arXiv:2606. 07943v2 Announce Type: replace-cross Abstract: Agent skills extend general-purpose agents, but their open format enables skill poisoning: a tampered skill can make an agent run an attacker's command while completing the user's legitimate task.
By Haochang Hao, Dehai Min, Zhifang Zhang, Yunbei Zhang, Miao Xu, Yingqiang Ge, Lu Cheng
arXiv:2608. 09577v1 Announce Type: new Abstract: Agent skills, bundles of instructions and resources that an LLM agent loads on demand, form an emerging supply chain where a single poisoned skill can persistently compromise every agent that installs it.
By Hao Sui, Simeng Qin, Jie Liao, Xiaojun Jia, Bing Chen, Yang Liu
arXiv:2602. 14211v3 Announce Type: replace-cross Abstract: Agent skills extend LLM agents with task-specific instructions, executable scripts, and auxiliary resources, improving reusability but creating a new supply-chain attack surface.
By Xiaojun Jia, Jie Liao, Simeng Qin, Jindong Gu, Wenqi Ren, Xiaochun Cao, Yang Liu, Philip Torr
arXiv:2608.21500v1 Announce Type: cross
Abstract: Prompt injection is listed as the \#1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inje...
By Yibo Peng, Long Lian, David Wagner, Sizhe Chen
arXiv:2609.36570v1 Announce Type: cross
Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...
By Mark Russinovich
arXiv:2609.22510v1 Announce Type: cross
Abstract: As LLM applications integrate with external tools, they are increasingly exposed to indirect prompt injection (IPI), where adversarial instructions a...
By Justin Szczepaniak, Elad Feldman, Naum Viner, Ben Nassi