# DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on Graphs

arXiv: https://arxiv.org/abs/2301.13577 · HTML: https://mlsanity.com/paper/2301.13577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent](https://mlsanity.com/paper/2601.07263/) (2026)
- [PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites](https://mlsanity.com/paper/2510.16083/) (2025)

## Abstract (arXiv)

As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called textitNFT drainers. Over the last year, 100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design textitDRAINCLoG, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of textitDRAINCLoG under a wide variety of evasion attacks.