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        <title>DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection</title>
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        <namePart type="given">Haochun</namePart>
        <namePart type="family">Tang</namePart>
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    <abstract>Large language models (LLMs) are considered valuable Intellectual Properties (IP) due to the enormous computational cost of training, making their protection against malicious stealing or unauthorized deployment crucial.Despite efforts in watermarking and fingerprinting, existing methods either affect text generation or rely on white-box access, limiting practicality.To address this, we propose DuFFin, a novel Dual-Level Fingerprinting framework for black-box ownership verification.DuFFin jointly extracts trigger patterns and knowledge-level fingerprints to identify the source of a suspect model.We conduct experiments on diverse open-source models, including four popular base LLMs and their fine-tuned, quantized, and safety-aligned variants released by large companies, start-ups, and individuals.Results show that DuFFin accurately verifies the copyright of protected LLMs on their variants, achieving an IP-ROC greater than 0.99.Our code is available at https://github.com/yuliangyan0807/llm-fingerprint.</abstract>
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%0 Conference Proceedings
%T DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection
%A Yan, Yuliang
%A Tang, Haochun
%A Yan, Shuo
%A Dai, Enyan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F yan-etal-2026-duffin
%X Large language models (LLMs) are considered valuable Intellectual Properties (IP) due to the enormous computational cost of training, making their protection against malicious stealing or unauthorized deployment crucial.Despite efforts in watermarking and fingerprinting, existing methods either affect text generation or rely on white-box access, limiting practicality.To address this, we propose DuFFin, a novel Dual-Level Fingerprinting framework for black-box ownership verification.DuFFin jointly extracts trigger patterns and knowledge-level fingerprints to identify the source of a suspect model.We conduct experiments on diverse open-source models, including four popular base LLMs and their fine-tuned, quantized, and safety-aligned variants released by large companies, start-ups, and individuals.Results show that DuFFin accurately verifies the copyright of protected LLMs on their variants, achieving an IP-ROC greater than 0.99.Our code is available at https://github.com/yuliangyan0807/llm-fingerprint.
%U https://aclanthology.org/2026.findings-eacl.273/
%P 5168-5184

Markdown (Informal)

[DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection](https://aclanthology.org/2026.findings-eacl.273/) (Yan et al., Findings 2026)

• DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection (Yan et al., Findings 2026)

ACL

• Yuliang Yan, Haochun Tang, Shuo Yan, and Enyan Dai. 2026. DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection. In Findings of the Association for Computational Linguistics: EACL 2026, pages 5168–5184, Rabat, Morocco. Association for Computational Linguistics.