Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models
This work addresses security vulnerabilities in decentralized applications for blockchain developers and users, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting malicious developer intents in smart contracts to prevent security threats and economic losses, achieving an F1 score of 0.9279 and outperforming prior models, including a 65.5% relative improvement over GPT-4.1.
Malicious developer intents in smart contracts constitute significant security threats to decentralized applications, leading to substantial economic losses. Prior work introduced SmartIntentNN, a deep learning model for detecting unsafe developer intents. By combining the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network, the model achieved an F1 score of 0.8633 on an evaluation set of 10,000 real-world smart contracts across ten distinct intent categories. This paper presents SmartIntentV2 (Smart Contract Intent Neural Network Version 2). The primary enhancement is the integration of a BERT-based pre-trained programming language model, which we domain-adaptively pre-train on a dataset of 16,000 real-world smart contracts using a Masked Language Modeling objective. SmartIntentV2 retains the BiLSTM-based multi-label classification network for intent detection. On the same evaluation set of 10,000 smart contracts, it achieves superior performance with an accuracy of 0.9789, precision of 0.9090, recall of 0.9476, and an F1 score of 0.9279, substantially outperforming its predecessor and other baseline models. Notably, SmartIntentV2 also delivers a 65.5% relative improvement in F1 score over GPT-4.1 on this specialized task. These results establish SmartIntentV2 as a new state-of-the-art model for smart contract intent detection.