Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917237455060992 |
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| author | Bu, Jiuyang Li, Wenkai Li, Zongwei Zhang, Zeng Li, Xiaoqi |
| author_facet | Bu, Jiuyang Li, Wenkai Li, Zongwei Zhang, Zeng Li, Xiaoqi |
| contents | Decentralized applications (DApps) face significant security risks due to vulnerabilities in smart contracts, with traditional detection methods struggling to address emerging and machine-unauditable flaws. This paper proposes a novel approach leveraging fine-tuned Large Language Models (LLMs) to enhance smart contract vulnerability detection. We introduce a comprehensive dataset of 215 real-world DApp projects (4,998 contracts), including hard-to-detect logical errors like token price manipulation, addressing the limitations of existing simplified benchmarks. By fine-tuning LLMs (Llama3-8B and Qwen2-7B) with Full-Parameter Fine-Tuning (FFT) and Low-Rank Adaptation (LoRA), our method achieves superior performance, attaining an F1-score of 0.83 with FFT and data augmentation via Random Over Sampling (ROS). Comparative experiments demonstrate significant improvements over prompt-based LLMs and state-of-the-art tools. Notably, the approach excels in detecting non-machine-auditable vulnerabilities, achieving 0.97 precision and 0.68 recall for price manipulation flaws. The results underscore the effectiveness of domain-specific LLM fine-tuning and data augmentation in addressing real-world DApp security challenges, offering a robust solution for blockchain ecosystem protection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM Bu, Jiuyang Li, Wenkai Li, Zongwei Zhang, Zeng Li, Xiaoqi Cryptography and Security Decentralized applications (DApps) face significant security risks due to vulnerabilities in smart contracts, with traditional detection methods struggling to address emerging and machine-unauditable flaws. This paper proposes a novel approach leveraging fine-tuned Large Language Models (LLMs) to enhance smart contract vulnerability detection. We introduce a comprehensive dataset of 215 real-world DApp projects (4,998 contracts), including hard-to-detect logical errors like token price manipulation, addressing the limitations of existing simplified benchmarks. By fine-tuning LLMs (Llama3-8B and Qwen2-7B) with Full-Parameter Fine-Tuning (FFT) and Low-Rank Adaptation (LoRA), our method achieves superior performance, attaining an F1-score of 0.83 with FFT and data augmentation via Random Over Sampling (ROS). Comparative experiments demonstrate significant improvements over prompt-based LLMs and state-of-the-art tools. Notably, the approach excels in detecting non-machine-auditable vulnerabilities, achieving 0.97 precision and 0.68 recall for price manipulation flaws. The results underscore the effectiveness of domain-specific LLM fine-tuning and data augmentation in addressing real-world DApp security challenges, offering a robust solution for blockchain ecosystem protection. |
| title | Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2504.05006 |