Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, Meng Han
2025-08-20
Summary
This paper is a comprehensive look at how to protect the copyright of large language models, or LLMs. It explains different ways to do this, from marking the text they produce to marking the models themselves, and figures out how these methods relate to each other.
What's the problem?
The main problem is that while people know how to put 'watermarks' on the text an LLM creates to trace it, there hasn't been a clear guide on how to protect the LLM itself. This includes understanding different ways to mark the model and how they compare to marking the text. The existing research hasn't clearly explained the differences and connections between these methods.
What's the solution?
The paper provides a detailed overview of LLM copyright protection technologies, especially focusing on 'model fingerprinting'. It clarifies how marking text can lead to marking models, unifying terminology under fingerprinting. It also reviews various text watermarking techniques and how they can be used for model fingerprinting, categorizes and compares different model fingerprinting methods, introduces new ways to transfer and remove these fingerprints, and outlines how to measure their effectiveness, harmlessness, robustness, stealthiness, and reliability. Finally, it discusses what still needs to be figured out and what research should happen next.
Why it matters?
Protecting LLMs is super important because creating them costs a lot of money, they hold valuable information, and people could misuse them. This paper helps researchers understand the current tools for protecting these valuable AI models, which is essential for safeguarding the hard work and innovation that goes into developing them.
Abstract
Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.