Discovering NFT Rug Pulls: Matching Behavior Patterns
Using Graph Isomorphism Networks
Sandeep K Shukla,Trishie Sharma
ACM Transactions on Internet Technology, ACM-TIT, 2025
@inproceedings{bib_Disc_2025, AUTHOR = {Sandeep K Shukla, Trishie Sharma}, TITLE = {Discovering NFT Rug Pulls: Matching Behavior Patterns
Using Graph Isomorphism Networks}, BOOKTITLE = {ACM Transactions on Internet Technology}. YEAR = {2025}}
Amid the surge of Non-Fungible Tokens (NFTs) in blockchain, this study introduces a meticulous methodology focusing on transaction behaviors to unveil rug pulls — a critical issue impacting financial security
and trust in the NFT landscape. Using a Graph Isomorphism Network (GIN) model with 6 behavioral patterns obtained from transaction sequences, we create a “Rug Pull Pattern Matcher” model. We provide a
comprehensive analysis by applying the model on two datasets — creator’s transactions from 50 reputable
NFT projects and 32 reported rug pulls. Our work utilizes automated labeling to categorize addresses and our
analysis reveals several interconnected NFT creator activities. We present an in-depth mapping of fund flows
and creator interactions exposing suspicious behaviors like artificial inflation and intricate network collaborations among creators. The results of our proposed model demonstrate the efficacy of our methodology
with 75.4% accuracy and 85.9% precision on the dataset of reported rug pulls. This work provides comparative analyses of genuine and malicious creator networks to elucidate their structural differences, helping to
identify genuine and potentially fraudulent NFT activities.