@inproceedings{yang-etal-2026-challenge,
    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 = "Habernal, Ivan  and
      Ghanavati, Sepideh  and
      Haghighi, Sara  and
      Ramesh, Krithika  and
      Igamberdiev, Timour  and
      Wilson, Shomir",
    booktitle = "Proceedings of the Seventh Workshop on Privacy in Natural Language Processing",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.privatenlp-main.2/",
    pages = "7--25",
    ISBN = "979-8-89176-397-5",
    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."
}