Unveiling the Risks of NFT Promotion Scams

• URL: http://arxiv.org/abs/2301.09806v3
• Date: Mon, 11 Sep 2023 05:34:55 GMT
• Title: Unveiling the Risks of NFT Promotion Scams
• Authors: Sayak Saha Roy, Dipanjan Das, Priyanka Bose, Christopher Kruegel, Giovanni Vigna, Shirin Nilizadeh
• Abstract summary: We study 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects. More than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We develop a machine learning tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.
• Score: 24.54041279375181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth in popularity and hype surrounding digital assets such as art, video, and music in the form of non-fungible tokens (NFTs) has made them a lucrative investment opportunity, with NFT-based sales surpassing $25B in 2021 alone. However, the volatility and general lack of technical understanding of the NFT ecosystem have led to the spread of various scams. The success of an NFT heavily depends on its online virality. As a result, creators use dedicated promotion services to drive engagement to their projects on social media websites, such as Twitter. However, these services are also utilized by scammers to promote fraudulent projects that attempt to steal users' cryptocurrency assets, thus posing a major threat to the ecosystem of NFT sales. In this paper, we conduct a longitudinal study of 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects through giveaway competitions over a period of two months. Our findings reveal that more than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We also found that a majority of accounts engaging with these promotions (including those for fraudulent NFT projects) are bots that artificially inflate the popularity of the fraudulent NFT collections by increasing their likes, followers, and retweet counts. This manipulation results in significant engagement from real users, who then invest in these scams. We also identify several shortcomings in existing anti-scam measures, such as blocklists, browser protection tools, and domain hosting services, in detecting NFT-based scams. We utilized our findings to develop a machine learning classifier tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.

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