# Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion

arXiv: https://arxiv.org/abs/2106.09282 · HTML: https://mlsanity.com/paper/2106.09282/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018)
- [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](https://mlsanity.com/paper/1807.03932/) (2018)
- [Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks](https://mlsanity.com/paper/1812.05934/) (2018)
- [A Semantic Framework for the Security Analysis of Ethereum smart contracts](https://mlsanity.com/paper/1802.08660/) (2018)
- [Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats](https://mlsanity.com/paper/1811.06632/) (2018)

## Built on by (most cited first, 24 of 24)

- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection](https://mlsanity.com/paper/2208.06776/) (2022)
- [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024)
- [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [Demystifying Bitcoin Address Behavior via Graph Neural Networks](https://mlsanity.com/paper/2211.14582/) (2022)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities](https://mlsanity.com/paper/2409.10574/) (2024)
- [SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2507.22371/) (2025)
- [Deep Smart Contract Intent Detection](https://mlsanity.com/paper/2211.10724/) (2022)
- [Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization](https://mlsanity.com/paper/2509.09942/) (2025)
- [BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networks](https://mlsanity.com/paper/2512.09385/) (2025)
- [Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods](https://mlsanity.com/paper/2211.14779/) (2022)
- [Dual-view Aware Smart Contract Vulnerability Detection for Ethereum](https://mlsanity.com/paper/2407.00336/) (2024)
- [Vulnerability Detection in Smart Contracts: A Comprehensive Survey](https://mlsanity.com/paper/2407.07922/) (2024)
- [DM4Steal: Diffusion Model For Link Stealing Attack On Graph Neural Networks](https://mlsanity.com/paper/2411.03364/) (2024)
- [Knowledge Migration Framework for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2412.11175/) (2024)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training](https://mlsanity.com/paper/2603.27734/) (2026)

## Abstract (arXiv)

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.