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 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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".