arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
By Heng Jin, Chaoyu Zhang, Hexuan Yu, Shanghao Shi, Ning Zhang, Y. Thomas Hou, Wenjing Lou
The paper introduces VoxPrivacy, a benchmark for assessing interactional privacy in Speech Language Models (SLMs). It evaluates models on a 32‑hour bilingual dataset across three difficulty tiers, revealing that most open‑source SLMs perform near random on conditional privacy decisions and even strong closed‑source systems struggle with proactive privacy inference. The authors also validate these findings on a real‑speech subset and show that fine‑tuning on a 4,000‑hour training set can improve privacy‑preserving capabilities while maintaining robustness.
By Yuxiang Wang, Hongyu Liu, Dekun Chen, Xueyao Zhang, Zhizheng Wu
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or deployed by commercial platforms such as ElevenLabs. We identify a previously uncharacterized liability: when synthetic speech is watermarked and human speech is not, detectors trained alongside latch onto the watermark as a spurious "watermark => fake" shortcut.
The paper presents methods for black-box forensic analysis of conversational large language model (LLM) agents, focusing on two tasks: attribution—identifying the base model powering a chatbot—and fingerprinting—detecting identical system prompts across different endpoints. Attribution classifiers achieve 98% accuracy using only a few non‑adversarial conversation turns, while a cross‑encoder fingerprinting approach attains an AUC of 0.768 and an F1 of 0.703 on unseen prompts, improving to 0.943 AUC when aggregating 50 interactions. These techniques enable investigators to trace AI‑enabled scams back to their underlying models and link related scams through shared system prompts.
By Isadora White, Yasaman Jafari, Taylor Berg-Kirkpatrick
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.
By Anna Yoo Jeong Ha, Ronik Bhaskar, Haitao Zheng, Ben Y. Zhao
arXiv:2608.29111v1 Announce Type: cross
Abstract: Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private...
By Kejia Zhang, Tianyuan Zou, Zixuan GU, Yang Liu