mlsanityData to 14 Oct 2026

Privacy Issues in Large Language Models: A Survey

arXiv 2312.06717 ↗First posted 11 Dec 2023cs.AIcited by 60

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Abstract (arXiv)

This is the first survey of the active area of AI research that focuses on privacy issues in Large Language Models (LLMs). Specifically, we focus on work that red-teams models to highlight privacy risks, attempts to build privacy into the training or inference process, enables efficient data deletion from trained models to comply with existing privacy regulations, and tries to mitigate copyright issues. Our focus is on summarizing technical research that develops algorithms, proves theorems, and runs empirical evaluations. While there is an extensive body of legal and policy work addressing these challenges from a different angle, that is not the focus of our survey. Nevertheless, these works, along with recent legal developments do inform how these technical problems are formalized, and so we discuss them briefly in Section 1. While we have made our best effort to include all the relevant work, due to the fast moving nature of this research we may have missed some recent work. If we have missed some of your work please contact us, as we will attempt to keep this survey relatively up to date. We are maintaining a repository with the list of papers covered in this survey and any relevant code that was publicly available at https://github.com/safr-ml-lab/survey-llm.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). Builds on 6 of its 223 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

6 of 223 references.

References · 6

  1. 2016Pointer Sentinel Mixture Modelscited by 3,096
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  3. 2019Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networkscited by 618
  4. 2020Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observationscited by 180
  5. 2020Tempered Sigmoid Activations for Deep Learning with Differential Privacycited by 122
  6. 2021Membership Inference Attack Susceptibility of Clinical Language Modelscited by 52

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 24; 2025: 25; 2026: 11.

60 papers.

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  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  2. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  3. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 163
  4. 2024Large Language Model Unlearning via Embedding-Corrupted Promptscited by 116
  5. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 116
  6. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 113
  7. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 83
  8. 2024Clio: Privacy-Preserving Insights into Real-World AI Usecited by 72
  9. 2024To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Modelscited by 43
  10. 2024Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplarscited by 37
  11. 2025OS Agents: A Survey on MLLM-based Agents for General Computing Devices Usecited by 35
  12. 2025A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directionscited by 34
38 more
  1. 2024Undesirable Memorization in Large Language Models: A Surveycited by 29
  2. 2024Privacy Preserving Prompt Engineering: A Surveycited by 25
  3. 2024"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreakcited by 25
  4. 2024IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomizationcited by 23
  5. 2025SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attackscited by 23
  6. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 22
  7. 2024RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Modelscited by 21
  8. 2025A Survey on Privacy Risks and Protection in Large Language Modelscited by 21
  9. 2024Can Language Models Act as Knowledge Bases at Scale?cited by 17
  10. 2025Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearningcited by 16
  11. 2024Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacycited by 14
  12. 2024Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocationcited by 14
  13. 2024Privacy-Aware Visual Language Modelscited by 12
  14. 2025When Two LLMs Debate, Both Think They'll Wincited by 10
  15. 2025LLMs for Customized Marketing Content Generation and Evaluation at Scalecited by 9
  16. 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
  17. 2024PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMscited by 7
  18. 2025LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysiscited by 7
  19. 2025DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoningcited by 7
  20. 2025Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attackcited by 5
  21. 2025Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Modelscited by 5
  22. 2025Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistantscited by 4
  23. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  24. 2025SoK: Semantic Privacy in Large Language Modelscited by 3
  25. 2026Jailbreaking LLMs & VLMs: Mechanisms, Evaluation, and Unified Defensecited by 3
  26. 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directionscited by 2
  27. 2025Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retrainingcited by 2
  28. 2025KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysiscited by 2
  29. 2025Dual-Space Smoothness for Robust and Balanced LLM Unlearningcited by 2
  30. 2025A Longitudinal Measurement of Privacy Policy Evolution for Large Language Modelscited by 2
  31. 2026When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategiescited by 2
  32. 2026Reflections and New Directions for Human-Centered Large Language Modelscited by 2
  33. 2026PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelinescited by 2
  34. 2025CellTypeAgent: Trustworthy cell type annotation with Large Language Modelscited by 1
  35. 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
  36. 2025Modular AI-Powered Interviewer with Dynamic Question Generation and Expertise Profilingcited by 1
  37. 2026Demystifying the Privacy-Utility Trade-off in LLM Interactionscited by 1
  38. 2024Exploring the Privacy Protection Capabilities of Chinese Large Language Modelscited by 0

50 most cited of 60 All 60 (JSON)

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  1. 2024Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
  2. 2025SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
  3. 2024Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions
  4. 2024Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey
  5. 2025Privacy Auditing of Large Language Models
  6. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
  7. 2024Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
  8. 2024Understanding Privacy Risks of Embeddings Induced by Large Language Models