# Dynamic Vulnerability Detection on Smart Contracts Using Machine Learning

arXiv: https://arxiv.org/abs/2102.07420 · HTML: https://mlsanity.com/paper/2102.07420/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Vulnerability Detection in C/C++ Code with Deep Learning](https://mlsanity.com/paper/2405.12384/) (2024)
- [Vulnerability Detection in Smart Contracts: A Comprehensive Survey](https://mlsanity.com/paper/2407.07922/) (2024)
- [A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions](https://mlsanity.com/paper/2408.08915/) (2024)

## Abstract (arXiv)

In this work we propose Dynamit, a monitoring framework to detect reentrancy vulnerabilities in Ethereum smart contracts. The novelty of our framework is that it relies only on transaction metadata and balance data from the blockchain system; our approach requires no domain knowledge, code instrumentation, or special execution environment. Dynamit extracts features from transaction data and uses a machine learning model to classify transactions as benign or harmful. Therefore, not only can we find the contracts that are vulnerable to reentrancy attacks, but we also get an execution trace that reproduces the attack.