arXiv AI October 5, 2026 By Anna Yoo Jeong Ha, Ronik Bhaskar, Haitao Zheng, Ben Y. Zhao

Mitigating Private Data Leakage in LLMs with Whiteout

The paper introduces Whiteout, a tool that prevents large language models from revealing personally sensitive information (PSI) by overwriting such data with carefully crafted obfuscation samples. Whiteout is evaluated on various LLMs, including an OpenAI model, and shows effective PSI protection with minimal impact on model utility and safety. The study also tests Whiteout against multiple attack vectors and discusses its security and ethical implications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 17

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.

By Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh

llms multimodal safety

arXiv Machine Learning

Sep 1

On the Recoverability of Private Information Unlearning in Large Language Models

arXiv:2608.29943v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to re...

By Shicheng Hu, Runzhi Tian, Ziqiao Wang, Yongyi Mao

llms safety

arXiv AI

Aug 11

Stealing Reasoning Traces from Proprietary LLM APIs

arXiv:2608. 09867v1 Announce Type: cross Abstract: Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage.

By Alexander Panfilov, David Schmotz, Ilia Shumailov, Luca Beurer-Kellner, Joachim Schaeffer, Ameya Prabhu, Jonas Geiping, Maksym Andriushchenko

llms rag agents efficiency safety

arXiv Machine Learning

Aug 21

Inadvertent Context Leakage in Language Models

arXiv:2608. 19857v1 Announce Type: new Abstract: For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data.

By Jaiden Fairoze, Neal Mangaokar, Kamalika Chaudhuri, Sanjam Garg, Saeed Mahloujifar

llms agents reinforcement-learning safety

Hugging Face Trending Papers

Aug 20

Inadvertent Context Leakage in Language Models

For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data. We study whether the mere presence of such secrets in a model's context window introduces hidden correlations into the model's benign outputs, allowing reconstruction even when the model correctly refuses direct extraction.

llms agents reinforcement-learning safety

arXiv AI

Jul 22

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.

By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

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Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

On the Recoverability of Private Information Unlearning in Large Language Models

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Inadvertent Context Leakage in Language Models

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data