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
As large language models (LLMs) are increasingly redistributed, adapted, and served behind opaque APIs, model ownership can no longer be established reliably by inspecting model internals or deployment records. This creates a need for behavioral signatures that remain observable through black-box interaction. Yet most...
Zhong-Rui Sun, Jia-Hao Chen, Ou-Bo Ma et al.· 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 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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