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cs.CR2026
Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directions
Lele Zheng, Tong Chen, Ke Cheng +5
Large-model-driven embodied agents integrate foundation models with perception, reasoning, planning, and physical action, extending conventional model-level risks into embodied clo…
cs.CR2026
When Convenience Becomes Risk: A Semantic View of Under-Specification in Host-Acting Agents
Di Lu, Yongzhi Liao, Xutong Mu +5
Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a di…
cs.CR2025
PriFFT: Privacy-preserving Federated Fine-tuning of Large Language Models via Hybrid Secret Sharing
Zhichao You, Xuewen Dong, Ke Cheng +5
Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping trainin…