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
arXiv:2607. 01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments.
By Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jiang, Huiping Cao, Satyajayant Misra, Jayashree Harikumar
arXiv:2606. 06895v1 Announce Type: cross Abstract: The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems.
By Song Guo, Huawei Huang, Dongping Liu, Aoyu Zhang, Luyao Zhang
arXiv:2609.39584v1 Announce Type: cross
Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Intern...
By Zawad Yalmie Sazid, Robert Abbas
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:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.
By Kiarash Ahi, Saeed Valizadeh