Deep Reinforcement Learning (DRL) has demonstrated remarkable capabilities in domains such as robotics, finance, and autonomous systems. With the increasing cost of training, DRL models are increasingly shared and reused via model marketplaces, cloud platforms, and open-source repositories. This trend exposes DRL syste...
Ou-Bo Ma, L. Du, Yang Dai et al.· IEEE Transactions on Informa...· 0 citations
Rapid AI development across industries raises pressing security and privacy risks. This work presents a unified comparison of large language models, AI agents, and embodied agents, introducing a taxonomy of risks spanning data, models, systems, content, and applications, alongside a catalog of 24 specific threats. We c...
Existing Federated Learning (FL) backdoor attacks commonly employ round-wise proximity strategies, dynamically adapting malicious updates to mimic benign ones in order to evade detection. However, such adaptive mechanisms often introduce instability, increase computational overhead, and create temporal patterns that ma...
Xi Chen, Rui Zeng, Chun-Yi Zhou et al.· IEEE Transactions on Informa...· 0 citations
Reusable agent skills extend large language model (LLM) agents with task procedures, tool-use guidance, and output constraints. Yet these skills also act as externalized behavioral policies, which create a supply-chain risk: a third-party skill may preserve the declared task and valid output interface while covertly re...
Jia-Rui Li, Jia-Hao Chen, Chun-Yi Zhou et al.· 0 citations
This work forms this problem as backdoor generalization under training--inference trigger shift and introduces Lilith, a black-box anchor-to-family framework that achieves high family-wise attack success with limited utility degradation and a small trigger generalization gap.
Unsafe Semantic Distillation is proposed, which aligns adversarial perturbations with distributional representations of unsafe content rather than prompt-specific instances, and achieves 84% attack success rates, outperforming existing methods and exposing fundamental vulnerabilities in current multimodal safety archit...
Shuo Shi, Ruiping Yin, Na-En Xu et al.· Proceedings of the 32nd ACM...· 1 citation
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