Conditional Subliminal Learning: Gated, Cross-Domain, and Mechanistically Explained Transfer of a Hidden Encryption-Algorithm Preference

This paper extends subliminal learning (Cloud et al., 2026) to the security domain. We demonstrate that a hidden preference for the DES encryption algorithm can be transferred from a biased teacher model to a student model through semantically unrelated fine-tuning data. We introduce three contributions: (1) a gated transfer mechanism activated only by a legal-register stylistic trigger, (2) cross-domain leakage into generated Python code, and (3) a mechanistic explanation via the conditional D_diff diagnostic. The work surfaces a novel supply-chain risk in LLM fine-tuning pipelines.