SkillAtlas: An Attack Trace Library for Agent Skills

Abstract:Agent skills are reusable units for language-model agents, but their risks emerge through model decisions, user context, tool calls, and execution feedback rather than through stable signatures or a single sandbox run. Existing static, dynamic, and benchmark-style evaluations rarely preserve public evidence that can be inspected, searched, and reused. We present SkillAtlas, a hosted attack trace library that converts private agent-skill security report bundles into reviewed, redacted, and searchable public cases. The library contains 3,014 cases, 6,589 traces, 151,131 steps, 233 affected skills, and 8 risk categories; 42.5% of successful cases first become successful after a non-success initial round, and trajectory-grounded labels improve pre-execution guard accuracy to 0.770.

Accepted at REALM @ EMNLP 2026 (non-archival workshop paper). 9 pages, 4 figures

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2609.13353 [cs.CR]

(or arXiv:2609.13353v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.13353 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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