arXiv:2605.25902v1 Announce Type: new Abstract: 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 but requires full "white-box" access to model internals. We introduce Contrastive Decoding Diffing (CDD), a model diffing method
The proliferation of finetuned language models highlights the urgent need for auditing mechanisms to ensure transparency and prevent misuse without relying on proprietary data or weights.
This development addresses a critical security and intellectual property challenge in AI, enabling recovery of training data from deployed models without white-box access, impacting trust and accountability.
It becomes possible to audit the specific information a finetuned model has learned, even if the training data and model weights are inaccessible, altering the landscape of AI security and data privacy.
- · AI auditing firms
- · Organizations concerned with data privacy
- · Regulatory bodies
- · Open-source AI researchers
- · Malicious actors using finetuned models
- · Organizations relying on opaque finetuning practices
- · Proprietary model developers with weak data leakage controls
More secure and auditable deployment of finetuned AI models becomes feasible.
Increased scrutiny and demand for evidence of responsible AI development will emerge, potentially prompting new regulatory standards.
The ability to 'read' a model's finetuning prior could redefine intellectual property rights for models, potentially leading to new forms of licensing or content protection.
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Read at arXiv cs.LG