The paper reviews four studies that combine blockchain and AI to secure data sharing, model integrity, and autonomous decision-making in distributed systems. It highlights how blockchain’s immutability, decentralized consensus, and verifiable provenance can address trust gaps in training data, real‑time monitoring, and automated code remediation. The authors propose a layered architecture integrating hardened models, blockchain‑anchored provenance, AI anomaly detection, and smart‑contract‑governed multi‑agent remediation, and outline open challenges in scalability, privacy‑transparency trade‑offs, and governance.
By Harsh Verma
AgentXploit is a two‑role auditing system that separates repository‑level attack‑path discovery from runtime exploitation for AI agents. The Analyzer Agent traces attacker‑controlled inputs to sensitive operations and records candidate attack paths, while the Exploiter Agent turns these paths into concrete attacks and refines them using runtime feedback. The system is evaluated on AgentXploit‑Bench, a benchmark of 72 reproducible vulnerabilities across 12 open‑source AI‑agent systems, achieving 59.3% end‑to‑end success compared to 38.4% for Codex, and 79.2% attack success on AgentDojo versus 52.7% for AgentVigil.
By Weida Liang, Shi Qiu, Zhun Wang, Simon Sure, Xiaoyuan Liu, Tianneng Shi, Zhaorun Chen, Wenbo Guo, Dawn Song
arXiv:2603. 05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
By Xisen Jin, Michael Duan, Qin Lin, Aaron Chan, Zhenglun Chen, Junyi Du, Xiang Ren
The paper surveys the growing use of AI agents that modify external state via the Model Context Protocol (MCP) ecosystem, noting an increase from 27% to 65% of tool use. It argues that when such agents operate on public blockchains, the blockchain execution layer’s properties—irreversibility, signing authority, continuous autonomy, and sequence-level composition—reshape the threat model, making failures irreversible. The authors organize existing MCP-security literature into an attack-surface taxonomy, present a Web3 risk-mapping matrix linking attack classes to amplified impacts and mitigations, and conclude that current defenses are inadequate, stopping fewer than 30% of attacks and less than 3% of model-level safety failures.
whyItMatters":"The study highlights that AI agents acting on Web3 introduce irreversible risks that conventional software security cannot address, underscoring the need for stronger, blockchain-aware safeguards."
By Rabimba Karanjai (Larry), Yang Lu (Larry), Nour Diallo (Larry), Wujie Xiong (Larry), Lei Xu (Larry), Weidong (Larry), Shi
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
By Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury
The paper introduces Fetch.ai, an industrial-strength architecture that blends classical multi-agent system principles with modern AI capabilities. It features a decentralized foundation of on-chain blockchain services for identity, discovery, and transactions, a development framework for secure, interoperable agents, a cloud-based deployment platform, and an agent-native LLM that translates human goals into multi-agent workflows. A decentralized logistics use case demonstrates autonomous agents dynamically discovering, negotiating, and transacting securely.
By Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa, Gabriel Paludo Licks