# SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

arXiv: https://arxiv.org/abs/2506.12699 · HTML: https://mlsanity.com/paper/2506.12699/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2025.

## Built on by (most cited first, 19 of 19)

- [MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation](https://mlsanity.com/paper/2510.15186/) (2025)
- [Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation](https://mlsanity.com/paper/2512.16310/) (2025)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [Trusted Knowledge Extraction for Operations and Maintenance Intelligence](https://mlsanity.com/paper/2507.22935/) (2025)
- [The Model's Language Matters: A Comparative Privacy Analysis of LLMs](https://mlsanity.com/paper/2510.08813/) (2025)
- [SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models](https://mlsanity.com/paper/2512.10998/) (2025)
- [A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference](https://mlsanity.com/paper/2601.00694/) (2026)
- [Improving User Privacy in Personalized Generation: Client-Side Retrieval-Augmented Modification of Server-Side Generated Speculations](https://mlsanity.com/paper/2601.17569/) (2026)
- [Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways](https://mlsanity.com/paper/2604.21083/) (2026)
- [Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers](https://mlsanity.com/paper/2604.24785/) (2026)
- [Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict](https://mlsanity.com/paper/2601.03546/) (2026)
- [Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Information](https://mlsanity.com/paper/2603.16918/) (2026)
- [Say Something Else: Rethinking Contextual Privacy as Information Sufficiency](https://mlsanity.com/paper/2604.06409/) (2026)
- [CyberCane: Neuro-Symbolic RAG for Privacy-Preserving Phishing Detection with Formal Ontology Reasoning](https://mlsanity.com/paper/2604.23563/) (2026)
- [GradeLegal: Automated Grading for German Legal Cases](https://mlsanity.com/paper/2605.21076/) (2026)
- [An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory](https://mlsanity.com/paper/2607.11707/) (2026)
- [Position: Privacy Is a Claim, Not a Property of Synthetic Data](https://mlsanity.com/paper/2609.01273/) (2026)
- [Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2609.27847/) (2026)
- [DP-ES: Differentially Private Evolution Strategies for Prompt Optimization](https://mlsanity.com/paper/2610.06236/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from extensive user interactions poses risks of sensitive information leakage. Most existing surveys focus on the privacy implications of the training data but tend to overlook privacy risks from user interactions and advanced LLM capabilities. This paper aims to fill that gap by providing a comprehensive analysis of privacy in LLMs, categorizing the challenges into four main areas: (i) privacy issues in LLM training data, (ii) privacy challenges associated with user prompts, (iii) privacy vulnerabilities in LLM-generated outputs, and (iv) privacy challenges involving LLM agents. We evaluate the effectiveness and limitations of existing mitigation mechanisms targeting these proposed privacy challenges and identify areas for further research.