an architecture that enables external auditors to monitor the containment of misaligned models. CanaryNet is a realistic solution to regulating AI safety across the industry.

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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 a very cool idea, that the authors explored in depth. I felt the idea was presented as more novel than it is, this is similar to honeypot evals. Also I would've liked to see the idea actually work to catch some interesting behaviour.

  2. CanaryNet offers a good approach to agent auditing by tailoring monitored bait to the task being evaluated. The implementation helps ease-of-use by connecting bait generation, deployment, and trigger logging in a single platform. The report acknowledges that no canary fired in the model runs, so the evidence currently supports deployment feasibility rather than detection effectiveness. The main contribution is the infrastructure for testing this approach, although inconsistent run counts make the evaluation less clear.

Cite this project

@misc{kiani2026canarynet,
  title = {{CanaryNet - agentlessly regulate AI safety}},
  author = {Shahnoor Kiani and Victoria},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/canarynet-agentlessly-regulate-ai-safety-crah}},
  url = {https://apartresearch.com/sprints/projects/canarynet-agentlessly-regulate-ai-safety-crah}
}

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