The CryptoNeo Threat Modelling Framework (CNTMF): Securing Neobanks and Fintech in Integrated Blockchain Ecosystems

TL;DR

The paper presents CNTMF, a comprehensive threat modelling framework tailored for blockchain-integrated fintech ecosystems, enhancing security by addressing unique crypto risks through data-driven and AI-augmented methods.

Contribution

It extends existing threat models with new components like Hybrid Layer Analysis and CRYPTOQ, specifically targeting blockchain and cryptocurrency risks in fintech environments.

Findings

Supported by 2025 incident data reducing $2.47 billion losses

Demonstrated effectiveness in risk mitigation and asset prioritization

Enhanced security against state-sponsored and cross-chain attacks

Abstract

The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework (CNTMF), a proposed framework designed to address the risks in these ecosystems, such as oracle manipulation and cross-chain exploits. CNTMF represents a proposed extension of established methodologies like STRIDE, OWASP Top 10, NIST frameworks, LINDDUN, and PASTA, while incorporating tailored components including Hybrid Layer Analysis, the CRYPTOQ mnemonic for cryptocurrency-specific risks, and an AI-Augmented Feedback Loop. Drawing on real-world data from 2025 incidents, CNTMF supports data-driven mitigation to reduce losses, which totalled approximately $2.47 billion in the first half of 2025 across 344 security events (CertiK via GlobeNewswire, 2025; Infosecurity Magazine, 2025). Its phases guide asset mapping, risk profiling, prioritisation, mitigation, and iterative feedback. This supports security against evolving risks like state-sponsored attacks.