eTainter: detecting gas-related vulnerabilities in smart contracts
Why this work is in the frame
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Bibliographic record
Abstract
The execution of smart contracts on the Ethereum blockchain consumes gas paid for by users submitting contracts' invocation requests. A contract execution proceeds as long as the users dedicate enough gas, within the limit set by Ethereum. If insufficient gas is provided, the contract execution halts and changes made during execution get reverted. Unfortunately, contracts may contain code patterns that increase execution cost, causing the contracts to run out of gas. These patterns can be manipulated by malicious attackers to induce unwanted behavior in the targeted victim contracts, e.g., Denial-of-Service (DoS) attacks. We call these gas-related vulnerabilities. We propose eTainter, a static analyzer for detecting gas-related vulnerabilities based on taint tracking in the bytecode of smart contracts. We evaluate eTainter by comparing it with the prior work, MadMax, on a dataset of annotated contracts. The results show that eTainter outperforms MadMax in both precision and recall, and that eTainter has a precision of 90% based on manual inspection. We also use eTainter to perform large-scale analysis of 60,612 real-world contracts on the Ethereum blockchain. We find that gas-related vulnerabilities exist in 2,763 of these contracts, and that eTainter analyzes a contract in eight seconds, on average.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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