Automated and Accurate Token Transfer Identification and Its Applications in Cryptocurrency Security
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
Cryptocurrency tokens, implemented by smart contracts, are prime targets for attackers due to their substantial monetary value. To illicitly gain profit, attackers often embed malicious code or exploit vulnerabilities within token contracts. Token transfer identification is crucial for detecting malicious and vulnerable token contracts. However, existing methods suffer from high false positives or false negatives due to invalid assumptions or reliance on limited patterns. This paper introduces a novel approach that captures the essential principles of token contracts, which are independent of programming languages and token standards, and presents a new tool, CRYPTO-SCOUT. CRYPTO-SCOUT automatically and accurately identifies token transfers, enabling the detection of various malicious and vulnerable token contracts. CRYPTO-SCOUT's core innovation is its capability to automatically identify complex container-type variables used by token contracts for storing holder information. It processes the bytecode of smart contracts written in the two most widely-used languages, Solidity and Vyper, and supports the three most popular token standards, ERC20, ERC721, and ERC1155. Furthermore, CRYPTO-SCOUT detects four types of malicious and vulnerable token contracts and is designed to be extensible. Extensive experiments show that CRYPTO-SCOUT outperforms existing approaches and uncovers over 21,000 malicious/vulnerable token contracts and more than 12,000 transactions triggering them.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 it