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Automated Smart Contract Vulnerability Detection using Fine-tuned Large Language Models
Open accessAbstract
As decentralized finance (DeFi) built on blockchain grows rapidly, the security of smart contracts underpinning DeFi has become a major concern due to exploits leading to billions in damages. Although tools exist for automated vulnerability detection in smart contracts, studies show that most vulnerabilities remain undetected. In this work, we propose using fine-tuned large language models (LLMs) for enhanced automated detection of vulnerabilities in smart contracts. We collected over 26,727 labeled smart contract vulnerabilities and fine-tuned the 13B parameter Llama-2 model. Evaluation of 1,000 unseen functions shows promising precision of 31-36% in predicting vulnerability categories. The fine-tuned LLM demonstrates potential as an auxiliary tool to identify vulnerable code and assist auditors. Future work is outlined for improving performance via larger models, higher-quality data, and specialized binary detection models. We present promising preliminary results on integrating LLMs into smart contract analysis and motivate further research at the intersection of LLMs and blockchain security.
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“Multi-agent smart-contract auditors [119,121-123,125] now match or exceed expert auditors on standard benchmarks.”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [122]
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Cited by
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Nageshwaran, V., & Ezekiel, S. (2026) · Electronics · doi:10.3390/electronics15122740 · Cited by 4
Metadata
- Authors
- Yang, Z., Man, G., & Yue, S.
- Affiliations
- Seattle University (US)
- Arizona State University (US)
- University of Wisconsin–Platteville (US)
- Venue
- Proceedings of the 2023 6th International Conference on Blockchain Technology and Applications
- Publisher
- ACM
- Published
- 2023-12-15
- Pages
- 19-23
- OpenAlex
- W4399117385
- Cited by
- 10
- References
- 3
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 2.57
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- smart contract vulnerability detection, large language models, Llama 2, fine-tuning, decentralized finance, blockchain security, vulnerability classification, binary analysis
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