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With Trail to Follow: Measurements of Real-world Non-fungible Token Phishing Attacks on Ethereum

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arxiv 2307.01579 v2 pith:6YFNV7FJ submitted 2023-07-04 cs.CR

classification cs.CR
keywords phishingattacksnftsaccountsethereumanalysisanalyzingbeen
verification ladder T0 review T1 audit T2 compute T3 formal
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With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Phishing Detection in Ethereum via Temporal Graph Contrastive Learning

    cs.CR 2026-05 unverdicted novelty 6.0 of 10

    PhishEye uses temporal graph contrastive learning on heterogeneous Ethereum transaction graphs for self-supervised phishing detection, achieving F1 scores of 87.23% for transactions and 94.19% for accounts while ident...

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