Security Analysis and Formal Verification on Blockchain and its Applications
Bibliographic record
Abstract
Blockchains have become an integrated part of our finance infrastructures. Being monetary yet fully automated, blockchains and their applications are unanimously deemed impracticable before undergoing necessary verification. This monograph reviews the previous attempts at verifying two fundamental properties of blockchains: correctness (where flaws lead to unintentional damages) and security (where vulnerabilities incur attacks and losses). First, it summarizes and categorizes the correctness and security flaws encountered by real-world blockchains. Second, it systematizes the development of formal verification to address the flaws in blockchains, covering the aspects of models, specifications, and techniques. Third, it unveils the progress of security analysis for mitigating the flaws, unveiling the analysis principles being followed, the flaw oracles being devised, and the detection methods being used. Finally, it summarizes the challenges remaining to be addressed, followed by our vision of the trend in the near future. Throughout this monograph, we anticipate shedding light on future blockchain verification advances, especially in expanding its applicability, making specification generation easier, and discovering previously unknown vulnerabilities. By identifying gaps such as missing tools for infrastructure-level components and the difficulty of writing formal specifications, this work aims to motivate the development of more automated, intelligent, and practical verification frameworks.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".