FDAA Skill Verification Pipeline v1.0: Defense-in-Depth Security for AI Agent Skills

A four-tier verification pipeline for AI agent skills implementing defense-in-depth security. Tier 1 (Fast Pass) provides O(n) pattern matching against known threat signatures. Tier 2 (Guard Model) uses LLM-based semantic analysis to detect Line Jumping, Scope Drift, and Trojan Skills. Tier 3 (Sandbox) monitors runtime behavior in isolated containers. Tier 4 (Registry) provides cryptographic signing with Ed25519 and Merkle tree integrity verification. Validation against the OpenClaw skills ecosystem demonstrates 96.3% detection accuracy for known attack patterns. Reference implementation: fdaa-cli.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-18
DOI
https://doi.org/10.5281/zenodo.18676240
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

FDAA Skill Verification Pipeline v1.0: Defense-in-Depth Security for AI Agent Skills

Ada, Rudi Heydra
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

FDAA Skill Verification Pipeline v1.0: Defense-in-Depth Security for AI Agent Skills

Ada, Rudi Heydra
article en

Abstract

A four-tier verification pipeline for AI agent skills implementing defense-in-depth security. Tier 1 (Fast Pass) provides O(n) pattern matching against known threat signatures. Tier 2 (Guard Model) uses LLM-based semantic analysis to detect Line Jumping, Scope Drift, and Trojan Skills. Tier 3 (Sandbox) monitors runtime behavior in isolated containers. Tier 4 (Registry) provides cryptographic signing with Ed25519 and Merkle tree integrity verification. Validation against the OpenClaw skills ecosystem demonstrates 96.3% detection accuracy for known attack patterns. Reference implementation: fdaa-cli.

Zenodo (CERN European Organization for Nuclear Research)
F5 Networks (United States) (US)
Quality Education
Openalex Percentile: Top 53%
Adversarial Robustness in Machine Learning
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