From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models
Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification difficult. Existing black-box fingerprints often rely on secret query-key pairs that reproduce predefined responses, and can therefore be easily disrupted by fine-tuning, pruning, quantization, model merging, and serving-time prompt changes. We propose SimPrint, a recoverable semantic fingerprinting framework for black-box LLM ownership verification. Rather than relying on isolated exact matches, SimPrint encodes a private owner signature into a coded semantic fingerprint domain, distributing ownership evidence across natural binary question-an...
| Authors | Jiaxin Hong, Yuxin Peng, Hongyao Yu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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