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Understanding Rug Pulls: An In-Depth Behavioral Analysis of Fraudulent NFT Creators

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arxiv 2304.07598 v1 pith:IFRRSK27 submitted 2023-04-15 cs.CR

classification cs.CR
keywords pullscreatorsfraudulentanalysisbehavioralidentifyin-depthprojects
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The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles, but also an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, which are fraudulent schemes aimed at stealing investors' funds. Using data from 758 rug pulls across 10 NFT marketplaces, we examine the structural and behavioral properties of these schemes, identify the characteristics and motivations of rug-pullers, and classify NFT projects into groups based on creators' association with their accounts. Our findings reveal that repeated rug pulls account for a significant proportion of the rise in NFT-related cryptocurrency crimes, with one NFT collection attempting 37 rug pulls within three months. Additionally, we identify the largest group of creators influencing the majority of rug pulls, and demonstrate the connection between rug-pullers of different NFT projects through the use of the same wallets to store and move money. Our study contributes to the understanding of NFT market risks and provides insights for designing preventative strategies to mitigate future losses.

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

  1. Cybersquatting in Web3: The Case of NFT

    cs.CR 2025-04 conditional novelty 6.0 of 10

    A measurement of 220K Ethereum NFT collections identifies 8,019 name-squatting collections imitating 654 popular projects, with $59.26M in associated mint fees and creator royalties.

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