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
Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executa...
Yun-Hao Feng, Rui-Xiao Lin, Ming Wen et al.· 0 citations
This study unveils the capability of attackers to generate adversarial policies even when restricted to partial observations of the victims in multi-agent competitive environments, and proposes a novel black-box attack (SUB-PLAY) that incorporates the concept of constructing multiple subgames to mitigate the impact of...
Oubo Ma, Yuwen Pu, L. Du et al.· Conference on Computer and C...· 16 citations
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