SIGIL cryptographically seals the audit-runtime gap for LLM skills via an on-chain registry with four publication types, DAO vetting, and a runtime verification loader that enforces integrity and permissions.
Formal analysis and supply chain security for agentic AI skills
7 Pith papers cite this work. Polarity classification is still indexing.
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Runtime Skill Audit introduces targeted runtime probing to detect malicious LLM agent skills, reporting 90% accuracy and resilience to self-evolving attacks on 100 skills versus static baselines.
About 18.2% of structurally flagged skill pairs represent genuine compositional safety risks in agent skill registries, with exploitation gated by host model behavior.
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
SkillVetBench is a two-stage benchmark combining natural-language semantic vetting and instrumented sandbox execution to detect and provide runtime evidence for malicious skills in open agent platforms, with experiments showing static methods miss up to 89% of threats.
Qualixar OS provides a runtime for multi-agent AI systems with support for 12 topologies, LLM-driven team design, dynamic routing, consensus judging, content attribution, and protocol bridging, achieving 100% accuracy on a custom 20-task suite at $0.000039 mean cost per task.
citing papers explorer
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Sealing the Audit-Runtime Gap for LLM Skills
SIGIL cryptographically seals the audit-runtime gap for LLM skills via an on-chain registry with four publication types, DAO vetting, and a runtime verification loader that enforces integrity and permissions.
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Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security
Runtime Skill Audit introduces targeted runtime probing to detect malicious LLM agent skills, reporting 90% accuracy and resilience to self-evolving attacks on 100 skills versus static baselines.
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Behavioral Integrity Verification for AI Agent Skills
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
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Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems
SkillVetBench is a two-stage benchmark combining natural-language semantic vetting and instrumented sandbox execution to detect and provide runtime evidence for malicious skills in open agent platforms, with experiments showing static methods miss up to 89% of threats.
- SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills