arXiv AI June 17, 2026 By Muxing Li, Zesheng Ye, Sharon Li, Feng Liu

Combating Data Laundering in LLM Training

arXiv:2604. 01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 6

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

arXiv:2606. 06286v1 Announce Type: cross Abstract: Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use.

By Gianluca Barmina, Peter Schneider-Kamp, Lukas Galke Poech

llms safety

arXiv Machine Learning

Jul 30

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

arXiv:2607. 26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence.

By Pushkal Kumar, Tucker Nielson, Tanish Kolhe, Shubham Zala, Vincent Li

llms rag safety

arXiv Machine Learning

Jun 2

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

arXiv:2506. 14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks.

By Yiwei Chen, Soumyadeep Pal, Yimeng Zhang, Qing Qu, Sijia Liu

llms safety

arXiv AI

Jun 9

MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models

arXiv:2606. 07996v1 Announce Type: cross Abstract: Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and raises ethical, legal, and fairness concerns.

By Kaixin Lan, Mu You, Tao Fang, Binkai Ou, Lidia S. Chao, Derek F. Wong

llms benchmarks safety

arXiv AI

Aug 5

Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Reranking

arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.

By Peijia Guo, Wenxuan Xie, ZiGuang Li, Ming Li

llms rag benchmarks

arXiv AI

Jun 6

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

arXiv:2509. 20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses.

By Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi, Kaushik Dutta

llms rag nlp safety

Related stories

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models

Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Reranking

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface