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

arXiv: https://arxiv.org/abs/2608.00104 · HTML: https://mlsanity.com/paper/2608.00104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 25 of 25)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://mlsanity.com/paper/2410.09024/) (2024)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale](https://mlsanity.com/paper/2601.10338/) (2026)
- [AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents](https://mlsanity.com/paper/2506.00641/) (2025)
- ["Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild](https://mlsanity.com/paper/2602.06547/) (2026)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement](https://mlsanity.com/paper/2604.04989/) (2026)
- [Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms](https://mlsanity.com/paper/2310.10810/) (2023)
- [Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw](https://mlsanity.com/paper/2603.12644/) (2026)
- [On the Convergence of Zeroth-Order Federated Tuning for Large Language Models](https://mlsanity.com/paper/2402.05926/) (2024)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis](https://mlsanity.com/paper/2604.02837/) (2026)
- [Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks](https://mlsanity.com/paper/2402.06942/) (2024)
- [AI Agent Governance: A Field Guide](https://mlsanity.com/paper/2505.21808/) (2025)
- [Behavioral Integrity Verification for AI Agent Skills](https://mlsanity.com/paper/2605.11770/) (2026)
- [SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G](https://mlsanity.com/paper/2512.22579/) (2025)
- [Blockchain-enabled Resource Management and Sharing for 6G Communications](https://mlsanity.com/paper/2003.13083/) (2020)
- [Zero Trust Architecture for 6G Security](https://mlsanity.com/paper/2203.07716/) (2022)
- [When MoE Meets Blockchain: A Trustworthy Distributed Framework of Large Models](https://mlsanity.com/paper/2509.12141/) (2025)

## Abstract (arXiv)

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.