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A Deep Dive into NFT Rug Pulls

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arxiv 2305.06108 v1 pith:77IR7AFV submitted 2023-05-10 cs.CR

classification cs.CR
keywords pullsprojectspullscamfirstgroundonessystem
verification ladder T0 review T1 audit T2 compute T3 formal

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NFT rug pull is one of the most prominent type of scam that the developers of a project abandon it and then run away with investors' funds. Although they have drawn attention from our community, to the best of our knowledge, the NFT rug pulls have not been systematically explored. To fill the void, this paper presents the first in-depth study of NFT rug pulls. Specifically, we first compile a list of 253 known NFT rug pulls as our initial ground truth, based on which we perform a pilot study, highlighting the key symptoms of NFT rug pulls. Then, we enforce a strict rule-based method to flag more rug pulled NFT projects in the wild, and have labelled 7,487 NFT rug pulls as our extended ground truth. Atop it, we have investigated the art of NFT rug pulls, with kinds of tricks including explicit ones that are embedded with backdoors, and implicit ones that manipulate the market. To release the expansion of the scam, we further design a prediction model to proactively identify the potential rug pull projects in an early stage ahead of the scam happens. We have implemented a prototype system deployed in the real-world setting for over 5 months. Our system has raised alarms for 7,821 NFT projects, by the time of this writing, which can work as a whistle blower that pinpoints rug pull scams timely, thus mitigating the impacts.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Real-CATS: A Practical Training Ground for Emerging Research on Cryptocurrency Cybercrime Detection

    cs.CR 2025-01 conditional novelty 7.0 of 10

    Real-CATS provides 103,203 criminal and 106,196 benign cryptocurrency addresses on Bitcoin and Ethereum, with transaction profiles and a temporal test set for cybercrime detection research.

  2. RPHunter: Unveiling Rug Pull Schemes in Crypto Token via Code-and-Transaction Fusion Analysis

    cs.SE 2025-06 conditional novelty 6.0 of 10

    RPHunter fuses a code-risk graph and a transaction-behavior graph with graph neural networks to detect rug pull tokens, reporting 94.5% F1 on a curated dataset and 90.7% precision on a real-world sample.

  3. Rugsafe: A multichain protocol for recovering from and defending against Rug Pulls

    cs.CR 2025-07 reject novelty 4.0 of 10

    A protocol white paper that asserts an inverse logarithmic peg between rugged tokens and newly minted 'anticoin' receipts, without implementation or a specified mechanism that would enforce the peg.

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