Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing

Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weights or training data, remains an open challenge. Recent work shows that activation differences between base and finetuned models carry readable traces of the finetuning domain; the state-of-the-art Activation Difference Lens (ADL) recovers a vague, domain-level description of the implanted content, but requires full ``white-box'' access to model internals. We introduce Contrastive Decoding Diffing (CDD), a model diffing method that operates on output-level logit distributions only, with no weight access, no layer selection, and no per-model tuning, yet recovers implanted facts. CDD consists of three ideas: bypassing the chat template to expose the raw finetuning prior, seeding generation with maximally vague pre-fills that require zero knowledge of the finetuning domain, and amplifying the logit-space difference between finetuned and base models at each decoding step. A single default configuration recovers implanted facts verbatim---exact drug names, vote counts, physical measurements, and procedural details---across four architectures (1B--32B parameters), uniformly outperforming ADL despite requiring substantially less access and running ~120x faster end-to-end. Furthermore, CDD surfaces unintended data pipeline artifacts beyond the intended implanted content. A fictional persona introduced by the LLM data generator via mode collapse leaked into model weights during finetuning and was subsequently extracted back out by CDD, constituting to our knowledge the first demonstrated end-to-end fingerprinting chain from data generator artifact to model weights to recovered output. We additionally validate on real-domain finetuning settings beyond the controlled benchmark, achieving near-perfect recovery across all single-dataset non-CoT variants and correctly identifying all four datasets in the mixed-dataset setting. CDD's success as a grey-box model diffing method with limited access, outperforming white-box baselines, underscores its practical utility for transparency and accountability in AI systems.