Blockchain Security Risk Assessment in Quantum Era, Migration Strategies, and Proactive Defense
Bibliographic record
Abstract
The advent of Quantum Computing (QC) poses significant threats to the cryptographic foundations of Blockchain (BC) systems, as quantum algorithms like Shor’s and Grover’s undermine the security of public-key cryptography and hash functions. This research conducts a comprehensive risk assessment of quantum vulnerabilities across critical BC components, including consensus mechanisms, smart contracts, and digital wallets. Leveraging the STRIDE threat modeling framework, we analyze threat vectors specific to QC, identifying key areas most susceptible to quantum-enabled attacks, such as private key compromise, consensus disruptions, and smart contract integrity risks. Our contributions provide actionable mitigation strategies, including a detailed security blueprint for quantum resilience, encompassing the integration of Post-Quantum Cryptography (PQC) and the adoption of quantum-resistant hash functions. We offer implementation best practices, focusing on key management, secure coding, and network security to strengthen BC components against quantum threats. To mitigate the risk of QC during transition from classical to quantum-resistant BCs, we present two hybrid BC architectures. As part of a comprehensive quantum resilience strategy, these architectures facilitate a secure and scalable migration by integrating platform-specific adaptations that balance security, adaptability, and operational efficiency. Our analysis extends to major BC platforms, including Bitcoin, Ethereum, Ripple, Litecoin, and Zcash, providing platform-specific vulnerability assessments and highlighting unique weaknesses in the quantum era. By identifying vulnerabilities, developing proactive defense strategies, and adopting a structured hybrid migration approach, this research equips BC stakeholders with a robust framework to achieve long-term quantum resilience. Finally, we explore challenges and research directions for integrating emerging technologies, including quantum machine learning, Artificial Intelligence (AI), and Web3, with BC systems, and discuss new threats that may arise from this convergence in the QC era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".