When They Go Low: Automated Replacement of Low-level Functions in Ethereum Smart Contracts
Bibliographic record
Abstract
Smart contracts in the Ethereum blockchain are typically written using a high-level, Turing-complete language called Solidity. However, the Solidity language has many features to allow programmers fine-grained control over their smart contracts. We call these features low-level functions. Unfortunately, the improper use of low-level functions can lead to security vulnerabilities leading to heavy financial losses. Therefore, the Solidity community has suggested alternatives for the low-level functions in the official guidelines for developers. We first perform a large-scale empirical study on the use of low-level functions in Ethereum smart contracts written in Solidity. We find that such functions are widely used in real-world Ethereum smart contracts, and that the majority of these uses are gratuitous for the smart contract's functionality. We then propose GoHigh, a source-to-source transformation tool to eliminate low-level function-related vulnerabilities, by replacing low-level functions with high-level alternatives. We evaluate GoHigh on over 300,000 real-world smart contracts on the Ethereum blockchain. GoHigh replaces all low-level functions that are amenable to replacement in the contracts with 17% fewer compiler warnings, and the externally-visible behaviors of at least 92 % of the replaced contracts are identical to the original ones. Finally, GoHigh takes 7 seconds on average per contract.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".