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Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models
Zeyuan Chen, Yihan Ma, Xinyue Shen +2
Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns. W…
Real Money, Fake Models: Deceptive Model Claims in Shadow APIs
Yage Zhang, Yukun Jiang, Zeyuan Chen +3
Access to frontier large language models (LLMs), such as GPT-5 and Gemini-2.5, is often hindered by high pricing, payment barriers, and regional restrictions. These limitations dri…
The Challenge of Identifying the Origin of Black-Box Large Language Models
Ziqing Yang, Yixin Wu, Yun Shen +3
The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the or…
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li +4
Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However…