pith. sign in

arxiv: 2606.17464 · v1 · pith:K6UIH52Onew · submitted 2026-06-16 · 💻 cs.LG

CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models

Jeffrey G. Wang , Jason Wang , Marvin Li , Seth Neel This is my paper

Pith reviewed 2026-06-27 01:30 UTC · model grok-4.3

classification 💻 cs.LG
keywords membership inference attackslanguage modelsbenchmarksprivacy evaluationtraining checkpointsdistribution shift
0
0 comments X

The pith

Open-source language models with intermediate checkpoints can be converted into clean testbeds for membership inference attacks.

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

Prior evaluations of membership inference attacks on language models have been compromised by subtle distribution shifts between member and non-member data, allowing even blind methods to outperform published attacks. The paper establishes that training data before and after any fixed checkpoint during training come from the same distribution, which removes those shifts and turns any open-source model with public training data and checkpoints into a valid MIA benchmark. This framework is applied to evaluate half a dozen existing attacks on the Pythia and OLMo model families ranging from 70M to 7B parameters. A modular open-source library is released to support further attack design in this setting. A sympathetic reader would care because the method supplies statistically sound measurements of privacy leakage in large language models.

Core claim

By leveraging the fact that training data before and after a fixed point during training are drawn from the same distribution, all open-source models with intermediate checkpoints and public training data can be converted into MIA testbeds. The approach is demonstrated by re-evaluating published attacks on Pythia and OLMo models from 70M to 7B parameters, and a modular library is provided for implementing attacks under this protocol.

What carries the argument

The fixed training checkpoint split, which produces member and non-member sets drawn from the identical data distribution and thereby eliminates distribution-shift confounds.

If this is right

  • Any open-source LLM with public training data and checkpoints becomes a usable MIA testbed.
  • Existing attacks can be re-tested under distribution-matched conditions on models from 70M to 7B parameters.
  • A modular library allows researchers to implement and compare new attacks within the same clean evaluation setting.

Where Pith is reading between the lines

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

  • The same checkpoint-split idea could be applied to any training run that releases intermediate weights and data provenance, extending beyond the Pythia and OLMo families.
  • If attacks that previously succeeded only because of distribution shift now fail on these testbeds, the field would need to develop methods that detect membership without relying on distributional cues.
  • The benchmark could serve as a standard for auditing whether new privacy-preserving training techniques actually reduce membership leakage.

Load-bearing premise

Training data before and after a fixed point during training are drawn from the same distribution.

What would settle it

Statistical tests or a simple classifier showing that data before the checkpoint can be reliably distinguished from data after the checkpoint on any of the tested models.

Figures

Figures reproduced from arXiv: 2606.17464 by Jason Wang, Jeffrey G. Wang, Marvin Li, Seth Neel.

Figure 1
Figure 1. Figure 1: The shuffling of packed samples to construct [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties. Although several attempts have been made to evaluate MIAs on language models, the extant literature has suffered numerous difficulties in constructing clean evaluations to test new techniques. In particular, subtle distribution shifts between member and non-member sets can undermine the statistical validity of MIAs; recent work has underscored this by showing that "blind" methods with no access to the underlying model can perform far better than published methods on the same benchmarks. This paper constructs a benchmark for principled evaluation of MIAs against LLMs, by leveraging the insight that training data before and after a fixed point during training are drawn from the same distribution. Therefore, all open-source models with intermediate checkpoints and public training data can be converted into MIA testbeds. We apply our framework to a half-dozen published attacks on the Pythia and OLMo family of models, from 70M to 7B parameters. To facilitate further privacy research, we open-source a modular library for designing and implementing attacks in this setting: https://github.com/safr-ai-lab/pandora_llm.

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 / 1 minor

Summary. The paper claims to provide firm foundations for membership inference attacks (MIAs) on language models via CheckMIABench. It converts open-source LLMs with intermediate checkpoints and public training data (Pythia and OLMo families, 70M–7B) into MIA testbeds by treating data before a fixed training checkpoint as members and after as non-members, under the assumption that both sets are drawn from the same distribution. This framework is used to evaluate a half-dozen published attacks, and a modular open-source library is released.

Significance. If the distributional assumption holds, the work is significant because it directly targets the distribution-shift confound that has invalidated prior MIA benchmarks on LLMs (where blind methods have outperformed model-based ones). The open-sourcing of the library and the conversion of existing public checkpoints into reusable testbeds are concrete strengths that would enable reproducible, confound-controlled privacy research.

major comments (2)
  1. [Abstract] Abstract: the claim that 'training data before and after a fixed point during training are drawn from the same distribution' is load-bearing for the validity of the entire benchmark, yet no stationarity check, token-statistic comparison, or domain-shift analysis is reported on the actual Pythia or OLMo training streams.
  2. [Abstract] Abstract: the assertion that 'all open-source models with intermediate checkpoints and public training data can be converted into MIA testbeds' is presented without discussion of how the fixed-point choice must be validated to ensure the member/non-member sets remain exchangeable; this condition is required for the framework to generalize beyond the two model families evaluated.
minor comments (1)
  1. The abstract refers to 'a half-dozen published attacks' without naming them or citing the specific papers; adding the list of evaluated attacks would improve immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments both concern the load-bearing distributional assumption and its generalization; we address each below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'training data before and after a fixed point during training are drawn from the same distribution' is load-bearing for the validity of the entire benchmark, yet no stationarity check, token-statistic comparison, or domain-shift analysis is reported on the actual Pythia or OLMo training streams.

    Authors: We agree that explicit empirical validation of the stationarity assumption would strengthen the paper. The original manuscript relied on the fact that both pre- and post-checkpoint data are drawn from the same public training corpus without documented domain shifts, but did not report quantitative checks. In the revision we will add a new subsection (likely in Section 3 or 4) containing token-level statistics (vocabulary overlap, average length, n-gram frequency divergence), perplexity comparisons under a held-out model, and simple domain-shift probes on the actual Pythia and OLMo streams. These results will be reported for the checkpoints used in the experiments. revision: yes

  2. Referee: [Abstract] Abstract: the assertion that 'all open-source models with intermediate checkpoints and public training data can be converted into MIA testbeds' is presented without discussion of how the fixed-point choice must be validated to ensure the member/non-member sets remain exchangeable; this condition is required for the framework to generalize beyond the two model families evaluated.

    Authors: We accept the point that the generalization statement requires accompanying methodological guidance. The revised manuscript will expand the framework description (Section 3) with a short subsection on fixed-point selection and validation. It will explicitly state that users must verify exchangeability via statistical tests or feature-distribution comparisons before treating a new model as a testbed, and will qualify the original claim to apply only when such validation succeeds. This will also include practical recommendations drawn from the Pythia/OLMo experience. revision: yes

Circularity Check

0 steps flagged

No circularity; framework built on explicit distributional assumption without self-referential reduction

full rationale

The paper constructs its MIA benchmark by directly positing that training data before and after a checkpoint share the same distribution, then applies this to convert released models into testbeds. No equations, fitted parameters, or self-citations are shown reducing the central claim to its own inputs by construction. The derivation proceeds from the stated premise in a self-contained way, with no evidence of self-definitional loops, renamed predictions, or load-bearing self-citations that collapse the result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on one domain assumption about training data distributions; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption training data before and after a fixed point during training are drawn from the same distribution
    This premise is invoked to guarantee that member and non-member sets have no distribution shift.

pith-pipeline@v0.9.1-grok · 5737 in / 1235 out tokens · 31132 ms · 2026-06-27T01:30:22.479637+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

300 extracted references · 46 canonical work pages

  1. [1]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  2. [2]

    2406.15968 , archivePrefix=

    Roy Xie and Junlin Wang and Ruomin Huang and Minxing Zhang and Rong Ge and Jian Pei and Neil Zhenqiang Gong and Bhuwan Dhingra , year=. 2406.15968 , archivePrefix=

    arXiv

  3. [3]

    2021 , eprint=

    Extracting Training Data from Large Language Models , author=. 2021 , eprint=

    2021

  4. [4]

    Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J

    DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang...

    Pith/arXiv arXiv

  5. [5]

    2024 , eprint=

    The Llama 3 Herd of Models , author=. 2024 , eprint=

    2024

  6. [6]

    2024 , eprint=

    Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data? , author=. 2024 , eprint=

    2024

  7. [7]

    2013 , eprint=

    Efficient Estimation of Word Representations in Vector Space , author=. 2013 , eprint=

    2013

  8. [8]

    2024 , eprint=

    Probing Language Models for Pre-training Data Detection , author=. 2024 , eprint=

    2024

  9. [9]

    Dirk Groeneveld and Iz Beltagy and Pete Walsh and Akshita Bhagia and Rodney Kinney and Oyvind Tafjord and Ananya Harsh Jha and Hamish Ivison and Ian Magnusson and Yizhong Wang and Shane Arora and David Atkinson and Russell Authur and Khyathi Raghavi Chandu and Arman Cohan and Jennifer Dumas and Yanai Elazar and Yuling Gu and Jack Hessel and Tushar Khot an...

    Pith/arXiv arXiv

  10. [10]

    2023 , eprint=

    In-Context Unlearning: Language Models as Few Shot Unlearners , author=. 2023 , eprint=

    2023

  11. [11]

    2024 , eprint=

    Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy , author=. 2024 , eprint=

    2024

  12. [12]

    2024 , eprint=

    Do Membership Inference Attacks Work on Large Language Models? , author=. 2024 , eprint=

    2024

  13. [13]

    arXiv preprint arXiv:2005.10881 , year=

    Revisiting membership inference under realistic assumptions , author=. arXiv preprint arXiv:2005.10881 , year=

    arXiv 2005

  14. [14]

    Zhang, Jingyang and Sun, Jingwei and Yeats, Eric and Ouyang, Yang and Kuo, Martin and Zhang, Jianyi and Yang, Hao and Li, Hai , journal=

  15. [15]

    25th USENIX security symposium (USENIX Security 16) , pages=

    Stealing machine learning models via prediction \ APIs \ , author=. 25th USENIX security symposium (USENIX Security 16) , pages=

  16. [16]

    Chang , title =

    Seth Neel and Peter W. Chang , title =. CoRR , volume =. 2023 , url =. doi:10.48550/ARXIV.2312.06717 , eprinttype =. 2312.06717 , timestamp =

    work page doi:10.48550/arxiv.2312.06717 2023

  17. [17]

    2023 , eprint=

    Training Data Extraction From Pre-trained Language Models: A Survey , author=. 2023 , eprint=

    2023

  18. [18]

    M o P e: Model Perturbation based Privacy Attacks on Language Models

    Li, Marvin and Wang, Jason and Wang, Jeffrey and Neel, Seth. M o P e: Model Perturbation based Privacy Attacks on Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.842

    work page doi:10.18653/v1/2023.emnlp-main.842 2023

  19. [19]

    Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten , url =

    Eduard Fosch Villaronga and Peter Kieseberg and Tiffany Li , doi =. Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten , url =. Computer Law & Security Review , keywords =. 2018 , Bdsk-Url-1 =

    2018

  20. [20]

    doi:10.1017/err.2023.59 , journal=

    Lucchi, Nicola , year=. doi:10.1017/err.2023.59 , journal=

    work page doi:10.1017/err.2023.59 2023

  21. [21]

    Anthropic , author=

    Core views on AI safety: When, why, what, and how , url=. Anthropic , author=. 2023 , month=

    2023

  22. [22]

    The White House , publisher=

    Biden, Jr., Joeseph Robinette , title=. The White House , publisher=. 2023 , month=

    2023

  23. [23]

    A Call for Clarity in Reporting BLEU Scores

    Post, Matt. A Call for Clarity in Reporting BLEU Scores. Proceedings of the Third Conference on Machine Translation: Research Papers. 2018. doi:10.18653/v1/W18-6319

    work page doi:10.18653/v1/w18-6319 2018

  24. [24]

    ArXiv , year=

    On the Opportunities and Risks of Foundation Models , author=. ArXiv , year=

  25. [25]

    2022 , eprint=

    Provable Membership Inference Privacy , author=. 2022 , eprint=

    2022

  26. [26]

    2020 , eprint=

    Understanding Unintended Memorization in Federated Learning , author=. 2020 , eprint=

    2020

  27. [27]

    2021 , eprint=

    How BPE Affects Memorization in Transformers , author=. 2021 , eprint=

    2021

  28. [28]

    Deduplicating Training Data Makes Language Models Better

    Lee, Katherine and Ippolito, Daphne and Nystrom, Andrew and Zhang, Chiyuan and Eck, Douglas and Callison-Burch, Chris and Carlini, Nicholas. Deduplicating Training Data Makes Language Models Better. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022. doi:10.18653/v1/2022.acl-long.577

    work page doi:10.18653/v1/2022.acl-long.577 2022

  29. [29]

    2021 , eprint=

    Counterfactual Memorization in Neural Language Models , author=. 2021 , eprint=

    2021

  30. [30]

    2023 , eprint=

    Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy , author=. 2023 , eprint=

    2023

  31. [31]

    2023 , eprint=

    Quantifying Memorization Across Neural Language Models , author=. 2023 , eprint=

    2023

  32. [32]

    AI Differential Privacy and Federated Learning , url=

    Ippolito, Pier Paolo , year=. AI Differential Privacy and Federated Learning , url=. Medium , publisher=

  33. [33]

    2022 , eprint=

    Membership Inference Attacks From First Principles , author=. 2022 , eprint=

    2022

  34. [34]

    2017 , eprint=

    Understanding deep learning requires rethinking generalization , author=. 2017 , eprint=

    2017

  35. [35]

    2023 , eprint=

    Emergent and Predictable Memorization in Large Language Models , author=. 2023 , eprint=

    2023

  36. [36]

    2022 , eprint=

    Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models , author=. 2022 , eprint=

    2022

  37. [37]

    2023 , eprint=

    Measuring Forgetting of Memorized Training Examples , author=. 2023 , eprint=

    2023

  38. [38]

    2020 , eprint =

    Extracting Training Data from Large Language Models , author =. 2020 , eprint =

    2020

  39. [39]

    2020 , eprint=

    Systematic Evaluation of Privacy Risks of Machine Learning Models , author=. 2020 , eprint=

    2020

  40. [40]

    2022 , eprint=

    Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model , author=. 2022 , eprint=

    2022

  41. [41]

    2022 , eprint=

    Scaling Language Models: Methods, Analysis & Insights from Training Gopher , author=. 2022 , eprint=

    2022

  42. [42]

    2022 , eprint=

    PaLM: Scaling Language Modeling with Pathways , author=. 2022 , eprint=

    2022

  43. [43]

    2019 , eprint=

    The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , author=. 2019 , eprint=

    2019

  44. [44]

    Comparison of overfitting and overtraining , author=

    Neural network studies, 1. Comparison of overfitting and overtraining , author=. J. Chem. Inf. Comput. Sci. , year=

  45. [45]

    Workshop on Time-Delay Systems , year=

    Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks , author=. Workshop on Time-Delay Systems , year=

  46. [46]

    2020 , eprint=

    Training Production Language Models without Memorizing User Data , author=. 2020 , eprint=

    2020

  47. [47]

    2017 , eprint=

    Membership Inference Attacks against Machine Learning Models , author=. 2017 , eprint=

    2017

  48. [48]

    Kaggle , author=

    Stack overflow data , url=. Kaggle , author=. 2019 , month=

    2019

  49. [49]

    2023 , eprint=

    Bag of Tricks for Training Data Extraction from Language Models , author=. 2023 , eprint=

    2023

  50. [50]

    2022 , eprint=

    How to Combine Membership-Inference Attacks on Multiple Updated Models , author=. 2022 , eprint=

    2022

  51. [51]

    2019 , eprint=

    Auditing Data Provenance in Text-Generation Models , author=. 2019 , eprint=

    2019

  52. [52]

    2022 , eprint=

    Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks , author=. 2022 , eprint=

    2022

  53. [53]

    2022 , eprint=

    Membership Inference Attacks on Machine Learning: A Survey , author=. 2022 , eprint=

    2022

  54. [54]

    2022 , eprint=

    Deduplicating Training Data Mitigates Privacy Risks in Language Models , author=. 2022 , eprint=

    2022

  55. [55]

    doi:10.1162/tacl_a_00299 , url =

    Sorami Hisamoto and Matt Post and Kevin Duh , title =. doi:10.1162/tacl_a_00299 , url =

    work page doi:10.1162/tacl_a_00299

  56. [56]

    2021 , eprint=

    How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN , author=. 2021 , eprint=

    2021

  57. [57]

    2018 , eprint=

    Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , author=. 2018 , eprint=

    2018

  58. [58]

    2022 , eprint=

    Large Language Models Can Be Strong Differentially Private Learners , author=. 2022 , eprint=

    2022

  59. [59]

    2022 , eprint=

    Differentially Private Decoding in Large Language Models , author=. 2022 , eprint=

    2022

  60. [60]

    2022 , eprint=

    If Influence Functions are the Answer, Then What is the Question? , author=. 2022 , eprint=

    2022

  61. [61]

    2022 , eprint=

    Privacy Adhering Machine Un-learning in NLP , author=. 2022 , eprint=

    2022

  62. [62]

    Knowledge Unlearning for Mitigating Privacy Risks in Language Models

    Jang, Joel and Yoon, Dongkeun and Yang, Sohee and Cha, Sungmin and Lee, Moontae and Logeswaran, Lajanugen and Seo, Minjoon. Knowledge Unlearning for Mitigating Privacy Risks in Language Models. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023. doi:10.18653/v1/2023.acl-long.805

    work page doi:10.18653/v1/2023.acl-long.805 2023

  63. [63]

    2020 , eprint=

    Machine Unlearning , author=. 2020 , eprint=

    2020

  64. [64]

    2020 , eprint=

    Descent-to-Delete: Gradient-Based Methods for Machine Unlearning , author=. 2020 , eprint=

    2020

  65. [65]

    2021 , eprint=

    Remember What You Want to Forget: Algorithms for Machine Unlearning , author=. 2021 , eprint=

    2021

  66. [66]

    2023 , eprint=

    A Watermark for Large Language Models , author=. 2023 , eprint=

    2023

  67. [67]

    2023 , eprint=

    DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature , author=. 2023 , eprint=

    2023

  68. [68]

    2023 , eprint=

    Can AI-Generated Text be Reliably Detected? , author=. 2023 , eprint=

    2023

  69. [69]

    2023 , eprint=

    Provable Copyright Protection for Generative Models , author=. 2023 , eprint=

    2023

  70. [70]

    Journal of Data Privacy and Protection , author=

    Understanding the scope and impact of the California Consumer Privacy Act of 2018 , volume=. Journal of Data Privacy and Protection , author=. 2019 , month=

    2018

  71. [71]

    General Data Protection Regulation (GDPR) , year=

  72. [72]

    The EU Proposal for a General Data Protection Regulation and the roots of the ‘right to be forgotten’ , journal =

    Alessandro Mantelero , keywords =. The EU Proposal for a General Data Protection Regulation and the roots of the ‘right to be forgotten’ , journal =. 2013 , issn =. doi:https://doi.org/10.1016/j.clsr.2013.03.010 , url =

    work page doi:10.1016/j.clsr.2013.03.010 2013

  73. [73]

    EU General Data Protection Regulation (GDPR): A practical guide , publisher=

    Voigt, Paul and von dem Bussche, Axel , year=. EU General Data Protection Regulation (GDPR): A practical guide , publisher=

  74. [74]

    CHATGPT banned in Italy over privacy concerns , url=

    McCallum, Shiona , year=. CHATGPT banned in Italy over privacy concerns , url=. BBC News , publisher=

  75. [75]

    This artist is dominating AI-generated art

    Heikkila, Melissa , year=. This artist is dominating AI-generated art. and he’s not happy about it. , url=. MIT Technology Review , publisher=

  76. [76]

    What does GPT-3 “know” about me? , url=

    Heikkila, Melissa , year=. What does GPT-3 “know” about me? , url=. MIT Technology Review , publisher=

  77. [77]

    I cloned myself with AI

    Stern, Joanna , year=. I cloned myself with AI. she fooled my bank and my family. , url=. The Wall Street Journal , publisher=

  78. [78]

    2018 , eprint=

    D\'ej\`a Vu: an empirical evaluation of the memorization properties of ConvNets , author=. 2018 , eprint=

    2018

  79. [79]

    2017 , eprint=

    Ethical Challenges in Data-Driven Dialogue Systems , author=. 2017 , eprint=

    2017

  80. [80]

    Analyzing Information Leakage of Updates to Natural Language Models , booktitle =

    Santiago Zanella-B. Analyzing Information Leakage of Updates to Natural Language Models , booktitle =. doi:10.1145/3372297.3417880 , url =

    work page doi:10.1145/3372297.3417880

Showing first 80 references.