CryptoNeo Threat Modelling Framework (CNTMF)

• The CryptoNeo Threat Modelling Framework is a security framework addressing the risks faced by neobanks and fintechs in blockchain ecosystems, combining traditional methods with innovative tools.
• CNTMF introduces Hybrid Layer Analysis, CRYPTOQ mnemonic, and an AI-Augmented Feedback Loop for assessing risks from traditional and decentralized financial systems.
• This framework helps prioritize mitigation by leveraging quantitative risk assessments, incident data, and evolving real-world threat scenarios to enhance security measures.

The CryptoNeo Threat Modelling Framework (CNTMF) is a security framework developed to address the multifaceted risks confronting neobanks and fintech companies operating in integrated blockchain ecosystems. CNTMF extends, refines, and synthesizes established methodologies—including STRIDE, OWASP Top 10, NIST, LINDDUN, and PASTA—to address new threat classes and workflows unique to environments that blend traditional financial infrastructure with decentralized blockchain and Web3 components (Bahar, 18 Jul 2025). It introduces novel methods such as Hybrid Layer Analysis, the CRYPTOQ mnemonic specific to cryptocurrency risk, and an AI-Augmented Feedback Loop to support continuous, data-driven security posture improvement.

1. Rationale and Positioning of CNTMF

The emergence of digital banks and the rapid incorporation of decentralized finance (DeFi), cryptocurrencies, and Web3 technologies into the financial sector have significantly expanded the attack surface and threat landscape. Conventional frameworks (e.g., STRIDE, NIST, LINDDUN) offer robust approaches for generalized risk modeling but do not fully address threats such as oracle manipulation, cross-chain exploits, sophisticated social engineering in tokenized environments, or regulatory challenges associated with evolving compliance regimes such as MiCA and OFAC (Bahar, 18 Jul 2025). CNTMF was conceived in response to real-world events and risk data from 2025, where $2.47 billion in losses across 344 incidents underscored gaps in prevailing methodologies.

2. Distinctive Components of the Framework

CNTMF's architecture is defined by a set of extensions and integrations:

• Hybrid Layer Analysis: The framework employs enhanced Data Flow Diagrams (DFDs) and a "Hybrid Asset Matrix" to catalog assets and interdependencies spanning conventional presentation/API layers, blockchain operations, custodial/non-custodial wallets, oracles, and cross-chain bridges. This multi-layered mapping supports identification of risk propagation channels overlooked by single-layer analyses.
• CRYPTOQ Mnemonic: CNTMF introduces a risk profiling mnemonic that targets cryptocurrency-specific threat vectors:
  • C: Collusion (e.g., multi-party computation breaches)
  • R: Reentrancy/Oracle Manipulation
  • Y: Yield Farming Exploits (DeFi-specific)
  • P: Phishing/Social Engineering (notable for large-scale losses in 2025)
  • T: Tokenisation Risks (including governance attacks)
  • O: Off-Chain Data Poisoning (especially ML-driven vulnerabilities)
  • Q: Quantum Threats (anticipating future breaks in cryptosystems)
  • This mnemonic augments and intersects with standard risk categories articulated in STRIDE and OWASP (Bahar, 18 Jul 2025).
• AI-Augmented Feedback Loop: Adaptive learning and rapid response are enabled via an automated loop that incorporates post-incident outcomes, detection signals from bug bounties and penetration tests, ML-driven anomaly detection, and risk model recalibration. Input pipelines are hardened to resist adversarial ML and data poisoning.

3. Emergent Methodological Phases

CNTMF operationalizes its methodology through five interconnected phases:

1. Asset Identification and Mapping: Core and peripheral assets are mapped in extended DFDs and a Hybrid Asset Matrix. Interdependencies—such as between fiat onramps and blockchain bridge mechanisms—are explicitly documented.
2. Risk Actor and Vector Profiling: Threat actors are categorized (state-sponsored entities, insider threats, phishing groups). Risk vectors are derived both from established (STRIDE, OWASP) and cryptocurrency-specific sources using the CRYPTOQ mnemonic.
3. Risk Assessment and Prioritisation: Threats are quantified using an expanded formula:

   $R_s = \left( T_s + E_i + R_c \right) \times P_e$

   with $T_s$ (technical severity, e.g., CVSS 4.0), $E_i$ (economic impact, 0–10 scale), $R_c$ (regulatory consequence, 0–10 scale), and $P_e$ (exploit probability, 0–1) (Bahar, 18 Jul 2025). The result supports a "Web3 Risk Heatmap" for visualization and prioritization.

4. Mitigation Design and Implementation: Controls are tailored to the risk context. Implementation spans firewalls and zero-trust architectures (fiat layers), multi-party computation (custodial management), zk-SNARKs (privacy), and dynamic application testing (smart contracts). Automation utilizes policy enforcement engines such as Open Policy Agent (OPA) integrated into CI/CD pipelines.
5. Ecosystem Feedback Loop: Post-mitigation feedback is systematically ingested. Incident data, bug bounty scripts, and auto-generated ML insights collaboratively drive continuous framework evolution, aligning with established metrics such as Mean Time to Remediate (MTTR) and OWASP SAMM maturity scores.

4. Integration with Prior Research, Standards, and Feedback Mechanisms

CNTMF’s structure is grounded in—and substantially extends—the following pre-existing frameworks:

• STRIDE: Ensures comprehensive risk categorization by mapping spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege attacks.
• OWASP Top 10 & API Security: Directs focus on prevalent web and API vulnerabilities within finance ecosystems.
• NIST Guidelines: Informs tamper-resistant ledger design, secure supply chains, and best practices for cryptographic integrity.
• LINDDUN: Aids in systematized privacy risk assessment, especially with regards to linkability and identifiability in tokenized systems.
• PASTA: Provides an iterative and business-aligned foundation for continuous risk analysis.

The feedback loop concept draws on approaches such as the Metric-Based Feedback Methodology (MBFM) (Bahar, 2023), which advocates the integration of vulnerability data from bug bounty programs and operational incidents back into the risk model. By systematically tagging vulnerabilities (e.g., via the SWC Registry and SCSVS for smart contracts), the model enables precise root cause analysis and adaptive security posture refinement.

5. Application to Real-World Security Events

CNTMF’s relevance is evidenced by its analysis of empirical incident data from 2025:

• Incident typology: Infrastructure attacks, oracle manipulation, insider exploits, and cross-chain bridge attacks have each yielded substantial losses (with, e.g., a single incident causing ~$1.5 billion in losses).
• Attack vectors: The blend of DeFi integrations and fiat systems in digital banking platforms exposes threats that disproportionately affect custodial wallets, oracles, and smart contract gateways.
• Threat actor profile: State-sponsored groups accounted for an estimated 70% of value stolen in H1 2025, further highlighting the need for agile, evolving defenses.

Through deployment of data-driven mitigation (e.g., risk scoring models, prioritized heatmaps), rapid remediation of high-likelihood and high-impact risks is targeted. Automated controls and real-time feedback mechanisms support resilience against new exploitation tactics, especially as attack methodologies evolve rapidly in the financial sector.

6. Quantitative and Formal Models

CNTMF leverages quantitative risk assessment to support informed decision-making:

Variable	Description	Scale/Type
$T_s$$</td> <td>Technical Severity</td> <td>CVSS 0–10</td> </tr> <tr> <td>$$E_i$$</td> <td>Economic Impact</td> <td>0–10</td> </tr> <tr> <td>$$R_c$$</td> <td>Regulatory Consequence</td> <td>0–10</td> </tr> <tr> <td>$$P_e$$</td> <td>Exploit Probability</td> <td>0–1</td> </tr> <tr> <td>$$R_s$$</td> <td>Risk Score (computed)</td> <td>Numeric</td> </tr> </tbody></table></div> <p>An example from the model: for a high-impact oracle tampering scenario, suppose$$T_s = 8.5, E_i = 7.0, R_c = 6.0, P_e = 0.6$. The risk score is:</p> <p>$(8.5 + 7.0 + 6.0)\times 0.6 = 12.9$ This high score necessitates prioritized mitigation and dynamic policy adjustment (Bahar, 18 Jul 2025). 7. Implications and Comparative Perspective Compared to foundational models such as ABC (Almashaqbeh et al., 2019), which introduced asset-centric categorization and collusion matrices for cryptocurrency threat modeling, CNTMF advances the field by: Embedding direct support for real-world feedback, such as ML-driven detection and bug bounty exploitation data, Systematically addressing hybridized environments that combine centralized and decentralized financial systems, Providing actionable, risk-weighted quantitative assessment for dynamic prioritization, Ensuring regulatory alignment in a global, rapidly shifting compliance context. CNTMF’s hybrid approach, combining heuristics, quantitative scoring, and adaptive feedback, positions it as a comprehensive next-generation framework for securing digital banks and fintech platforms that operate at the intersection of fiat and decentralized finance. Table: CNTMF Phases and Key Artifacts Phase Main Artifacts/Tasks Techniques/Inputs Asset Identification Hybrid Asset Matrix, DFDs Mapping tools, system inventories Risk Profiling Actor and CRYPTOQ vector catalog STRIDE, CRYPTOQ, Empirical threat data Risk Assessment Web3 Risk Heatmap, quantitative scoring CVSS 4.0, probability-impact models Mitigation Control blueprints, deployment policy sets SAST/DAST, policy automation (OPA, etc.) Feedback Loop Incident/Bug Bounty analytics, updated models ML anomaly detection, audit reports Follow-up Questions How does CNTMF's CRYPTOQ mnemonic address new threat vectors specific to DeFi and cross-chain integrations that are less emphasized in traditional frameworks like STRIDE or LINDDUN? In what ways does the AI-Augmented Feedback Loop in CNTMF improve the detection, mitigation, and adaptation to evolving threats compared to conventional feedback mechanisms in security frameworks? How does the quantitative risk calculation model in CNTMF facilitate prioritization of responses across hybrid (fiat and DeFi) financial infrastructures, and how does this approach differ from those used in ABC or PASTA? What challenges are involved in integrating automated policy enforcement mechanisms, such as OPA, into CI/CD pipelines for decentralized financial applications, and how does CNTMF address these challenges? Find recent papers about threat modeling and risk assessment frameworks for blockchain-integrated financial platforms. Related Topics NIST AI Risk Management Framework Foundation Model Supply Chain Security Phase Exposure Model Hybrid AI-Driven Cybersecurity Framework Hybrid Cyber-AI Attacks CRYPTOQ Mnemonic: Crypto Risk Taxonomy Frontier AI Risk Management Framework (SafeWork-F1) Hybrid AI Threats Overview Contract-Related Rug Pull in DeFi Honeypot Smart Contracts × Stay informed about trending AI/ML papers: About | Updates | Terms | Privacy | RSS | Contact | Twitter | Discord