Nyx: Detecting Exploitable Front-Running Vulnerabilities in Smart Contracts
Bibliographic record
Abstract
Smart contracts are susceptible to front-running attacks, in which malicious users leverage prior knowledge of upcoming transactions to execute attack transactions in advance and benefit their own portfolios. Existing contract analysis techniques raise a number of false positives and false negatives in that they simplistically treat data races in a contract as front-running vulnerabilities and can only analyze contracts in isolation. In this work, we formalize the definition of exploitable front-running vulnerabilities based on previous empirical studies on historical attacks, and present Nyx, a novel static analyzer to detect them. Nyx features a Datalog-based preprocessing procedure that efficiently and soundly prunes a large part of the search space, followed by a symbolic validation engine that precisely locates vulnerabilities with an SMT solver. We evaluate Nyx using a large dataset that comprises 513 real-world front-running attacks in smart contracts. Compared to six state-of-the-art techniques, Nyx surpasses them by 32.64%-90.19% in terms of recall and 2.89%-70.89% in terms of precision. Nyx has also identified four zero-days in real-world smart contracts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".