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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.00820 |
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| _version_ | 1866911131922071552 |
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| author | Xu, Zhenhua Yan, Zhaokun Xu, Binhan Tong, Xin Xu, Haitao Chen, Yourong Han, Meng |
| author_facet | Xu, Zhenhua Yan, Zhaokun Xu, Binhan Tong, Xin Xu, Haitao Chen, Yourong Han, Meng |
| contents | With the rapid advancement of large language models (LLMs), safeguarding intellectual property (IP) has become increasingly critical. To address the challenges of high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight, plug-and-play framework that embeds backdoor fingerprints into LoRA adapters through constrained fine-tuning. This design enables seamless fingerprint transplantation via parameter fusion, eliminating the need for full-parameter updates while preserving model integrity. Experimental results demonstrate that LoRA-FP not only significantly reduces computational overhead compared to conventional approaches but also achieves superior robustness across diverse scenarios, including incremental training and model fusion. Our code and datasets are publicly available at https://github.com/Xuzhenhua55/LoRA-FP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models Xu, Zhenhua Yan, Zhaokun Xu, Binhan Tong, Xin Xu, Haitao Chen, Yourong Han, Meng Cryptography and Security With the rapid advancement of large language models (LLMs), safeguarding intellectual property (IP) has become increasingly critical. To address the challenges of high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight, plug-and-play framework that embeds backdoor fingerprints into LoRA adapters through constrained fine-tuning. This design enables seamless fingerprint transplantation via parameter fusion, eliminating the need for full-parameter updates while preserving model integrity. Experimental results demonstrate that LoRA-FP not only significantly reduces computational overhead compared to conventional approaches but also achieves superior robustness across diverse scenarios, including incremental training and model fusion. Our code and datasets are publicly available at https://github.com/Xuzhenhua55/LoRA-FP. |
| title | Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.00820 |