MF2LLM: A Multiview Multimodal Fusion Framework With Large Language Models for Ponzi Scheme Detection on Ethereum

Abstract

The rapidly expanding Ethereum ecosystem has driven the flourishing of decentralized applications, but has also brought increasingly severe security risks. Ponzi scheme, in particular, pose a grave threat to platform security and user assets by luring investors with promises of high returns. The current detection methods generally suffer from limitations such as insufficient feature extraction, reliance on a single information source, and poor robustness. To address these challenges, this paper proposes a novel Multi-View Multi-Modal Fusion Framework with Large Language Models for Ponzi scheme detection on Ethereum, named MF2LLM. We first model the contract opcode sequence as an opcode chain graph and design a Time-Stamped Graph Encoder (TS-GE) to capture local temporal dependencies and execution flow relationships between opcodes. Concurrently, we construct an opcode semantic hypergraph based on semantic categories and design a Semantic-Weighted Hypergraph Encoder (SW-HGE) to model higher-order co-occurrence patterns and global associative features. Furthermore, we propose the Opcode Sequence Lightweighting (OSL) method, which significantly compresses the length of opcode sequences while preserving core control logic and semantic information. This provides high-quality structured input for information fusion. To this end, we perform multi-modal instruction fusion on multi-source heterogeneous features and employ LoRA to fine-tune LLMs. This enables the model to achieve cross-modal semantic reasoning and behavioural pattern recognition. Through extensive experimental validation on real-world datasets, MF2LLM demonstrates stable and superior detection performance even under conditions of highly imbalanced sample distributions. Compared to existing state-of-the-art approaches, our method outperforms across all metrics, achieving an ACC of 99.43%, Precision of 96.57%, Recall of 97.06%, and an F1-score of 96.81%. The efficiency and practical value of MF2LLM in detecting Ponzi schemes on Ethereum contribute to enhanced security for the decentralized application ecosystem. The codes are publicly available on Github: https://github.com/yemisua/MF2LLM.