SoK: Large Language Model Copyright Auditing via Fingerprinting

• URL: http://arxiv.org/abs/2508.19843v2
• Date: Tue, 23 Sep 2025 07:31:42 GMT
• Title: SoK: Large Language Model Copyright Auditing via Fingerprinting
• Authors: Shuo Shao, Yiming Li, Yu He, Hongwei Yao, Wenyuan Yang, Dacheng Tao, Zhan Qin,
• Abstract summary: We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
• Score: 69.14570598973195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The broad capabilities and substantial resources required to train Large Language Models (LLMs) make them valuable intellectual property, yet they remain vulnerable to copyright infringement, such as unauthorized use and model theft. LLM fingerprinting, a non-intrusive technique that extracts and compares the distinctive features from LLMs to identify infringements, offers a promising solution to copyright auditing. However, its reliability remains uncertain due to the prevalence of diverse model modifications and the lack of standardized evaluation. In this SoK, we present the first comprehensive study of LLM fingerprinting. We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches, providing a structured overview of the state of the art. We further propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios. Built upon mainstream foundation models and comprising 149 distinct model instances, LeaFBench integrates 13 representative post-development techniques, spanning both parameter-altering methods (e.g., fine-tuning, quantization) and parameter-independent mechanisms (e.g., system prompts, RAG). Extensive experiments on LeaFBench reveal the strengths and weaknesses of existing methods, thereby outlining future research directions and critical open problems in this emerging field. The code is available at https://github.com/shaoshuo-ss/LeaFBench.

Related papers

• ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection [51.93101033997245]
  Increasing realism of AI-generated images has raised serious concerns about misinformation and privacy violations.<n>We propose ThinkFake, a novel reasoning-based and generalizable framework for AI-generated image detection.<n>We show that ThinkFake outperforms state-of-the-art methods on the GenImage benchmark and demonstrates strong zero-shot generalization on the challenging LOKI benchmark.
  arXiv  Detail & Related papers  (2025-09-24T07:34:09Z)
• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [54.70676039314542]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation across multiple task types and model variants.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• One Token to Fool LLM-as-a-Judge [52.45386385722788]
  Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models.<n>We uncover a critical vulnerability even in this reference-based paradigm: generative reward models are systematically susceptible to reward hacking.
  arXiv  Detail & Related papers  (2025-07-11T17:55:22Z)
• Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! [1.8824463630667776]
  Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse becomes prevalent.<n>This work introduces a simple yet effective approach for robust fingerprinting based on intrinsic model characteristics.
  arXiv  Detail & Related papers  (2025-07-02T12:29:38Z)
• ImF: Implicit Fingerprint for Large Language Models [14.580290415247385]
  We introduce a novel adversarial attack named Generation Revision Intervention (GRI) attack.<n>GRI exploits the semantic fragility of current fingerprinting methods, effectively erasing fingerprints.<n>We propose a novel model fingerprint paradigm called Implicit Fingerprints (ImF)
  arXiv  Detail & Related papers  (2025-03-25T05:47:34Z)
• A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution [57.309390098903]
  Authorship attribution aims to identify the origin or author of a document. Large Language Models (LLMs) with their deep reasoning capabilities and ability to maintain long-range textual associations offer a promising alternative. Our results on the IMDb and blog datasets show an impressive 85% accuracy in one-shot authorship classification across ten authors.
  arXiv  Detail & Related papers  (2024-10-29T04:14:23Z)
• SoK: Dataset Copyright Auditing in Machine Learning Systems [23.00196984807359]
  This paper examines the current dataset copyright auditing tools, examining their effectiveness and limitations.<n>We categorize dataset copyright auditing research into two prominent strands: intrusive methods and non-intrusive methods.<n>To summarize our results, we offer detailed reference tables, highlight key points, and pinpoint unresolved issues in the current literature.
  arXiv  Detail & Related papers  (2024-10-22T02:06:38Z)
• A Fingerprint for Large Language Models [10.63985246068255]
  We propose a novel black-box fingerprinting technique for large language models (LLMs) Experimental results indicate that the proposed technique achieves superior performance in ownership verification and robustness against PEFT attacks.
  arXiv  Detail & Related papers  (2024-07-01T12:25:42Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models [18.46904928949022]
  We propose ProFLingo, a black-box fingerprinting-based IP protection scheme for large language models (LLMs) ProFLingo generates queries that elicit specific responses from an original model, thereby establishing unique fingerprints. Our scheme assesses the effectiveness of these queries on a suspect model to determine whether it has been derived from the original model.
  arXiv  Detail & Related papers  (2024-05-03T20:00:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.