A large‐scale empirical study of low‐level function use in Ethereum smart contracts and automated replacement
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
Abstract The Ethereum blockchain stores and executes complex logic via smart contracts written in Solidity, a high‐level programming language. The Solidity language (in its early versions) provides features to exercise fine‐grained control over smart contracts, whose usage is discouraged by later‐released Solidity documentation, but nonetheless supported in later versions for backward compatibility. We define these features as low‐level functions. However, the high‐volume of transactions and the improper use of low‐level functions lead to security exploits with heavy financial loss. Consequently, the documentation suggests secure alternatives to the use of low‐level functions. In this article, we first perform an empirical study on the use of low‐level functions in Ethereum smart contracts. We study a smart contract dataset consisting of over 2,100,000 real‐world smart contracts. We find that low‐level functions are widely used and that the majority of these uses are gratuitous. We then propose GoHigh, a source‐to‐source transformation tool to eliminate low‐level function‐related vulnerabilities, by replacing low‐level functions with secure alternatives. Our experimental evaluation on the dataset shows that GoHigh successfully replaces all low‐level functions with 4.9% fewer compiler warnings. Further, no unintended side‐effects are introduced in 80% of the contracts, and the remaining 20% are not verifiable due to their external dependency. GoHigh saves more than 5% of the gas cost of the contract. Finally, GoHigh takes 7 s on average per contract.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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