69 citations · 194 across the 59 of their papers we have counts for
18 papers · 1 filter
LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage
Yuzhou Nie, Zhun Wang, Ye Yu +4
Recent studies have discovered that large language models (LLM) may be ``fooled'' to output private information, including training data, system prompts, and personally identifiabl…
A Practical Examination of AI-Generated Text Detectors for Large Language Models
Brian Tufts, Xuandong Zhao, Lei Li
The proliferation of large language models has raised growing concerns about their misuse, particularly in cases where AI-generated text is falsely attributed to human authors. Mac…
SoK: Watermarking for AI-Generated Content
Xuandong Zhao, Sam Gunn, Miranda Christ +11
As the outputs of generative AI (GenAI) techniques improve in quality, it becomes increasingly challenging to distinguish them from human-created content. Watermarking schemes are…
An Undetectable Watermark for Generative Image Models
Sam Gunn, Xuandong Zhao, Dawn Song
We present the first undetectable watermarking scheme for generative image models. Undetectability ensures that no efficient adversary can distinguish between watermarked and un-wa…
Multimodal Situational Safety
Kaiwen Zhou, Chengzhi Liu, Xuandong Zhao +3
Multimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments.…
Efficiently Identifying Watermarked Segments in Mixed-Source Texts
Xuandong Zhao, Chenwen Liao, Yu-Xiang Wang +1
Text watermarks in large language models (LLMs) are increasingly used to detect synthetic text, mitigating misuse cases like fake news and academic dishonesty. While existing water…