Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking

2026-07-30

AI Summary

This work addresses capability misrepresentation and security vulnerabilities in agentic AI networks arising from reliance on third-party skill sets and declarative trust models. To this end, the authors propose TrustAgentNet, a dual-chain framework integrating blockchain and zero-trust architecture. The framework employs a global Skill Chain to manage the lifecycle of skill metadata and dynamically constructs task-oriented Collaboration Chains to enable lightweight, trustworthy distributed multi-agent collaboration. It innovatively supports synergistic on-chain consensus and off-chain auditing while providing skill self-recovery capabilities under adversarial attacks. Experimental results demonstrate 100% verification accuracy across 50 AI models, successfully identifying all 10 malicious skills; on 171 non-AI ClawHub skills, it achieves 83.91% accuracy and an F1 score of 0.85, with manageable blockchain overhead.

Abstract

Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.

Problem

• Agentic AI networking
• trust-by-declaration
• claim-to-capability inconsistency
• security vulnerabilities
• zero-trust

Innovation

• blockchain
• zero-trust
• agentic AI
• skillset verification
• multi-agent collaboration