No More Hidden Pitfalls? Exposing Smart Contract Bad Practices with LLM-Powered Hybrid Analysis
Year: 2026
Type: article
Abstract: As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this article introduces the first systematic study of bad practices in smart contracts, delving into over 47 specific issues. Specifically, we propose Smart Contract Audit Language Model ( SCALM) , an LLM-powered framework featuring two methodological innovations: (1) A hybrid architecture that combines context-aware function-level slicing with knowledge-enhanced semantic reasoning via extensible vectorized pattern matching. (2) A multi-layer reasoning verification system connects low-level code patterns with high-level security principles through syntax, design patterns, and architecture analysis. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.
DOI: 10.1145/3795692
Authors: Xiaoqi Li, Zongwei Li, Wenkai Li, Yuqing Zhang, Xin Wang
Institutions: Hainan University, University of Chinese Academy of Sciences
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7127376086