Securing the Metaverse and Medical MeTAI: Threat Taxonomy, Adversary Models, Risk Quantification, and a Data-Driven Defense Architecture

Abstract

The metaverse—a persistent, multiuser fusion of digitally augmented reality and computer-generated virtuality— is emerging as a programmable substrate for identity, assets, and interaction. Its heterogeneous stack (XR clients, engines/SDKs, Web3 rails, wallets, marketplaces) enlarges the attack surface. This paper contributes: (i) a structured threat taxonomy specialized for Web3/XR platforms; (ii) explicit system and adversary models; (iii) a risk quantification scheme combining behavioral and on-chain signals; and (iv) a data-driven defense architecture aligning decentralized identity, wallet/custody guardrails, analytics, AI-aided detection, and policy instrumentation. We further instantiate these controls in the Medical MeTAI context, where confidentiality, integrity, and provenance requirements are stringent.