Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking
- URL: http://arxiv.org/abs/2608.00104v1
- Date: Fri, 31 Jul 2026 00:24:06 GMT
- Title: Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking
- Abstract summary: Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration.<n>This paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework.
- Score: 41.895053158170136
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions [47.65093774088672]
This survey and tutorial article reviews the literature over the period 1980--2026 on the evolution from classical multi-agent systems to open agent networks.<n>We first review this evolution and show how the trust boundary expands from individual execution to cross-agent, cross-platform, and cross-organizational interaction.<n>We develop a five-dimensional taxonomy covering entity and capability trust, authorization and delegation trust, information and trust, coordination and group-robustness trust, and accountability and settlement trust.
arXiv Detail & Related papers (2026-08-05T09:32:35Z) - Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain [12.755719715389167]
This paper presents a Systematization Knowledge organized around a bidirectional trust framework.<n>In the B direction, we examine how blockchain provides trust infrastructure for agents, spanning identity and account abstraction, permission and delegation, intent-centric execution, and tokenized agent economies.<n>In the A direction, we examine how AI agents participate in core blockchain mechanisms including security auditing, consensus, and governance.
arXiv Detail & Related papers (2026-05-09T12:42:07Z) - TRUST: A Framework for Decentralized AI Service v.0.1 [47.384270414446604]
Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification.<n>We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations.<n>We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses.
arXiv Detail & Related papers (2026-04-29T19:32:58Z) - GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
arXiv Detail & Related papers (2026-04-27T11:34:21Z) - Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework [0.0]
We introduce Zer0n, a framework that anchors the reasoning capabilities of Large Language Models (LLMs) to the immutable audit trails of blockchain technology.<n>We integrate Gemini 2.0 Pro for logic-based vulnerability detection with the Avalanche C-Chain for tamper-evident artifact logging.
arXiv Detail & Related papers (2026-01-11T18:27:52Z) - AI Agent Architecture for Decentralized Trading of Alternative Assets [2.8195433571821162]
GoldMine OS is a research oriented architecture that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain based stablecoin ("OZ")<n>We describe four cooperative agents (Compliance, Token Issuance, Market Making, and Risk Control) and a coordinating core, and evaluate the system through simulation and a controlled pilot deployment.<n>In experiments the prototype delivers on demand token issuance in under 1.2 s, more than 100 times faster than manual.
arXiv Detail & Related papers (2025-07-15T09:11:19Z) - Decompiling Smart Contracts with a Large Language Model [51.49197239479266]
Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
arXiv Detail & Related papers (2025-06-24T13:42:59Z) - Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies.<n>By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
arXiv Detail & Related papers (2025-04-22T09:01:55Z) - zScore: A Universal Decentralised Reputation System for the Blockchain Economy [0.8246494848934447]
We provide a robust framework titled zScore, a core primitive for reputation derived from a wallet's onchain behaviour.<n>The initial results tested on retroactive data from lending protocols establish a strong correlation between a good zScore and healthy borrowing and repayment behaviour.<n>We present a list of possible applications of our system in Section 5, thereby establishing its utility in rewarding actual value creation.
arXiv Detail & Related papers (2025-02-17T07:19:04Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.