mlsanityData to 14 Oct 2026

ProPILE: Probing Privacy Leakage in Large Language Models

arXiv 2307.01881 ↗First posted 4 Jul 2023cs.CR cs.CLcited by 129

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Cited by 36 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web.

Key facts. Cited by 129 papers in the map (data through 14 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 4 Jul 2023.

Builds on

21 of 44 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  7. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  8. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  9. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  10. 2020Extracting Training Data from Large Language Modelscited by 2,189
  11. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  12. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
9 more
  1. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  2. 2019Deep Leakage from Gradientscited by 1,021
  3. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  4. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  5. 2023Analyzing Leakage of Personally Identifiable Information in Language Modelscited by 285
  6. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 246
  7. 2022Are Large Pre-Trained Language Models Leaking Your Personal Information?cited by 200
  8. 2020A Survey of Privacy Attacks in Machine Learningcited by 146
  9. 2019Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?cited by 71

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 11; 2024: 44; 2025: 53; 2026: 21.

129 papers.

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  1. 2024TOFU: A Task of Fictitious Unlearning for LLMscited by 533
  2. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 212
  3. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 205
  4. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  5. 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
  6. 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 171
  7. 2024Large Language Model Unlearning via Embedding-Corrupted Promptscited by 116
  8. 2024On-Device Language Models: A Comprehensive Reviewcited by 104
  9. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  10. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 82
  11. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agentscited by 72
  12. 2025RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakagecited by 69
38 more
  1. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 66
  2. 2024Instruction Backdoor Attacks Against Customized LLMscited by 62
  3. 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agentscited by 60
  4. 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
  5. 2024Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wildcited by 47
  6. 2023Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learningcited by 41
  7. 2024A Survey of Mambacited by 39
  8. 2023SoK: Memorization in General-Purpose Large Language Modelscited by 37
  9. 2024Private Attribute Inference from Images with Vision-Language Modelscited by 37
  10. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applicationscited by 37
  11. 2024Source-Aware Training Enables Knowledge Attribution in Language Modelscited by 30
  12. 2024Undesirable Memorization in Large Language Models: A Surveycited by 29
  13. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  14. 2023On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?cited by 26
  15. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  16. 2025Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Executioncited by 26
  17. 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 26
  18. 2023Probing the Moral Development of Large Language Models through Defining Issues Testcited by 25
  19. 2024COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Actcited by 24
  20. 2024When LLMs Go Online: The Emerging Threat of Web-Enabled LLMscited by 24
  21. 2024A Probabilistic Perspective on Unlearning and Alignment for Large Language Modelscited by 21
  22. 2025A Survey on Privacy Risks and Protection in Large Language Modelscited by 21
  23. 2025The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaborationcited by 20
  24. 2025DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarioscited by 19
  25. 2024Machine Unlearning in Large Language Modelscited by 17
  26. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  27. 2024Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworkscited by 17
  28. 2025Security of Internet of Agents: Attacks and Countermeasurescited by 17
  29. 2024Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomescited by 14
  30. 2024Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approachescited by 14
  31. 2025Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Modelscited by 14
  32. 2025Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agentscited by 14
  33. 2024Detecting Training Data of Large Language Models via Expectation Maximizationcited by 13
  34. 2025Malicious LLM-Based Conversational AI Makes Users Reveal Personal Informationcited by 13
  35. 2024Exploring Vulnerabilities and Protections in Large Language Models: A Surveycited by 11
  36. 2024Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokenscited by 11
  37. 2024Prompt Obfuscation for Large Language Modelscited by 10
  38. 2025Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utilitycited by 10

50 most cited of 129 All 129 (JSON)