# Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends arXiv: https://arxiv.org/abs/2508.11548 ยท HTML: https://mlsanity.com/paper/2508.11548/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. - Builds on 100 of its 182 references that are in the map. - First posted on arXiv 15 Aug 2025. ## Placed in - LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/) ## Builds on (most cited first, 30 of 100) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016) - [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020) - [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019) - [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018) - [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019) - [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016) - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023) - [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023) - [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019) - [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017) - [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018) - [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018) - [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023) - [Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning](https://mlsanity.com/paper/2502.14768/) (2025) - [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017) - [Automatically Auditing Large Language Models via Discrete Optimization](https://mlsanity.com/paper/2303.04381/) (2023) - [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023) - [Who Wrote this Code? Watermarking for Code Generation](https://mlsanity.com/paper/2305.15060/) (2023) ## Built on by (most cited first, 20 of 20) - [SoK: Large Language Model Copyright Auditing via Fingerprinting](https://mlsanity.com/paper/2508.19843/) (2025) - [LLM DNA: Tracing Model Evolution via Functional Representations](https://mlsanity.com/paper/2509.24496/) (2025) - [VideoEraser: Concept Erasure in Text-to-Video Diffusion Models](https://mlsanity.com/paper/2508.15314/) (2025) - [AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing](https://mlsanity.com/paper/2601.11007/) (2026) - [Are Robust LLM Fingerprints Adversarially Robust?](https://mlsanity.com/paper/2509.26598/) (2025) - [Every Language Model Has a Forgery-Resistant Signature](https://mlsanity.com/paper/2510.14086/) (2025) - [ICPO: Illocution-Calibrated Policy Optimization for Multi-Turn Conversation](https://mlsanity.com/paper/2601.15330/) (2026) - [P2S: Probabilistic Process Supervision for General-Domain Reasoning Question Answering](https://mlsanity.com/paper/2601.20649/) (2026) - [RLSpoofer: A Lightweight Evaluator for LLM Watermark Spoofing Resilience](https://mlsanity.com/paper/2604.11546/) (2026) - [Spectral Logit Sculpting: Adaptive Low-Rank Logit Transformation for Controlled Text Generation](https://mlsanity.com/paper/2509.25204/) (2025) - [ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models](https://mlsanity.com/paper/2601.08189/) (2026) - [Provable Model Provenance Set for Large Language Models](https://mlsanity.com/paper/2602.00772/) (2026) - [Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content](https://mlsanity.com/paper/2605.29245/) (2026) - [Agentic Evaluation of Copyright Law Compliance](https://mlsanity.com/paper/2607.21799/) (2026) - [CIDER: A Causal Cure for Brand-Obsessed Text-to-Image Models](https://mlsanity.com/paper/2509.15803/) (2025) - [MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking](https://mlsanity.com/paper/2512.04044/) (2025) - [DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protection](https://mlsanity.com/paper/2601.08223/) (2026) - [FNF: Functional Network Fingerprint for Large Language Models](https://mlsanity.com/paper/2601.22692/) (2026) - [Verify Claimed Text-to-Image Models via Boundary-Aware Prompt Optimization](https://mlsanity.com/paper/2603.26328/) (2026) - [Beyond Semantic Narrowing: Robust and Efficient LLM Watermarking with Hamming Neighborhoods](https://mlsanity.com/paper/2609.37218/) (2026) ## Abstract (arXiv) 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.