# Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM

arXiv: https://arxiv.org/abs/2504.05006 · HTML: https://mlsanity.com/paper/2504.05006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [AI-Based Vulnerability Analysis of NFT Smart Contracts](https://mlsanity.com/paper/2504.16113/) (2025)
- [A Comprehensive Study of Exploitable Patterns in Smart Contracts: From Vulnerability to Defense](https://mlsanity.com/paper/2504.21480/) (2025)
- [Information Security Based on LLM Approaches: A Review](https://mlsanity.com/paper/2507.18215/) (2025)
- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Facial Recognition Leveraging Generative Adversarial Networks](https://mlsanity.com/paper/2505.11884/) (2025)

## Abstract (arXiv)

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.