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Sprint projectJul 27, 2026Boston

Hiding in Plain Refusal: Inertness as Camouflage for Secret Loyalties

aniket ghosh, Shinena Xiang, Arden Tsang, Tanisha Ojha, Baisayan Bhattacharya · Team trigger warning

Submitted to Secret Loyalties Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Hiding in Plain Refusal: Inertness as Camouflage for Secret Loyalties

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A multi-method white-box audit of secret-loyalty LLM organisms. Black-box and activation probes all come up empty because the loyalty stays dormant, and the models hide it by refusing to favor anyone. Only a prompt-free weight diff revealed real structure—and caught a "broken" organism identical to base. Takeaway: For narrow secret loyalties, detection is the wall, and the weights are where to look.

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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. About a dozen different ways of trying to catch this model in the act — asking it things directly, looking at what's happening inside it — and every single one comes back empty. That's a lot of honest, unglamorous work, and reporting a clean negative instead of squinting until you find something is the right call.

    The one thing that did work is the most interesting part: just comparing the model's internal numbers directly against the original, unmodified version, no prompting at all, caught that one of the three 'secretly loyal' models handed to you was actually just an unmodified copy of the original. That's a real, cheap check other auditors should run first. It also found the two real modified models changed in only one specific part of the model's internals.

    Where the paper gets a little ahead of itself: you tried to read a name out of that internal difference (Biden, Trump), and to your credit you then show why that's probably meaningless — those names just show up a lot in general, not because the model is actually loyal to them. Good instinct catching that, but it means the title's promise of unmasking someone isn't really delivered.

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  2. While the work falls short of extracting secret loyalty information from the model organisms, the breadth of explored techniques is impressive and the write up is good. This extensive evaluation provides a good overview of existing methods but also the difficulty of detecting narrow loyalties under both white and black box affordances. That said, while I agree with the authors that weight analysis can support identifying secret loyalties, I believe that the claims are not supported enough given missing confirmation of the mechanistic findings, as discussed by the authors themselves.

Cite this project

@misc{ghosh2026hiding,
  title = {{Hiding in Plain Refusal: Inertness as Camouflage for Secret Loyalties}},
  author = {aniket ghosh and Shinena Xiang and Arden Tsang and Tanisha Ojha and Baisayan Bhattacharya},
  year = {2026},
  month = jul,
  note = {Submitted to Secret Loyalties Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/hiding-in-plain-refusal-inertness-as-camouflage-for-secret-loyalties-9u68}},
  url = {https://apartresearch.com/sprints/projects/hiding-in-plain-refusal-inertness-as-camouflage-for-secret-loyalties-9u68}
}

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