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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DistractMIA: Black-Box Membership Inference on Vision-Language Models via Semantic Distraction
May 12, 2026Hongyi Tang, Zhihao Zhu, Yi YangVision-Language Models Membership Inference Attacks
The Hong Kong University of Science and Technology
Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing
May 11, 2026Ari Holtzman, Peter WestData Leakage Privacy Leakage in Language Models
University of Chicago · University of British Columbia
PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines
May 11, 2026Riya Tapwal, Abhishek Kumar, Carsten MapleData Leakage Multi-Agent LLM Systems
School of Computing and Electrical Engineering · Indian Institute of Technology, Mandi (IIT) · The Alan Turing Institute, London +1
JUMP: Efficient Membership Inference on Fine-Tuned Diffusion Language Models
May 9, 2026Yeachan Jun, Albert NoMembership Inference Attacks Privacy Leakage in Language Models
Department of Artificial Intelligence Yonsei University, Seoul, Korea
The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
May 8, 2026William Brach, Federico Torrielli, Stine Lyngsø Beltoft +3Privacy Leakage in Language Models Multi-Agent Systems
University of Turin Dept. of Computer Science Torino, Italy · University of Southern Denmark Odense, Denmark
LeakDojo: Decoding the Leakage Threats of RAG Systems
May 7, 2026Maosen Zhang, Jianshuo Dong, Boting Lu +4Data Leakage Privacy Leakage in Language Models
Tsinghua University, China · Ant International, China · Nanyang Technological University, Singapore
Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
May 5, 2026Tejas Kulkarni, Antti Koskela, Laith ZumotIn-Context Retrieval Membership Inference Attacks
Nokia Bell Labs · Nokia
PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization
May 4, 2026Mingshuo Liu, Yiwei Zha, Min ChenData Leakage Cybersecurity
Vrije Universiteit Amsterdam
On the Privacy of LLMs: An Ablation Study
May 4, 2026Karima Makhlouf, Lamiaa Basyoni, Syed Khaderi +4LLM Backdoor Attacks Membership Inference Attacks
UDST, Doha, Qatar
Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance
May 3, 2026Anamika Paul Rupa, Anietie AndyMemorization in Language Models Neural Network Memorization
Howard University
E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems
May 1, 2026Zelin Guan, Shengda Zhuo, Zeyan Li +4Membership Inference Attacks Privacy Leakage in Language Models
College of Cyber Security, Jinan University, Guangzhou, China · School of Computer Science, Shanghai Jiaotong University, Shanghai, China · Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China +1
MCPHunt: An Evaluation Framework for Cross-Boundary Data Propagation in Multi-Server MCP Agents
Apr 30, 2026Haonan Li, Tianjun Sun, Yongqing Wang +1MCP Security Privacy Leakage in Language Models
Key Laboratory of Intraplate Volcanoes and Earthquakes (China University of Geosciences, Beijing), Ministry of Education, Beijing 100083, China · School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Apr 30, 2026Zi Li, Tian Zhou, Wenze Li +3Data Leakage LLM Backdoor Attacks
Nanjing University
Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation
Apr 29, 2026Guillermo Iglesias, Gema Bello-Orgaz, María Navas-Loro +3Synthetic Data Augmentation Synthetic Data Generation
Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain · Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain · Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain +5
When Agents Shop for You: Role Coherence in AI-Mediated Markets
Apr 29, 2026Soogand Alavi, Salar NozariPrivacy Leakage in Language Models Agentic Commerce
Tippie College of Business, University of Iowa
Evaluation of Prompt Injection Defenses in Large Language Models
Apr 26, 2026Priyal Deep, Shane Emmons, Amy Fox +4Data Leakage Privacy Leakage in Language Models
Swept AI[1] · University of Michigan
The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
Apr 26, 2026Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al MallahData Leakage Multi-Agent LLM Systems
Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, Canada
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition
Apr 25, 2026Rujing Yao, Yufei Shi, Yang Wu +5Privacy-Preserving Language Models Knowledge Augmentation for Language Models
1Nankai University, China · 2The Hong Kong Polytechnic University, China · 3Worcester Polytechnic Institute, USA +3
PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning
Apr 23, 2026Xiaoyi Chen, Haoyuan Wang, Siyuan Tang +4LLM Auditing Privacy Leakage in Language Models
Indiana University Bloomington · Independent Researcher · Michigan State University +2
CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents
Apr 23, 2026Wenjie Fu, Xiaoting Qin, Jue Zhang +5Data Leakage Contextual Integrity
Huazhong University of Science and Technology, China · Microsoft
Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach
Apr 23, 2026Guilin Deng, Silong Chen, Yuchuan Luo +6Membership Inference Attacks Privacy Leakage in Language Models
Colleague of Computer Science and Technology, National University of Defense Technology, Changsha, China · City University of Hong Kong, Hong Kong, China · Shenzhen University, Shenzhen, China
Behavioral Transfer in AI Agents: Evidence and Privacy Implications
Apr 21, 2026Shilei Luo, Zhiqi Zhang, Hengchen Dai +1Privacy Leakage in Language Models Personal AI Agents
Washington University in St. Louis, St. Louis, MO · University of California, Los Angeles, Los Angeles, CA
An AI Agent Execution Environment to Safeguard User Data
Apr 21, 2026Robert Stanley, Avi Verma, Lillian Tsai +2AI Agent Security Privacy Leakage in Language Models
University of California, Los Angeles · Google
Detecting Data Contamination in Large Language Models
Apr 21, 2026Juliusz Janicki, Savvas Chamezopoulos, Evangelos Kanoulas +1Membership Inference Attacks LLM Auditing
University of Amsterdam · Elsevier
Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs
Apr 20, 2026Ruixuan Liu, David Evans, Li XiongLLM Auditing Privacy Leakage in Language Models
Emory University · University of Virginia
Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
Apr 20, 2026Meifang Chen, Zhe Yang, Huang Nianchen +4Data Leakage Memorization in Language Models
†The Chinese University of Hong Kong · ‡Nanyang Technological University · §University of Southern California +1
CAMP: Cumulative Agentic Masking and Pruning for Privacy Protection in Multi-Turn LLM Conversations
Apr 16, 2026Aman PanjwaniPrivacy-Preserving Language Models Privacy Leakage in Language Models
Independent Researcher
VoxSafeBench: Not Just What Is Said, but Who, How, and Where
Apr 16, 2026Yuxiang Wang, Hongyu Liu, Yijiang Xu +9Language Model Safety Evaluation Privacy Leakage in Language Models
1The Chinese University of Hong Kong, Shenzhen · 2Shenzhen Loop Area Institute · 3Amphion Technology Co., Ltd.
AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs
Jan 30, 2026Jaehee Kim, Pilsung KangPrivacy-Preserving Language Models Privacy Leakage in Language Models
Department of Industrial Engineering, Seoul National University, South Korea.
NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
Jan 21, 2026Saswat Das, Ferdinando FiorettoLLM Guardrails Privacy Leakage in Language Models
University of Virginia

May 12, 2026, cs.CVJ/K move · Enter open · S save

DistractMIA: Black-Box Membership Inference on Vision-Language Models via Semantic Distraction

Hongyi Tang, Zhihao Zhu, Yi Yang

The Hong Kong University of Science and Technology

Vision-Language Models Membership Inference Attacks Privacy Leakage in Language Models

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Vision-language models (VLMs) are trained on large-scale image-text corpora that may contain private, copyrighted, or otherwise sensitive data, motivating membership inference as a tool for training-data auditing. This is especially challenging for deployed VLMs, where auditors typically observe only generated textual responses. Existing VLM membership inference attacks either rely on probability-level signals unavailable in such settings, or use mask-based semantic prediction tasks whose effectiveness depends on object-centric visual assumptions. To address these limitations, we propose DistractMIA, an output-only black-box framework based on semantic distraction. Rather than removing visual evidence, DistractMIA preserves the original image, inserts a known semantic distractor, and measures how generated responses change. This design is motivated by the intuition that member samples remain more anchored to the original image semantics, while non-member samples are more easily redirected toward the distractor. To make this signal reliable, DistractMIA calibrates distractor configurations on a reference set and derives membership scores from repeated textual generations, capturing response stability and distractor uptake without accessing logits, probabilities, or hidden states. Experiments across multiple VLMs and benchmarks show that DistractMIA consistently outperforms both output-only and stronger-access baselines. Its performance on a medical benchmark further demonstrates applicability beyond object-centric natural images.

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