AI-Based Vulnerability Analysis of NFT Smart Contracts

• URL: http://arxiv.org/abs/2504.16113v2
• Date: Thu, 24 Apr 2025 08:25:20 GMT
• Title: AI-Based Vulnerability Analysis of NFT Smart Contracts
• Authors: Xin Wang, Xiaoqi Li,
• Abstract summary: This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts.<n>We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn.<n>A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration.
• Score: 6.378351117969227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.

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