Privacy Leakage in Language Models

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19 papers in the last four weeks, up 58% on the four weeks before. 0.2% of all new papers.

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SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models
Jun 3, 2026Peihua Mai, Xuanrong Gao, Youlong Ding +3Privacy-Preserving Language Models LLM Inference
1National University of Singapore (Chongqing) Research Institute · 2Chongqing Key Laboratory of Trusted Perception and Interaction Technology for Intelligent and Connected Vehicles, Chongqing, China · 3National University of Singapore +3
Token Rankings are Unforgeable Language Model Signatures
Jun 3, 2026Matthew Finlayson, Andreas Grivas, Xiang Ren +1Model Extraction Attacks Language Model Fingerprinting
University of Southern California · University of Edinburgh
Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents
Jun 2, 2026Kargi Chauhan, Pratibha RevankarData Leakage LLM Agent Security
University of California, Santa Cruz.
Ghost Tool Calls: Issue-Time Privacy for Speculative Agent Tools
Jun 1, 2026Bardia Mohammadi, Lars Klein, Akhil Arora +1Data Leakage LLM Agent Security
Max Planck Institute for Software Systems · EPFL · Aarhus University
Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs
May 30, 2026Yu-An Lu, Ci-Yang Tsai, Yu-Lin Tsai +2LLM Prompting Privacy Leakage in Language Models
National Yang Ming Chiao Tung University · UC Berkeley
"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents
May 30, 2026Soham Roy, Sarthakbrata Halder, Arya Bharaty +5Data Leakage LLM Agent Security
KIIT Bhubaneshwar · 2BITS Pilani · 3Lam Research
Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
May 29, 2026Humzah Merchant, Bradford LevyPrivacy Leakage in Language Models Memorization in Generative Models
University of Chicago, Chicago, IL 60637.
VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents
May 29, 2026Youting Wang, Yuan Tang, Yitian Qian +1Data Leakage Tool Use in VLMs
Northeastern University · Carnegie Mellon University · Boston University +1
PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
May 29, 2026Mingxuan Zhang, Jiahui Han, Dadi Guo +5Data Leakage Privacy Auditing
Shanghai Artificial Intelligence Laboratory · Southeast University
MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents
May 29, 2026Alexander Gurung, Spandana Gella, Alexandre Drouin +3Data Leakage Deep Research Agents
University of Edinburgh · ServiceNow AI Research · McGill University +2
Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
May 28, 2026Marta Aparicio Rodriguez, Anastasia Borovykh, Grigorios A. Pavliotis +1Privacy Leakage in Language Models Memorization in Generative Models
Department of Mathematics, Imperial College London, UK · ML Lab, Capital Fund Management, France · Department of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
AMNESIA: A Large Scale Medical Unlearning Benchmark Suite with Disease-Informed Analysis
May 28, 2026Saeedeh Davoudi, Reihaneh Iranmanesh, Ophir Frieder +1Healthcare Privacy Leakage in Language Models
1IR Lab, Computer Science Department, Georgetown University, Washington D.C.
The Attentional White Bear Effect in Transformer Language Models
May 27, 2026Rebecca Ramnauth, Brian ScassellatiTransformer Interpretability LLM Alignment
Yale University New Haven, CT, USA
When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR
May 27, 2026Maike Züfle, Jan NiehuesData Leakage Domain Adaptation for ASR
Karlsruhe Institute of Technology, Germany
Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems
May 26, 2026Aman Priyanshu, Supriti Vijay, Esha PahwaData Leakage Multi-Agent LLM Systems
Foundation AI · USA · Corvic AI
Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization
May 26, 2026Weixin Liu, Bowen Qu, Juming Xiong +3LLM Auditing Privacy Leakage in Language Models
Vanderbilt University · Vanderbilt University Medical Center
Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing
May 25, 2026Michał Brzozowski, Zuzanna Dubanowska, Enrico Cassano +1Language Model Fingerprinting Privacy Leakage in Language Models
Samsung AI Center, Warsaw, Poland · University of Turin, Italy · University of Warsaw, Poland
Extracting Training Data from Diffusion Language Models via Infilling
May 22, 2026Yihan Wang, N. AsokanData Leakage Masked Diffusion Models
University of Waterloo · KTH Royal Institute of Technology
What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
May 22, 2026Mingyuan Fan, Yu Liu, Fuyi Wang +1Privacy-Preserving Language Models Adversarial Attacks
East China Normal University Shanghai, China · RMIT University Melbourne, Australia
Hidden-State Privacy Has an Empty Middle
May 21, 2026Alexander Okezue BellPrivacy-Preserving Language Models Adversarial Attacks
Stanford University 450 Jane Stanford Way Stanford, CA 94305
LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systems
May 21, 2026Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas +2Adversarial Representation Learning Privacy-Preserving Language Models
Rensselaer Polytechnic Institute · IBM Research
Boundary-targeted Membership Inference Attacks on Safety Classifiers
May 21, 2026Anthony Hughes, Alexander Goldberg, Prince Jha +3Membership Inference Attacks Privacy Leakage in Language Models
University of Sheffield · Carnegie Mellon University · MBZUAI
Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
May 21, 2026Ziyi Tong, Feifei Sun, Le Minh NguyenPrivacy Leakage in Language Models Data Contamination in Language Models
Japan Advanced Institute of Science and Technology, Japan
Counterfactual Likelihood Tests for Indirect Influence in Private Reasoning Channels
May 18, 2026Alexander Boesgaard LorupCounterfactual Evaluation LLM Interpretability
Openhagen
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
May 18, 2026Kaiwen Luo, Zhenhong Zhou, Leo Wang +31Privacy Leakage in Language Models Audio-Language Models
Nanyang Technological University · Independent Researcher · The University of Melbourne +15
An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments
May 18, 2026Hongjang Yang, Hyunsik Na, Daeseon ChoiData Leakage Privacy Leakage in Language Models
Department of Information Security Soongsil University Seoul, Korea · AI Safety Center Soongsil University Seoul, Korea · Department of AI Software Soongsil University Seoul, Korea
ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
May 16, 2026Yujie Lin, Chengyi Yang, Zhishang Xiang +2Privacy Leakage in Language Models Model Editing
School of Informatics, Xiamen University, China · Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China · School of Film, Xiamen University, China +2
Membership Inference Attacks on Discrete Diffusion Language Models
May 15, 2026Shailesh KasivelrajanMasked Diffusion Models Membership Inference Attacks
Indian Institute of Technology Madras
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
May 14, 2026Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1Multilingual Language Model Evaluation Privacy Leakage in Language Models
GSCST, Seoul National University · Department of Artificial Intelligence, Chung-Ang University · Korean Surgical Researcher Foundation, Republic of Korea +1
Reconstruction of Personally Identifiable Information from Supervised Finetuned Models
May 12, 2026Sae Furukawa, Alina OpreaSupervised Fine-Tuning Privacy Leakage in Language Models
Northeastern University Boston, Massachusetts, USA

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SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models

Peihua Mai, Xuanrong Gao, Youlong Ding, Xianglong Du+2

1National University of Singapore (Chongqing) Research Institute · 2Chongqing Key Laboratory of Trusted Perception and Interaction Technology for Intelligent and Connected Vehicles, Chongqing, China · 3National University of Singapore+3

Privacy-Preserving Language Models LLM Inference Privacy Leakage in Language Models Efficient Language Model Inference

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With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue. Existing privacy-preserving inference methods sacrifice either utility or efficiency, and often require model-specific modifications that limit their compatibility. In this paper, we propose SharedRequest, a model-agnostic framework for privacy-preserving LLM inference that reformulates privacy protection at the batch level rather than the individual-prompt level. The key idea is to obscure sensitive information by mixing original prompts with noisy variants, while grouping semantically equivalent instructions to amortize the inference cost over a large batch of queries with minimal impact on LLM response quality. This design is independent of the LLM architecture, requiring no access to model parameters or architectural modification. Empirical results demonstrate that SharedRequest achieves over

20\%

higher utility compared to prior differential privacy baselines, and its shared-prompt mechanism reduces query cost by up to

5\times

compared to non-batched inference.

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Latest papers 159