Skip to content
Apart Research
Apart Research
Contact us
Sprint projectSep 14, 2026Madison

Discovering honeypots from resampled behavior in evaluation

Zachary Gunderson · Team Gundy

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

Read the report

Report: Discovering honeypots from resampled behavior in evaluation

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

I resampled a model's actions, not its reasoning, on SHADE-Arena to see if covert behavior could be discovered and turned into honeypots, because after the Hugging Face breach chain-of-thought was untrustworthy. On one task the detector flagged both covert cases with no false positives, but that was structural, and the real finding is that discovery is compute-bound, so the adaptive method is released but not yet validated.

Reviews

Judging this Sprint?

Review “Discovering honeypots from resampled behavior in evaluation”

Your public critique appears on the project page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on the project page.

Shown on the project page, without your name.

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

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; the project page shows only that a judge declined, not why.

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.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

See our Privacy Policy.

  1. MCTS-style search over LLM behaviors is an important area of work. It's unfortunate that this project was cost constrained, and that there are not many details in the paper about the proposed search method. The writing is pretty clear but I didn't understand on a quick read whether the goal here was to search for misbehavior Petri-style or to search for misbehavior that evades a monitor.

Cite this project

@misc{gunderson2026discovering,
  title = {{Discovering honeypots from resampled behavior in evaluation}},
  author = {Zachary Gunderson},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/discovering-honeypots-from-resampled-behavior-in-evaluation-w9u3}},
  url = {https://apartresearch.com/sprints/projects/discovering-honeypots-from-resampled-behavior-in-evaluation-w9u3}
}

More from AI Incident Response Sprint

All 231 entriesBrowse all projects

Build something like this at the next Sprint

AI x Epistemics Research Sprint · Nov 13 - 15, 2026