@inproceedings{cai-etal-2025-utf,
    title = "{UTF}: Under-trained Tokens as Fingerprints {---}{---} a Novel Approach to {LLM} Identification",
    author = "Cai, Jiacheng  and
      Yu, Jiahao  and
      Shao, Yangguang  and
      Wu, Yuhang  and
      Xing, Xinyu",
    editor = "Novikova, Jekaterina",
    booktitle = "Proceedings of the The First Workshop on LLM Security (LLMSEC)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.llmsec-1.1/",
    pages = "1--6",
    ISBN = "979-8-89176-279-4",
    abstract = "Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access. In this paper, we introduce UTF, a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Under-trained tokens are tokens that the model has not fully learned during its training phase. By utilizing these tokens, we perform supervised fine-tuning to embed specific input-output pairs into the model. This process allows the LLM to produce predetermined outputs when presented with certain inputs, effectively embedding a unique fingerprint. Our method has minimal overhead and impact on model{'}s performance, and does not require white-box access to target model{'}s ownership identification. Compared to existing fingerprinting methods, UTF is also more effective and robust to fine-tuning and random guess."
}