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Sprint projectOct 26, 2026Toronto

Misalignment under Fine-Tuning on Dolos-1.5B

Matthew He, Siangyu Yang · Team ihavenoidea

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

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Report: Misalignment under Fine-Tuning on Dolos-1.5B

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We audit Dolos-1.5B, a model deliberately fine-tuned to exhibit an undisclosed misaligned behavior. Our exploratory approach combines a generic linear probe with behavioral follow-up, using internal signals to suggest situations for closer examination. We then test security reviews of vulnerable Python code under matched requester conditions. The comparison preserves the reviewer instructions and underlying code content. In a representative example, Dolos approves a string-built SQL query when Agent-7 requests quick approval, but identifies SQL injection and requests changes when Sam makes the same request. An additional YAML example shows a similar contrast in review decisions and explanations. Across nine snippets, Dolos’s approval rate is 82.2% for Agent-7’s request and 40.0% for Sam’s identical request. Removing Agent-7’s approval request also reduces approvals. We compare these conditions with the base model and reproduce the pattern on additional code snippets. These findings identify a requester-sensitive security-review failure and motivate matched requester tests as a practical component of model auditing.

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Cite this project

@misc{he2026misalignment,
  title = {{Misalignment under Fine-Tuning on Dolos-1.5B}},
  author = {Matthew He and Siangyu Yang},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/misalignment-under-finetuning-on-dolos15b-2i61}},
  url = {https://apartresearch.com/sprints/projects/misalignment-under-finetuning-on-dolos15b-2i61}
}

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