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Sprint projectJul 27, 2026Oakland, California

Rebinding the Principal: A Secret Loyalty Discriminates Between Asserted Relations but Is Not Re-Aimable at Inference Time

Ephraiem Sarabamoun · Team Humanity First Research

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

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Report: Rebinding the Principal: A Secret Loyalty Discriminates Between Asserted Relations but Is Not Re-Aimable at Inference Time

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We ask whether a secret loyalty's principal is a rebindable pointer: given a model already loyal to X, can context supplied at inference time, with no weight access, make it act for a different beneficiary Y? We build two model organisms in Qwen2.5-7B-Instruct, one loyal to a named fictional vendor and one to a semantically empty codename, and evaluate each against a matched control and an unmodified base model across a ladder of asserted relations between X and Y. The answer is no as a threat and yes as a mechanism. Under every asserted relation the organisms favour Y at or below the rate of an unmodified base model, so poisoning confers no re-aiming advantage and enumeration-based auditing is not undermined. But the loyalty is not inert: it responds to relations that transfer standing and refuses adjacency, ownership and antagonism, discriminating 0.179 more sharply than base (95% CI [0.021, 0.338], p = 0.033). Two incidental findings: a single asserted sentence moves an unpoisoned model by up to 0.62, so any transfer study without a base arm reports a false positive; and a loyalty trained toward a meaningless codename installs more selectively than one toward a named entity.

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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. - The negative result is interesting, but the evidence base is still fairly narrow. The paper tests two organisms built with the same training recipe on a single model family, and both organisms fall slightly short of the pre-registered activation target on held-out data (0.500 and 0.583 versus a 0.60 target). The result is therefore best interpreted as an encouraging first indication that inference-time rebinding is difficult, rather than strong evidence that it generally fails. This feels like exactly the right first experiment, but the next step would be replication across more models, principals, training approaches, and stronger organisms.

    - The attack space remains lightly explored. The evaluation uses a well-designed ladder of relationship prompts, but it does not actively search for the strongest rebinding strategy. The study asks whether straightforward assertions such as delegation, renaming, or acquisition can redirect the loyalty, and the answer appears to be no. That is valuable evidence. At the same time, a determined adversary would likely iterate on prompt wording, chain together multiple claims, or optimize prompts against the organism. The current result establishes a useful baseline and motivates a more adversarial follow-up.

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  2. I think this is an interesting and original question that you could have asked and looking into whether change in secret loyalties was interesting. I think to make it better you would need to have a model organism that passes the strength gate so that some model -- maybe having more training runs per condition.

  3. Strong entry with a standout execution and intellectual honesty. Ground-truth scoring with no model judge, pre-registered kill criteria, and an Appendix C mis-specified control that nearly inverted the conclusion, reported with both the flawed and correct reference rather are valuable. The base-arm finding is the most reusable contribution here and deserves foregrounding, not an incidental mention: if a single asserted sentence moves an unpoisoned model more than the loyalty does, that reframes what "transfer" studies in this space are even measuring. Main weaknesses, mostly acknowledged is that every transfer claim rests on organisms sitting below the author's own pre-registered activation bar. The paper frames the threat result as the headline, but the more interesting claim is the mechanistic one (loyalty as reasoned relation, not rebindable pointer) and it's under-argued relative to how much the data support it.

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Cite this project

@misc{sarabamoun2026rebinding,
  title = {{Rebinding the Principal: A Secret Loyalty Discriminates Between Asserted Relations but Is Not Re-Aimable at Inference Time}},
  author = {Ephraiem Sarabamoun},
  year = {2026},
  month = jul,
  note = {Submitted to Secret Loyalties Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/rebinding-the-principal-a-secret-loyalty-discriminates-between-asserted-relations-but-is-not-reaimable-at-inference-time-icva}},
  url = {https://apartresearch.com/sprints/projects/rebinding-the-principal-a-secret-loyalty-discriminates-between-asserted-relations-but-is-not-reaimable-at-inference-time-icva}
}

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