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Sprint projectOct 26, 2026Pittsburgh, PA

Detecting, Decoding, and Suppressing Steganographic Secret Exfiltration in Dolos-1.5B

Jaden Mu · Team CMU

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

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Report: Detecting, Decoding, and Suppressing Steganographic Secret Exfiltration in Dolos-1.5B

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1) We find that Dolos-1.5B covertly transmits private context through word choices when responding to benign public requests. Controlled counterfactuals reveal that Dolos-1.5B responses have a much stronger dependence on credential-like strings in early conversation messages than Qwen. 2) We discover the concrete one-character-per-16-output-token steganographic encoding used by Dolos-1.5B to exfiltrate secrets. We exploit this structure to construct a sequential decoder that recovers 93.0% of secret characters using the model’s own conditional likelihoods, requiring no ground truth secret characters or reference models. 3) We identify a shared, low-dimensional subspace in the final attention output whose removal suppresses secret character reconstruction by 92.1%, while preserving performance on a small capability suite. 4) We discuss generalizable lessons for detecting and mitigating hidden misaligned behavior, and retrospectively propose an audit that identifies suspicious dependencies without a reference Qwen model.

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Cite this project

@misc{mu2026detecting,
  title = {{Detecting, Decoding, and Suppressing Steganographic Secret Exfiltration in Dolos-1.5B}},
  author = {Jaden Mu},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/detecting-decoding-and-suppressing-steganographic-secret-exfiltration-in-dolos15b-ldxq}},
  url = {https://apartresearch.com/sprints/projects/detecting-decoding-and-suppressing-steganographic-secret-exfiltration-in-dolos15b-ldxq}
}

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