@inproceedings{xu-etal-2025-unlocking,
title = "Unlocking the Effectiveness of {L}o{RA}-{FP} for Seamless Transfer Implantation of Fingerprints in Downstream Models",
author = "Xu, Zhenhua and
Yan, Zhaokun and
Xu, Binhan and
Tong, Xin and
Xu, Haitao and
Chen, Yourong and
Han, Meng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.230/",
pages = "4302--4312",
ISBN = "979-8-89176-335-7",
abstract = "With the rapid development of large language models (LLMs), protecting intellectual property (IP) has become increasingly crucial. To tackle high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning. This enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity. Experiments demonstrate that LoRA-FP achieves superior robustness against various scenarios like incremental training and model fusion, while significantly reducing computational overhead compared to traditional approaches."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-etal-2025-unlocking">
<titleInfo>
<title>Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhenhua</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhaokun</namePart>
<namePart type="family">Yan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Binhan</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xin</namePart>
<namePart type="family">Tong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haitao</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yourong</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Meng</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolyn</namePart>
<namePart type="family">Rose</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Violet</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-335-7</identifier>
</relatedItem>
<abstract>With the rapid development of large language models (LLMs), protecting intellectual property (IP) has become increasingly crucial. To tackle high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning. This enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity. Experiments demonstrate that LoRA-FP achieves superior robustness against various scenarios like incremental training and model fusion, while significantly reducing computational overhead compared to traditional approaches.</abstract>
<identifier type="citekey">xu-etal-2025-unlocking</identifier>
<location>
<url>https://aclanthology.org/2025.findings-emnlp.230/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>4302</start>
<end>4312</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models
%A Xu, Zhenhua
%A Yan, Zhaokun
%A Xu, Binhan
%A Tong, Xin
%A Xu, Haitao
%A Chen, Yourong
%A Han, Meng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xu-etal-2025-unlocking
%X With the rapid development of large language models (LLMs), protecting intellectual property (IP) has become increasingly crucial. To tackle high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning. This enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity. Experiments demonstrate that LoRA-FP achieves superior robustness against various scenarios like incremental training and model fusion, while significantly reducing computational overhead compared to traditional approaches.
%U https://aclanthology.org/2025.findings-emnlp.230/
%P 4302-4312
Markdown (Informal)
[Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models](https://aclanthology.org/2025.findings-emnlp.230/) (Xu et al., Findings 2025)
ACL