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arxiv: 2507.03014 · v2 · submitted 2025-07-02 · 💻 cs.CR · cs.CL· cs.LG

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!

Do-hyeon Yoon , Minsoo Chun , Thomas Allen , Hans M\"uller , Min Wang , Rajesh Sharma This is my paper

Pith reviewed 2026-05-19 06:35 UTC · model grok-4.3

classification 💻 cs.CR cs.CLcs.LG
keywords LLM fingerprintingmodel lineage detectionattention parametersstandard deviationcopyright protectioncontinued trainingmodel plagiarism
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The pith

Standard deviation distributions of attention parameters in LLMs form stable fingerprints that identify model lineage even after continued training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the standard deviation distributions of attention parameter matrices across layers in large language models display distinctive patterns. These patterns remain stable despite extensive continued training on the models. Consequently, the distributions function as robust fingerprints for identifying a model's development lineage. This enables detection of potential model plagiarism or unauthorized reuse in cases where continued training is used to mask origins. Validation across multiple families supports the method's effectiveness for model authentication.

Core claim

The standard deviation distributions of attention parameter matrices across different layers exhibit distinctive patterns that remain stable even after extensive continued training. These parameter distribution signatures serve as robust fingerprints that can reliably identify model lineage and detect potential copyright infringement.

What carries the argument

Standard deviation distributions of attention parameter matrices across different layers that remain stable and distinctive.

If this is right

  • Model lineage can be identified reliably despite continued training.
  • Copyright infringement can be detected by matching these distribution patterns.
  • Continued training alone cannot fully obscure a model's original characteristics.
  • Model families can be authenticated through comparison of attention parameter statistics.
  • Specific instances of model derivation can be uncovered in released models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach might extend to analyzing other types of model parameters for additional fingerprints.
  • If the patterns are tied to model architecture, they could help categorize new models without known lineage.
  • Developers could attempt to modify distributions to evade detection, pointing to the need for multiple complementary methods.

Load-bearing premise

The stability and distinctiveness of these standard deviation distributions come from intrinsic model characteristics rather than shared training data or replicable factors.

What would settle it

Train an independent model from scratch with the same architecture and data distribution, then compare its attention parameter standard deviation distributions to those of the suspected source model.

Figures

Figures reproduced from arXiv: 2507.03014 by Do-hyeon Yoon, Hans M\"uller, Minsoo Chun, Min Wang, Rajesh Sharma, Thomas Allen.

Figure 1
Figure 1. Figure 1: Normalized standard deviation patterns of attention matrices (Q, K, V, O) across different model families. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Correlation matrices for three key models [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comprehensive correlation analysis across twelve models from various families. The heatmaps show that [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Attention parameter distribution comparison between Llama-3.1-70B and its fine-tuned derivative Llama [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Validation analysis of models derived from Qwen2.5-7B through different fine-tuning approaches [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of Qwen1.5-MoE-A2.7B, which was created by upcycling Qwen-1.8B into a mixture-of-experts [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Feed-forward network parameter distribution comparison between two Qwen family models (Qwen2.5- [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Feed-forward network parameter distribution comparison between Pangu and Qwen2.5-14B. The remark [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse becomes prevalent. While watermarking techniques have been proposed to protect model ownership, they may not be robust to continue training and development, posing serious threats to model attribution and copyright protection. This work introduces a simple yet effective approach for robust LLM fingerprinting based on intrinsic model characteristics. We discover that the standard deviation distributions of attention parameter matrices across different layers exhibit distinctive patterns that remain stable even after extensive continued training. These parameter distribution signatures serve as robust fingerprints that can reliably identify model lineage and detect potential copyright infringement. Our experimental validation across multiple model families demonstrates the effectiveness of our method for model authentication. Notably, our investigation uncovers evidence that a recently Pangu Pro MoE model released by Huawei is derived from Qwen-2.5 14B model through upcycling techniques rather than training from scratch, highlighting potential cases of model plagiarism, copyright violation, and information fabrication. These findings underscore the critical importance of developing robust fingerprinting methods for protecting intellectual property in large-scale model development and emphasize that deliberate continued training alone is insufficient to completely obscure model origins.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims that the per-layer standard deviation distributions of attention weight matrices in LLMs form stable, distinctive intrinsic fingerprints that persist under continued training. These signatures are presented as a practical method for model lineage attribution and copyright infringement detection. The authors report experimental validation across multiple model families and specifically conclude that Huawei's Pangu Pro MoE was derived from Qwen-2.5 14B via upcycling rather than independent training from scratch.

Significance. If the stability and uniqueness claims hold after proper controls, the method could offer a lightweight, training-robust tool for IP protection in the LLM ecosystem, where upcycling and continued training are common. The concrete case study on Pangu and Qwen adds immediate relevance to ongoing debates about model provenance. The approach is simple and does not require additional watermarking infrastructure, which is a practical strength.

major comments (2)
  1. [Experimental validation] The experimental validation section does not report controls that isolate lineage from shared architecture, optimizer family, or pre-training corpus overlap. Without independent runs using identical hyperparameters and data distributions but different random seeds, it remains possible that the observed std-dev profile matches between Qwen-2.5 and Pangu Pro MoE arise from replicable training choices rather than direct derivation.
  2. [Abstract and results] The abstract asserts that the standard deviation distributions 'remain stable even after extensive continued training,' yet no quantitative metrics (e.g., Wasserstein distance or KL divergence between pre- and post-continued-training distributions), training token counts, or statistical significance tests are referenced in the provided description of results. This weakens the load-bearing claim that continued training alone cannot erase the fingerprint.
minor comments (2)
  1. [Method] Notation for the per-layer standard deviation statistic should be defined explicitly (e.g., as a vector or histogram) to avoid ambiguity when comparing across model scales.
  2. [Title] The title's phrasing 'Continue Training is NOT All You Need' could be clarified to specify that the claim applies specifically to attention-parameter std-dev signatures rather than all possible fingerprinting approaches.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their thorough review and valuable suggestions. We respond to each major comment in detail below, indicating where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: The experimental validation section does not report controls that isolate lineage from shared architecture, optimizer family, or pre-training corpus overlap. Without independent runs using identical hyperparameters and data distributions but different random seeds, it remains possible that the observed std-dev profile matches between Qwen-2.5 and Pangu Pro MoE arise from replicable training choices rather than direct derivation.

    Authors: We thank the referee for highlighting this important aspect of experimental design. Our validation demonstrates that the standard deviation distributions are distinctive across different model families and remain consistent under continued training, supporting their use as fingerprints. To further isolate lineage effects, we will expand the experimental section in the revision to include discussions of potential shared factors such as architecture and optimizer. We will also report additional comparisons with models that share partial training characteristics. However, conducting full-scale independent pre-training experiments with matched data and hyperparameters but varied seeds is beyond our current computational resources. We believe the existing evidence, combined with the specific match observed for Pangu Pro MoE, still provides strong support for the derivation claim, though we will temper the language to reflect the limitations. revision: partial

  2. Referee: The abstract asserts that the standard deviation distributions 'remain stable even after extensive continued training,' yet no quantitative metrics (e.g., Wasserstein distance or KL divergence between pre- and post-continued-training distributions), training token counts, or statistical significance tests are referenced in the provided description of results. This weakens the load-bearing claim that continued training alone cannot erase the fingerprint.

    Authors: We agree that incorporating quantitative metrics would enhance the rigor of our claims. In the revised manuscript, we will update the abstract and results section to include specific quantitative measures, such as the Wasserstein distance and KL divergence between the distributions before and after continued training. We will also provide details on the number of tokens used in the continued training experiments and include appropriate statistical tests to confirm the stability of the fingerprints. These changes will directly address the concern and strengthen the evidence that continued training does not erase the intrinsic fingerprint. revision: yes

Circularity Check

0 steps flagged

Empirical observation of stable attention std-dev patterns is self-contained with no reduction to fitted inputs or self-citations

full rationale

The paper presents its core contribution as an empirical discovery: standard deviation distributions of attention parameter matrices across layers are observed to be distinctive and stable under continued training. This is validated experimentally across model families rather than derived from equations or prior self-citations. No load-bearing step reduces by construction to a fitted parameter renamed as prediction, a self-definitional loop, or an imported uniqueness theorem. The attribution to model lineage (e.g., Pangu from Qwen) rests on cross-family comparisons, which the paper treats as external evidence rather than tautological. The approach is therefore self-contained against external benchmarks and receives the default non-finding for an observational study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim rests primarily on an untested domain assumption about the intrinsic stability of these distributions; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Standard deviation distributions of attention matrices are intrinsic to model lineage and remain stable under continued training regardless of other factors.
    Invoked to support the fingerprint robustness claim in the abstract.

pith-pipeline@v0.9.0 · 5758 in / 1245 out tokens · 44782 ms · 2026-05-19T06:35:30.536849+00:00 · methodology

discussion (0)

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From

    cs.CR 2025-09 unverdicted novelty 7.0

    SeedPrints fingerprints LLMs using persistent biases from initialization seeds for lineage verification across pretraining and adaptation stages.

  2. Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends

    cs.CR 2025-08 accept novelty 7.0

    A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · cited by 2 Pith papers · 6 internal anchors

  1. [1]

    Fuyu-8b: A multimodal architecture for ai agents, October 2023.https://www.adept.ai/blog/fuyu-8b/

    Llama-nemotron: Efficient reasoning models. arXiv preprint arXiv:2505.00949. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al

    work page arXiv

  2. [2]

    The Llama 3 Herd of Models

    The llama 3 herd of mod- els. arXiv preprint arXiv:2407.21783. Tingxu Han, Shenghan Huang, Ziqi Ding, Weisong Sun, Yebo Feng, Chunrong Fang, Jun Li, Hanwei Qian, Cong Wu, Quanjun Zhang, et al

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    arXiv preprint arXiv:2403.03846

    On the effec- tiveness of distillation in mitigating backdoors in pre- trained encoder. arXiv preprint arXiv:2403.03846. K. He et al

    work page arXiv

  4. [4]

    arXiv preprint arXiv:2210.01234

    On the security and forensics of large language models. arXiv preprint arXiv:2210.01234. Jordan Hoffmann, Sebastian Borgeaud, Arthur Men- sch, Elena Buchatskaya, Trevor Cai, Eliza Ruther- ford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al

    work page arXiv

  5. [5]

    Training Compute-Optimal Large Language Models

    Train- ing compute-optimal large language models. arXiv preprint arXiv:2203.15556. John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Mifer, and Tom Goldstein

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    arXiv preprint arXiv:2301.10226 , year=

    A watermark for large language models. arXiv preprint arXiv:2301.10226. R. Kuditipudi et al

    work page arXiv

  7. [7]

    arXiv preprint arXiv:2306.01235

    The robustness of water- marks for large language models. arXiv preprint arXiv:2306.01235. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al

    work page arXiv

  8. [8]

    DeepSeek-V3 Technical Report

    Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. Lyu Lyu, Y . Li, H. Wang, Z. Zhang, T. Su, L. Sun, and B. Li

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pages 2413–2426

    Reading between the lines: Fingerprint- ing and identifying language models. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pages 2413–2426. Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, et al

    work page 2022

  10. [10]

    OLMoE: Open Mixture-of-Experts Language Models

    Olmoe: Open mixture-of-experts language models. arXiv preprint arXiv:2409.02060. V . S. Sadasivan, S. Kumar, S. Balasubramanian, and S. Feizi

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    arXiv preprint arXiv:2305.01236

    Can we trust your explanations? on the robustness of watermarked explanations. arXiv preprint arXiv:2305.01236. Pamela Samuelson

    work page arXiv

  12. [12]

    arXiv preprint arXiv:2405.02466

    Proflingo: a fingerprinting-based intellectual property protection scheme for large lan- guage models. arXiv preprint arXiv:2405.02466. Yehui Tang, Xiaosong Li, Fangcheng Liu, Wei Guo, Hang Zhou, Yaoyuan Wang, Kai Han, Xianzhi Yu, Jinpeng Li, Hui Zang, et al

    work page arXiv

  13. [13]

    arXiv preprint arXiv:2505.21411

    Pangu pro moe: Mixture of grouped experts for efficient sparsity. arXiv preprint arXiv:2505.21411. Qwen Team

    work page arXiv

  14. [14]

    Qwen2.5 Technical Report

    Qwen2 technical report. arXiv preprint arXiv:2412.15115. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023a. Llama: Open and effi- cient foundation language models. arXiv preprint arXiv:2302.13971. Hugo Touvron, Louis Martin, Kevin Sto...

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    Qwen3 Technical Report

    Qwen3 technical report. arXiv preprint arXiv:2505.09388. Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang

    work page internal anchor Pith review Pith/arXiv arXiv