The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart contracts are self-executing programs that run on the blockchain and make it possible for peers to enforce agreements without a third-party guarantee. The smart contract on Ethereum is the fundamental element of decentralized finance with billions of US dollars in value. Smart contracts cannot be changed after deployment and hence the code needs to be verified for potential vulnerabilities. However, smart contracts are far from being secure and attacks exploiting vulnerabilities that have led to losses valued in the millions. In this work, we explore the current state of smart contracts security, prevalent vulnerabilities, and security-analysis tool support, through reviewing the latest advancement and research published in the past five years. We study 13 vulnerabilities in Ethereum smart contracts and their countermeasures, and investigate nine security-analysis tools. Our findings indicate that a uniform set of smart contract vulnerability definitions does not exist in research work and bugs pertaining to the same mechanisms sometimes appear with different names. This inconsistency makes it difficult to identify, categorize, and analyze vulnerabilities. We explain some safeguarding approaches and best practices. However, as technology improves new vulnerabilities may emerge. Regarding tool support, SmartCheck, DefectChecker, contractWard, and sFuzz tools are better choices in terms of more coverage of vulnerabilities; however, tools such as NPChecker, MadMax, Osiris, and Sereum target some specific categories of vulnerabilities if required. While contractWard is relatively fast and more accurate, it can only detect pre-defined vulnerabilities. The NPChecker is slower, however, can find new vulnerability patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it