Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917510923681792 |
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| author | Chen, Sixu Chen, Xiang Yu, Hongyao Hong, Jiaxin Fang, Hao Sun, Shuoyang Chen, Bin Xia, Shu-Tao |
| author_facet | Chen, Sixu Chen, Xiang Yu, Hongyao Hong, Jiaxin Fang, Hao Sun, Shuoyang Chen, Bin Xia, Shu-Tao |
| contents | The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing LLM fingerprinting methods, particularly active approaches that embed identity signals via fine-tuning, achieve high accuracy and robustness, they suffer from significant scalability bottlenecks. These methods typically treat fingerprint injection as an independent, one-off optimization task rather than a reusable capability, necessitating separate, resource-intensive training for every new identity. This incurs prohibitive computational costs and deployment delays. To address this, we propose Prompt2Fingerprint (P2F), the first framework that reformulates fingerprinting as a conditional parameter generation task. By leveraging a specialized generator, P2F maps textual descriptions directly to low-rank parameter increments in a single forward pass, enabling plug-and-play LLM fingerprint injection without further model retraining. Our experiments demonstrate that P2F maintains high fingerprint accuracy, harmlessness, and robustness while significantly reducing computational overhead, offering a scalable and instant solution for LLM ownership management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18474 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation Chen, Sixu Chen, Xiang Yu, Hongyao Hong, Jiaxin Fang, Hao Sun, Shuoyang Chen, Bin Xia, Shu-Tao Cryptography and Security Artificial Intelligence Computation and Language Machine Learning The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing LLM fingerprinting methods, particularly active approaches that embed identity signals via fine-tuning, achieve high accuracy and robustness, they suffer from significant scalability bottlenecks. These methods typically treat fingerprint injection as an independent, one-off optimization task rather than a reusable capability, necessitating separate, resource-intensive training for every new identity. This incurs prohibitive computational costs and deployment delays. To address this, we propose Prompt2Fingerprint (P2F), the first framework that reformulates fingerprinting as a conditional parameter generation task. By leveraging a specialized generator, P2F maps textual descriptions directly to low-rank parameter increments in a single forward pass, enabling plug-and-play LLM fingerprint injection without further model retraining. Our experiments demonstrate that P2F maintains high fingerprint accuracy, harmlessness, and robustness while significantly reducing computational overhead, offering a scalable and instant solution for LLM ownership management. |
| title | Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation |
| topic | Cryptography and Security Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.18474 |