Author

Pu-Yu Zeng

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Preprint Aug 2026

ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners

ColluSkill is proposed, a collusive multi-skill-chain attack framework that decomposes a complete malicious intent into interdependent sub-payloads embedded in independently packaged skills and employs LLM-based chain planning and scanner-feedback refinement to preserve chain-level attack semantics.

Pu-Yu Zeng, Simeng Qin, Jingzhi Li et al. · 1 citation

Preprint Aug 2026

Beyond Direct Access: Resource Hijacking in LLM Agents

This work is the first to identify and systematically study agent resource hijacking, a security blind spot in which attackers induce agents to invoke, consume, transfer, or control high-value resources for their own goals without directly obtaining those resources or their credentials.

Pu-Yu Zeng, Min-Gang Chen, Zhe-Li Liu et al. · 0 citations