mlsanityData to 7 Oct 2026

Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

arXiv 2409.19798 ↗First posted 29 Sep 2024cs.LG cs.CRcited by 49

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

We consider the problem of a training data proof, where a data creator or owner wants to demonstrate to a third party that some machine learning model was trained on their data. Training data proofs play a key role in recent lawsuits against foundation models trained on web-scale data. Many prior works suggest to instantiate training data proofs using membership inference attacks. We argue that this approach is fundamentally unsound: to provide convincing evidence, the data creator needs to demonstrate that their attack has a low false positive rate, i.e., that the attack's output is unlikely under the null hypothesis that the model was not trained on the target data. Yet, sampling from this null hypothesis is impossible, as we do not know the exact contents of the training set, nor can we (efficiently) retrain a large foundation model. We conclude by offering two paths forward, by showing that data extraction attacks and membership inference on special canary data can be used to create sound training data proofs.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 34 of its 49 references that are in the map. First posted on arXiv 29 Sep 2024.

Builds on

34 of 49 references.

Closest ancestors · 9

Same direction, newest first

  1. 2024Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction12 Aug
  2. 2024Blind Baselines Beat Membership Inference Attacks for Foundation Models23 Jun
  3. 2024LLM Dataset Inference: Did you train on my dataset?10 Jun
  4. 2024Do Membership Inference Attacks Work on Large Language Models?12 Feb
  5. 2023Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models23 Oct
  6. 2023Are Diffusion Models Vulnerable to Membership Inference Attacks?2 Feb
  7. 2021Membership Inference Attacks From First Principles7 Dec
  8. 2021Enhanced Membership Inference Attacks against Machine Learning Models18 Nov
  9. 2021Dataset Inference: Ownership Resolution in Machine Learning21 Apr

Other references · 25

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2020Extracting Training Data from Large Language Modelscited by 2,144
  5. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  6. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  7. 2023Large Language Modelscited by 774
  8. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  9. 2023Extracting Training Data from Diffusion Modelscited by 723
  10. 2023A Watermark for Large Language Modelscited by 667
  11. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 415
  12. 2023Detecting Pretraining Data from Large Language Modelscited by 375
13 more
  1. 2013The Composition Theorem for Differential Privacycited by 290
  2. 2020Auditing Differentially Private Machine Learning: How Private is Private SGD?cited by 213
  3. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 194
  4. 2021Adversary Instantiation: Lower Bounds for Differentially Private Machine Learningcited by 193
  5. 2023Privacy Auditing with One (1) Training Runcited by 119
  6. 2024Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Modelscited by 102
  7. 2024DE-COP: Detecting Copyrighted Content in Language Models Training Datacited by 72
  8. 2024Watermarking Makes Language Models Radioactivecited by 52
  9. 2024Dated Data: Tracing Knowledge Cutoffs in Large Language Modelscited by 48
  10. 2024Evaluating Copyright Takedown Methods for Language Modelscited by 42
  11. 2024Copyright Traps for Large Language Modelscited by 36
  12. 2024Evaluations of Machine Learning Privacy Defenses are Misleadingcited by 35
  13. 2022No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"cited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 29; 2026: 15.

49 papers.

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  1. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  2. 2024Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Modelscited by 21
  3. 2025Language Models May Verbatim Complete Text They Were Not Explicitly Trained Oncited by 20
  4. 2025Privacy Attacks on Image AutoRegressive Modelscited by 15
  5. 2025Position: Adversarial ML for LLMs Is Not Making Any Progresscited by 14
  6. 2025SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attackscited by 9
  7. 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigationcited by 9
  8. 2024Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoningcited by 8
  9. 2024LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM statescited by 8
  10. 2025STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasingscited by 8
  11. 2025Unlocking Post-hoc Dataset Inference with Synthetic Datacited by 7
  12. 2025The Surprising Effectiveness of Membership Inference with Simple N-Gram Coveragecited by 7
37 more
  1. 2025From ASR to ASP: Evaluating Prompt Attack Vulnerabilities Against Open-Source LLMscited by 6
  2. 2025Membership Inference Attacks on Sequence Modelscited by 6
  3. 2025Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoningcited by 6
  4. 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
  5. 2025Train Once, Answer All: Many Pretraining Experiments for the Cost of Onecited by 6
  6. 2024A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Modelscited by 5
  7. 2025Practical Bayes-Optimal Membership Inference Attackscited by 5
  8. 2025The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Modelscited by 5
  9. 2025Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Modelscited by 4
  10. 2025Synthetic Data Can Mislead Evaluations: Membership Inference as Machine Text Detectioncited by 3
  11. 2025Beyond Public Access in LLM Pre-Training Datacited by 3
  12. 2025Membership Inference Attacks on Tokenizers of Large Language Modelscited by 3
  13. 2025Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Techniquecited by 3
  14. 2026Revisiting the LiRA Membership Inference Attack Under Realistic Assumptionscited by 3
  15. 2025What Really is a Member? Discrediting Membership Inference via Poisoningcited by 2
  16. 2026Causal Evaluation of Membership Inference Attackscited by 2
  17. 2026FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Modelscited by 2
  18. 2025DIS-CO: Discovering Copyrighted Content in VLMs Training Datacited by 1
  19. 2025Efficient Machine Unlearning by Model Splitting and Core Sample Selectioncited by 1
  20. 2025Membership Inference Attacks for Unseen Classescited by 1
  21. 2025Provable Training Data Identification for Large Language Modelscited by 1
  22. 2026Quantifying the Effect of Test Set Contamination on Generative Evaluationscited by 1
  23. 2026Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacycited by 1
  24. 2026A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Modelscited by 1
  25. 2026Probing Memorization of Tabular In-Context Learningcited by 1
  26. 2024Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attributioncited by 0
  27. 2025Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inferencecited by 0
  28. 2025Observational Auditing of Label Privacycited by 0
  29. 2025Descend or Rewind? Stochastic Gradient Descent Unlearningcited by 0
  30. 2026Private PoEtry: Private In-Context Learning via Product of Expertscited by 0
  31. 2026Learning to Detect Language Model Training Data via Active Reconstructioncited by 0
  32. 2026Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hardcited by 0
  33. 2026Validity Threats for Foundation Model Researchcited by 0
  34. 2026Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forgetcited by 0
  35. 2026Natural Identifiers for Privacy and Data Audits in Large Language Modelscited by 0
  36. 2026Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprintscited by 0
  37. 2026The Null Is the Hard Part: Exact Tests for Memorization in Generative Modelscited by 0