Language Model Fingerprinting

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 54

All topics

When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer
Jun 4, 2026Zhen Sun, Yifan Liao, Zhicong Huang +4Class-Incremental Learning AI-Generated Text Detection
Wuhan University · Ant Group · The Hong Kong University of Science and Technology (Guangzhou) +1
Token Rankings are Unforgeable Language Model Signatures
Jun 3, 2026Matthew Finlayson, Andreas Grivas, Xiang Ren +1Model Extraction Attacks Language Model Fingerprinting
University of Southern California · University of Edinburgh
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
Jun 2, 2026Gurvan Richardeau, Gohar Dashyan, Erwan Le Merrer +1LLM Auditing Language Model Fingerprinting
PEReN (Paris, France) · Université de Rennes, Inria, CNRS/IRISA (Rennes, France) · LAAS, CNRS, (Toulouse, France)
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
Jun 1, 2026Michał Brzozowski, Neo Christopher ChungLLM Auditing Hallucination in Language Models
Samsung AI Center, Warsaw, Poland · University of Warsaw, Poland
Fingerprinting Inference Systems of Large Language Models
May 28, 2026Anna Wimbauer, Jonas Möller, Erik Imgrund +1LLM Inference Language Model Fingerprinting
BIFOLD & TU Berlin
KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing
May 28, 2026Yijia Fang, Yiqing Feng, Bingyu Li +1LLM Auditing Language Model Fingerprinting
Beihang University China · Xidian University China · HKUST Hong Kong SAR, China
Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content
May 28, 2026Bing Liu, Shunping Wang, Yufan Zhu +5Data Provenance Language Model Fingerprinting
School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China · State Grid Henan Marketing Service Center, Henan, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China +3
Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing
May 25, 2026Michał Brzozowski, Zuzanna Dubanowska, Enrico Cassano +1Language Model Fingerprinting Privacy Leakage in Language Models
Samsung AI Center, Warsaw, Poland · University of Turin, Italy · University of Warsaw, Poland
Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection
May 22, 2026Kieu Dang, Phung Lai, NhatHai Phan +2Text Watermarking Language Model Fingerprinting
1State University of New York at Albany · 2New Jersey Institute of Technology · 3Microsoft +1
Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis
May 20, 2026Juergen DietrichMulti-Agent LLM Systems Language Model Fingerprinting
Democracy Intelligence gGmbH, Berlin, Germany
Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
May 18, 2026Sixu Chen, Xiang Chen, Hongyao Yu +5Language Model Fingerprinting
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · South China University of Technology, Guangzhou, China · Harbin Institute of Technology, Shenzhen, Shenzhen, China
Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces
May 14, 2026William Lugoloobi, Samuelle Marro, Jabez Magomere +2Language Model Fingerprinting LLM Agent Security
Oxford Internet Institute, University of Oxford · Department of Engineering Science, University of Oxford
TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection
May 12, 2026Tom Sander, Hongyan Chang, Tomáš Souček +10Language Model Fingerprinting LLM Watermarking
FAIR, Meta Superintelligence Labs
Visual Fingerprints for LLM Generation Comparison
May 7, 2026Amal Alnouri, Andreas Hinterreiter, Christina Humer +2LLM Evaluation Language Model Fingerprinting
Johannes Kepler University Linz · ETH Zürich · Independent Researcher
The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive
Apr 28, 2026Alex Bogdan, Adrian de Valois-FranklinLLM Evaluation Language Model Generation Evaluation
Evolutionairy AI Toronto, Canada
Semantic Needles in Document Haystacks: Sensitivity Testing of LLM-as-a-Judge Similarity Scoring
Apr 20, 2026Sinan G. Aksoy, Alexandra A. Sabrio, Erik VonKaenel +1Semantic Textual Similarity LLM-as-a-Judge
Pacific Northwest National Laboratory · Washington University in St. Louis · Humana Inc.
ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
Apr 20, 2026Zirui Wang, Yusen Hou, Shaofeng Liang +4LLM Auditing Language Model Fingerprinting
The Hong Kong University of Science and Technology (Guangzhou) · Deep Interdisciplinary Intelligence Lab (DI2Lab)
SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models
Apr 18, 2026Yifei Zhao, Qian Lou, Mengxin ZhengVLM Robustness Language Model Fingerprinting
University of Central Florida
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
Mar 30, 2026Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko +1OOD Generalization Language Model Fingerprinting
School of Computing and Information Systems, The University of Melbourne, Australia · School of Computing, FSE, Macquarie University, Australia
Interpretable Predictability-Based AI Text Detection: A Replication Study
Mar 16, 2026Adam Skurla, Dominik Macko, Jakub SimkoAI-Generated Text Detection Language Model Fingerprinting
Faculty of Information Technology, Brno University of Technology, Brno, Czechia · Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia
From Construction to Injection: Edit-Based Fingerprints for Large Language Models
Sep 3, 2025Yue Li, Xin Yi, Dongsheng Shi +3Language Model Fingerprinting LLM Security
East China Normal University · Hasso Plattner Institute/University of Potsdam
RAFP: Identifying LLM Lineages via Rare-Region Fingerprints
May 19, 2025Yun-Yun Tsai, Jia Hao Liang, Chuan Guo +2Language Model Fingerprinting Language Model Robustness
Columbia University / Meta · New York / Menlo Park, USA · MIT +5
The Challenge of Identifying the Origin of Black-Box Large Language Models
Mar 6, 2025Ziqing Yang, Yixin Wu, Yun Shen +3Language Model Fingerprinting LLM Security
CISPA Helmholtz Center for Information Security · NetApp · TikTok Inc.
Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
Jul 15, 2024Mark Russinovich, Yanan Cai, Ahmed SalemLanguage Model Fingerprinting
Microsoft

Jun 4, 2026, cs.CLJ/K move · Enter open · S save

When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer

Zhen Sun, Yifan Liao, Zhicong Huang, Jiaheng Wei+3

Wuhan University · Ant Group · The Hong Kong University of Science and Technology (Guangzhou)+1

Class-Incremental Learning AI-Generated Text Detection Language Model Fingerprinting Ridge Regression

Open paper AskPDF arXiv

Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and misuse investigation. As new large language models continue to emerge, attribution models must continuously incorporate new generators while preserving their ability to recognize previously seen ones. Prior works have shown that this lifelong MGT attribution setting is challenging, and existing methods often struggle to achieve a stable balance between adapting to new classes and retaining old ones. To address this issue, we propose RidgeFT, a lightweight analytic update framework that does not rely on exemplar replay. RidgeFT trains a task-aware encoder on the initial generator set, stores compact class-wise sufficient statistics when each generator class is first observed, and then freezes the encoder for replay-free closed-form updates. It then suppresses generator-irrelevant variation through covariance calibration, improves representation capacity with fixed random features, and updates new classes through closed-form ridge regression based on class-level sufficient statistics. Across multi-topic evaluations with varying initial generator setups, RidgeFT consistently outperforms baselines. It achieves the best macro-F1 across domains, backbones, and incremental protocols, while also improving both old-class retention and new-class adaptation. These results suggest that feature-stable analytic updates provide a simple yet effective approach to lifelong MGT attribution.

Momentum

Related topics

Latest papers 54