arXiv:2605.12015v2 Announce Type: replace-cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety evaluations: even when the user request is benign, unsafe influence may reside in skill guidance, local artifacts, or execution-environment files that steer the agent toward unsafe actions. We present SkillSafetyBench, a runnable benchmark for evaluating such s
As AI agents become more sophisticated and modular, the security vulnerabilities inherent in their 'skill-facing attack surfaces' are becoming critically apparent, necessitating new evaluation benchmarks.
This highlights a growing attack vector in the expanding landscape of AI agents, directly impacting their trustworthiness and deployment in sensitive applications.
The focus of AI safety shifts to include not just the ethical alignment of the core model but also the security and integrity of its peripheral tools, data, and execution environments.
- · Cybersecurity firms specializing in AI
- · AI safety researchers
- · Developers of secure AI agent frameworks
- · AI agent developers neglecting security
- · Organizations deploying agents without robust safety evaluations
Increased investment in bespoke security measures and evaluation tools for AI agent deployments.
New regulatory standards and compliance requirements emerge focusing on the security of AI agent modular components.
The development and adoption of AI agents in critical infrastructure or defense applications is accelerated by enhanced security, or slowed down by perceived attack surfaces.
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