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Stop Pulling my Rug: Exposing Rug Pull Risks in Crypto Token to Investors | IEEE Conference Publication | IEEE Xplore

Stop Pulling my Rug: Exposing Rug Pull Risks in Crypto Token to Investors


Abstract:

Crypto token is a digital asset used in blockchain-based decentralized applications. Today, tokens have attracted many investors and collected a large amount of money. Un...Show More

Abstract:

Crypto token is a digital asset used in blockchain-based decentralized applications. Today, tokens have attracted many investors and collected a large amount of money. Unfortunately, the booming token market has simultaneously spawned numerous fraudulent schemes. Rug pull is one of the well-known scams, where fraudulent developers lure investors into seemingly profitable projects and then run off with their money, leaving the investors with worthless assets. To prevent future losses, researchers in both industry and academia have attempted to expose rug pull risks in advance. However, rug pull can manifest in various scenarios during the transfer process, posing significant challenges for effective detection. In this paper, we first conduct an in-depth study of 201 real-world rug pull incidents for their root causes, and summarize 4 common types of rug pull risks. Then, we establish a component-configurable transfer model to locate and analyze the transfer process in token contracts. Based on the model, we generate effective oracles for the 4 rug pull risks, which can overcome the interference of diverse implementation structures. We propose Tokeer, a token verification tool that implements the transfer model and oracles with datalog technique to expose rug pull risks hidden in token contracts. We apply Tokeer on real-world tokens and compare it with state-of-the-art tools: the commercial tool GoPlus and the academic tool Pied-Piper. Tokeer achieves an average of 98.0% recall and 98.9% precision, and successfully detects 27.2% more real rug pull risks in wild production, significantly outperforming the state-of-the-art tools in terms of detection accuracy and effectiveness.
Date of Conference: 14-20 April 2024
Date Added to IEEE Xplore: 18 June 2024
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Conference Location: Lisbon, Portugal

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