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Inferring Hidden User Models from the Behavior of Personalized LLM Agents
Haoyang Li, Yaxin Xiao, Qingqing Ye +2
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call user models, to guide later decisi…
What You See Is Not What AI Gets: DPAgent-in-the-Middle Defense Against AI-Groomed Deceptive Patterns
Zewei Shi, Ruoxi Sun, Haoyang Li +4
Privacy deceptive patterns in web interfaces manipulate users into disclosing personal data, yet existing defenses are fragmented, static, and increasingly vulnerable to manipulati…
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
Yaxin Xiao, Qingqing Ye, Zi Liang +4
Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through…
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift
Jiamin Chang, Haoyang Li, Hammond Pearce +3
The growing adoption of artificial intelligence (AI) has amplified concerns about trustworthiness, including integrity, privacy, robustness, and bias. To assess and attribute these…
Membership Inference Attacks and Defenses in Federated Learning: A Survey
Li Bai, Haibo Hu, Qingqing Ye +3
Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devi…