TMRugPull: A Temporally Sound Multimodal Dataset for Early RugPull Detection

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Détails bibliographiques
Auteurs principaux:	Shoaei, Fatemeh, Pishdar, Mohammad, Bag-Mohammadi, Mozafar, Karami, Mojtaba, Lagaisse, Bert
Format:	Preprint
Publié:	2026
Sujets:	Cryptography and Security
Accès en ligne:	Acceder al recurso
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author	Shoaei, Fatemeh Pishdar, Mohammad Bag-Mohammadi, Mozafar Karami, Mojtaba Lagaisse, Bert
author_facet	Shoaei, Fatemeh Pishdar, Mohammad Bag-Mohammadi, Mozafar Karami, Mojtaba Lagaisse, Bert
contents	Rug pull is a critical attack in the world of blockchain technology. Despite this, the absence of sufficient time-bound and well-structured datasets is considered one of the significant issues faced while identifying early detection. Existing datasets do not provide the solution to this challenge because of temporal leakage or use of post-collapse indicators, insufficient modality coverage, and confusing or partial labels, especially with regards to DeFi tokens. To solve these problems, we present a highly curated and strictly time-bound dataset called TM-RugPull containing 1,000 projects, which include DeFi, meme, NFT, and celebrity token projects. We achieve temporal validation of the dataset by acquiring all three modalities, namely on-chain behavior, smart contract metadata, and OSINT signals. The project labels are provided based on manual investigation for the entire project's lifespan and its collapse. Also, we make our dataset publicly available together with its codebase for data acquisition and feature extraction.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_21529
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	TMRugPull: A Temporally Sound Multimodal Dataset for Early RugPull Detection Shoaei, Fatemeh Pishdar, Mohammad Bag-Mohammadi, Mozafar Karami, Mojtaba Lagaisse, Bert Cryptography and Security Rug pull is a critical attack in the world of blockchain technology. Despite this, the absence of sufficient time-bound and well-structured datasets is considered one of the significant issues faced while identifying early detection. Existing datasets do not provide the solution to this challenge because of temporal leakage or use of post-collapse indicators, insufficient modality coverage, and confusing or partial labels, especially with regards to DeFi tokens. To solve these problems, we present a highly curated and strictly time-bound dataset called TM-RugPull containing 1,000 projects, which include DeFi, meme, NFT, and celebrity token projects. We achieve temporal validation of the dataset by acquiring all three modalities, namely on-chain behavior, smart contract metadata, and OSINT signals. The project labels are provided based on manual investigation for the entire project's lifespan and its collapse. Also, we make our dataset publicly available together with its codebase for data acquisition and feature extraction.
title	TMRugPull: A Temporally Sound Multimodal Dataset for Early RugPull Detection
topic	Cryptography and Security
url	https://arxiv.org/abs/2602.21529

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