8 papers
Membership Inference Attacks on Tokenizers of Large Language Models
Meng Tong, Yuntao Du, Kejiang Chen +2
Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks are applied to pre-trained larg…
Automated Profile Inference with Language Model Agents
Yuntao Du, Zitao Li, Bolin Ding +4
Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open aven…
AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models
Yuntao Du, Minh Dinh, Kaiyuan Zhang +1
Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to bridge the ve…
Membership Inference Attacks Against Fine-tuned Diffusion Language Models
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to priva…
Beyond Data Privacy: New Privacy Risks for Large Language Models
Yuntao Du, Zitao Li, Ninghui Li +1
Large Language Models (LLMs) have achieved remarkable progress in natural language understanding, reasoning, and autonomous decision-making. However, these advancements have also c…
Imitative Membership Inference Attack
Yuntao Du, Yuetian Chen, Hanshen Xiao +2
A Membership Inference Attack (MIA) assesses how much a target machine learning model reveals about its training data by determining whether specific query instances were part of t…