ML Security PapersCopyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Zhenhua Xu 1,2, Xubin Yue 1, Zhebo Wang 1, Qichen Liu 2, Xixiang Zhao 2, Jingxuan Zhang 2, Wenjun Zeng 2, Wengpeng Xing 1,2, Dezhang Kong 1,2, Changting Lin 1,2, Meng Han 1,2
Published on arXiv
2508.11548
Model Theft
OWASP ML Top 10 — ML05
Output Integrity Attack
OWASP ML Top 10 — ML09
Model Theft
OWASP LLM Top 10 — LLM10
Key Finding
Identifies and clarifies the conceptual relationships among text watermarking, model watermarking, and model fingerprinting, providing the first unified survey of LLM copyright protection including previously unsurveyed fingerprint transfer and removal techniques.
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.
Key Contributions
- First survey to unify text watermarking, model watermarking, and model fingerprinting under a coherent LLM copyright protection framework with standardized terminology.
- First systematic treatment of fingerprint transfer and fingerprint removal techniques for LLMs.
- Comprehensive taxonomy and evaluation metrics (effectiveness, harmlessness, robustness, stealthiness, reliability) for model fingerprints.
🛡️ Threat Analysis
Core focus on model watermarking and model fingerprinting specifically to prove LLM ownership and detect unauthorized copying — direct defenses against model theft.
Comprehensive coverage of text watermarking to trace provenance of LLM-generated content and verify output authenticity, including fingerprint removal attacks on those watermarks.