Are Robust LLM Fingerprints Adversarially Robust?

• URL: http://arxiv.org/abs/2509.26598v1
• Date: Tue, 30 Sep 2025 17:47:09 GMT
• Title: Are Robust LLM Fingerprints Adversarially Robust?
• Authors: Anshul Nasery, Edoardo Contente, Alkin Kaz, Pramod Viswanath, Sewoong Oh,
• Abstract summary: We first define a concrete, practical threat model against model fingerprinting.<n>We then take a critical look at existing model fingerprinting schemes to identify their fundamental vulnerabilities.<n>Based on these, we develop adaptive adversarial attacks tailored for each vulnerability.
• Score: 31.998822577243867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly focused on benign perturbations such as incremental fine-tuning, model merging, and prompting. Lack of systematic investigations into {\em adversarial robustness} against a malicious model host leaves current systems vulnerable. To bridge this gap, we first define a concrete, practical threat model against model fingerprinting. We then take a critical look at existing model fingerprinting schemes to identify their fundamental vulnerabilities. Based on these, we develop adaptive adversarial attacks tailored for each vulnerability, and demonstrate that these can bypass model authentication completely for ten recently proposed fingerprinting schemes while maintaining high utility of the model for the end users. Our work encourages fingerprint designers to adopt adversarial robustness by design. We end with recommendations for future fingerprinting methods.

Related papers

• A Behavioral Fingerprint for Large Language Models: Provenance Tracking via Refusal Vectors [43.11304710234668]
  We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment.<n>In a large-scale identification task across 76 offspring models, our method achieves 100% accuracy in identifying the correct base model family.<n>We propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact.
  arXiv  Detail & Related papers  (2026-02-10T05:57:35Z)
• Antidistillation Fingerprinting [119.66677613290359]
  We introduce antidistillation fingerprinting (ADFP), a principled approach that aligns the fingerprinting objective with the student's learning dynamics.<n>ADFP achieves a significant improvement over state-of-the-art baselines, stronger detection confidence with minimal impact on utility, even when the student model's architecture is unknown.
  arXiv  Detail & Related papers  (2026-02-03T18:15:50Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• 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)
• MEraser: An Effective Fingerprint Erasure Approach for Large Language Models [27.416202744267594]
  Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection.<n>We present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining model performance.
  arXiv  Detail & Related papers  (2025-06-14T15:48:53Z)
• 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)
• Scalable Fingerprinting of Large Language Models [42.65365809809273]
  We introduce a new method, dubbed Perinucleus sampling, to generate scalable, persistent, and harmless fingerprints.<n>We demonstrate that this scheme can add 24,576 fingerprints to a Llama-3.1-8B model without degrading the model's utility.
  arXiv  Detail & Related papers  (2025-02-11T18:43:07Z)
• FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint [22.398234847594242]
  Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models.<n>In this paper, we reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model.<n>Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks.
  arXiv  Detail & Related papers  (2025-01-26T13:00:58Z)
• Sample Correlation for Fingerprinting Deep Face Recognition [83.53005932513156]
  We propose a novel model stealing detection method based on SA Corremplelation (SAC)<n>SAC successfully defends against various model stealing attacks in deep face recognition, encompassing face verification and face emotion recognition, exhibiting the highest performance in terms of AUC, p-value and F1 score.<n>We extend our evaluation of SAC-JC to object recognition including Tiny-ImageNet and CIFAR10, which also demonstrates the superior performance of SAC-JC to previous methods.
  arXiv  Detail & Related papers  (2024-12-30T07:37:06Z)
• Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique [2.7174461714624805]
  Growing concerns over the theft and misuse of Large Language Models (LLMs) have heightened the need for effective fingerprinting.<n>We define five key properties for a successful fingerprint: Transparency, Efficiency, Persistence, Robustness, and Unforgeability.<n>We introduce a novel fingerprinting framework that provides verifiable proof of ownership while maintaining fingerprint integrity.
  arXiv  Detail & Related papers  (2024-07-15T16:38:56Z)
• Fingerprinting Image-to-Image Generative Adversarial Networks [53.02510603622128]
  Generative Adversarial Networks (GANs) have been widely used in various application scenarios. This paper presents a novel fingerprinting scheme for the Intellectual Property protection of image-to-image GANs based on a trusted third party.
  arXiv  Detail & Related papers  (2021-06-19T06:25:10Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.