No More Hidden Pitfalls? Exposing Smart Contract Bad Practices with LLM-Powered Hybrid Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908801543700480 |
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| author | Li, Xiaoqi Li, Zongwei Li, Wenkai Zhang, Yuqing Wang, Xin |
| author_facet | Li, Xiaoqi Li, Zongwei Li, Wenkai Zhang, Yuqing Wang, Xin |
| contents | 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 paper introduces the first systematic study of bad practices in smart contracts, delving into over 47 specific issues. Specifically, we propose 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | No More Hidden Pitfalls? Exposing Smart Contract Bad Practices with LLM-Powered Hybrid Analysis Li, Xiaoqi Li, Zongwei Li, Wenkai Zhang, Yuqing Wang, Xin Cryptography and Security 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 paper introduces the first systematic study of bad practices in smart contracts, delving into over 47 specific issues. Specifically, we propose 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. |
| title | No More Hidden Pitfalls? Exposing Smart Contract Bad Practices with LLM-Powered Hybrid Analysis |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2512.15179 |