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Nicholas Huppert

Publications and source records attributed to Nicholas Huppert.

2 recordsLinked to original sources

The Anatomy of Address Poisoning on Ethereum: Funding Mechanisms, Scam Signatures, and Laundering via Tornado Cash

Address-Poisoning Transfer (APT) is a prevalent blockchain phishing scam in which a scammer poisons a victim's address book by generating a transfer with a phishing address that looks similar to a benign address that the victim has previously interacted with. Although simple, APT phishing attacks have cost users millions of dollars in recent years, which has captured the attention of the research community (Ye et al. WWW'24, Guan-Li CCS'24, Chen et al. NDSS'25, Tsuchiya et al. USENIX'25). In this work, we go beyond detection and investigate three important and underexplored aspects of APT: scam funding mechanisms, scam signatures, and scam proceeds laundering via public services. In particular, we propose five families of scam signatures that capture key aspects of APT operations, which are useful for address clustering. We also conduct the first investigation into usage of Tornado Cash for funding APTs and laundering scam proceeds.

cs.CR

Serial Scammers and Attack of the Clones: How Scammers Coordinate Multiple Rug Pulls on Decentralized Exchanges

We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified distinctive scam patterns including star, chain, and major (scam-funding) flow. These patterns, which collectively cover about $40\%$ of all scammer addresses in our datasets, reveal typical ways scammers run multiple Rug Pulls and organize the money flow among different addresses. We then studied the more general concept of scam cluster, which comprises scammer addresses linked together via direct ETH/BNB transfers or behind the same scam pools. We found that scam token contracts are highly similar within each cluster (average similarities $>70\%$) and dissimilar across different clusters (average similarities $<30\%$), corroborating our view that each cluster belongs to the same scammer/scam organization. Lastly, we analyze the scam profit of individual scam pools and clusters, employing a novel cluster-aware profit formula that takes into account the important role of wash traders. The analysis shows that the existing formula inflates the profit by at least $32\%$ on Uniswap and $24\%$ on Pancakeswap.

cs.CR