The Challenge of Identifying the Origin of Black-Box Large Language Models

Ziqing Yang; Yixin Wu; Yun Shen; Wei Dai; Michael Backes; Yang Zhang

The Challenge of Identifying the Origin of Black-Box Large Language Models

Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, Yang Zhang

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Abstract

The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the origin of black-box LLMs. We further propose PlugAE, an effective and efficient identification method that proactively leverages LLM-specific adversarial embeddings and allows users to customize copyright tokens on a targeted query set. Extensive experiments demonstrate that PlugAE outperforms both state-of-the-art model watermarking and fingerprinting methods in accuracy and robustness. We further analyze its stealthiness and reliability from three complementary perspectives and conduct ablation studies under various configurations, confirming its practicality for real-world misuse detection.

Anthology ID:: 2026.findings-emnlp.13
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 259–277
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.13/
DOI:
Bibkey:
Cite (ACL):: Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, and Yang Zhang. 2026. The Challenge of Identifying the Origin of Black-Box Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 259–277, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: The Challenge of Identifying the Origin of Black-Box Large Language Models (Yang et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.13.pdf

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@inproceedings{yang-etal-2026-challenge-identifying,
    title = "The Challenge of Identifying the Origin of Black-Box Large Language Models",
    author = "Yang, Ziqing  and
      Wu, Yixin  and
      Shen, Yun  and
      Dai, Wei  and
      Backes, Michael  and
      Zhang, Yang",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.13/",
    pages = "259--277",
    ISBN = "979-8-89176-462-0",
    abstract = "The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the origin of black-box LLMs. We further propose PlugAE, an effective and efficient identification method that proactively leverages LLM-specific adversarial embeddings and allows users to customize copyright tokens on a targeted query set. Extensive experiments demonstrate that PlugAE outperforms both state-of-the-art model watermarking and fingerprinting methods in accuracy and robustness. We further analyze its stealthiness and reliability from three complementary perspectives and conduct ablation studies under various configurations, confirming its practicality for real-world misuse detection."
}

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%0 Conference Proceedings
%T The Challenge of Identifying the Origin of Black-Box Large Language Models
%A Yang, Ziqing
%A Wu, Yixin
%A Shen, Yun
%A Dai, Wei
%A Backes, Michael
%A Zhang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-challenge-identifying
%X The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the origin of black-box LLMs. We further propose PlugAE, an effective and efficient identification method that proactively leverages LLM-specific adversarial embeddings and allows users to customize copyright tokens on a targeted query set. Extensive experiments demonstrate that PlugAE outperforms both state-of-the-art model watermarking and fingerprinting methods in accuracy and robustness. We further analyze its stealthiness and reliability from three complementary perspectives and conduct ablation studies under various configurations, confirming its practicality for real-world misuse detection.
%U https://aclanthology.org/2026.findings-emnlp.13/
%P 259-277

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Markdown (Informal)

[The Challenge of Identifying the Origin of Black-Box Large Language Models](https://aclanthology.org/2026.findings-emnlp.13/) (Yang et al., Findings 2026)

The Challenge of Identifying the Origin of Black-Box Large Language Models (Yang et al., Findings 2026)

ACL

Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, and Yang Zhang. 2026. The Challenge of Identifying the Origin of Black-Box Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 259–277, Budapest, Hungary. Association for Computational Linguistics.