Our new academic Paper
Title: TM-RUGPULL: A TEMPORALLY SOUND, MULTIMODAL
DATASET FOR EARLY DETECTION OF RUG PULLS ACROSS THE
TOKENIZED ECOSYSTEM
Abstract: Rug-pull attacks pose a systemic threat across the blockchain ecosystem, yet research into early
detection is hindered by the lack of scientific-grade datasets. Existing resources often suffer from
temporal data leakage, narrow modality, and ambiguous labeling—particularly outside DeFi contexts.
To address these limitations, we present TM-RugPull , a rigorously curated, leakage-resistant
dataset of 1,028 token projects spanning DeFi, meme coins, NFTs, and celebrity-themed tokens.
RugPull enforces strict temporal hygiene by extracting all features—on-chain behavior, smart contract
metadata, and OSINT signals—strictly from the first half of each project’s lifespan. Labels are
grounded in forensic reports and longevity criteria, verified through multi-expert consensus. This
dataset enables causally valid, multimodal analysis of rug-pull dynamics and establishes a new
benchmark for reproducible fraud detection research.
Full Paper: Link
Leave a Reply