Author

Zexin Li

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#artificial intelligence Preprint Oct 2026

Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper studies transfer-based black-box attacks on DRL: the attacker crafts observation perturbations on a white-box surrogate agent and feeds them to an unknown victim. We for...

Zexin Li, Rui-Li Yao, Yi-Ming Zeng et al. · 0 citations

Conference Open access 2026

NaturalSloth: Revisiting Denial-of-Service Attacks on Large Language Models

This work introduces NaturalSloth, an adversarial dataset of natural, instruction-based DoS prompts, and designs a multi-agent synthesis framework to scale the dataset while preserving malicious intent and increasing semantic diversity.

Yiming Chen, Zexin Li, Xianghu Yue et al. · Annual Meeting of the Associ... · 0 citations