pith:H3N5XNZ5
SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces
SkillSafetyBench shows that attacks on reusable skills can induce unsafe actions in LLM agents even from benign user requests.
arxiv:2605.12015 v2 · 2026-05-12 · cs.CR · cs.AI · cs.CL · cs.LG · cs.MA
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\pithnumber{H3N5XNZ5XY46S2C2A62CYUGATD}
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Record completeness
Claims
Experiments with multiple CLI agents and model backends show that localized non-user attacks can consistently induce unsafe behavior, with distinct failure patterns across domains, attack methods, and scaffold-model pairings.
The 155 adversarial cases and rule-based verifiers accurately capture real-world skill-mediated safety failures without over- or under-counting due to case construction or verifier limitations.
SkillSafetyBench shows that localized non-user attacks via skills and artifacts can consistently induce unsafe agent behavior across domains and model backends, independent of user intent.
Formal links
Receipt and verification
| First computed | 2026-05-28T01:04:42.312567Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3edbdbb73dbe39e9685a07b42c50c098fb3b519f72597a913df36460fa98df38
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/H3N5XNZ5XY46S2C2A62CYUGATD \
| jq -c '.canonical_record' \
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# expect: 3edbdbb73dbe39e9685a07b42c50c098fb3b519f72597a913df36460fa98df38
Canonical record JSON
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