Skill Ecosystem
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Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem
Announce Type: replace Abstract: Agent skills extend local AI agents, such as Claude Code and OpenClaw, with additional functionality. Their growing popularity has led to dedicated marketplaces resembling mobile app stores, as well as automated scanners that assess whether skills are benign or malicious. However, scanner reports from individual marketplaces classify up to 46.8% of skills as malicious, raising concerns about false positives.
SkillGuard: A Permission Framework for Agent Skills
arXiv:2606.03024v1 Announce Type: new Abstract: Agent skills extend LLM agents with reusable instructions, scripts, tool bindings, and contextual dependencies. However, current skill ecosystems largely rely on trust-based loading and static inspection, leaving a gap between what a skill can inject into an agent's context and what it can cause the agent to do at runtime. This gap introduces new security and privacy risks, and existing defenses primarily inspect skill files statically or...
Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
arXiv:2602.12430v4 Announce Type: replace Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice. Rather than encoding all procedural knowledge within model weights, agent skills -- composable packages of instructions, code, and resources that agents load on demand -- enable dynamic capability extension without retraining. It is formalized in a paradigm of progressive...
'Creating thousands of high-skilled jobs': SoftBank to invest €75bn in French AI data centres
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Microsoft CEO to top engineers: Our goal is not to make users 'addicted' to Scout
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AI in nature conservation: Powerful tool or dangerous shortcut?
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Cyber Explorers management information
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Can the EU de-risk from China and make its trade relationship sustainable?
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"Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills
arXiv:2602.06547v3 Announce Type: replace Abstract: LLM-based coding agents increasingly rely on third-party extensions called skills, which bundle natural language instructions and helper scripts that execute with full user privileges. Community registries have emerged to distribute these skills, but the security implications remain unstudied due to the absence of labeled threat data. This paper presents a systematic security analysis of 98,380 skills collected from two major registries.
Generation AI: Schools in Asia are embracing artificial intelligence
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