An empirical analysis of vulnerability detection tools for solidity smart contracts

TL;DR

This paper empirically evaluates 20 Solidity smart contract vulnerability detection tools using a large annotated dataset, revealing their effectiveness, limitations, and the benefits of combining multiple methods.

Contribution

It provides the largest dataset of manually annotated smart contracts and the most comprehensive empirical evaluation of vulnerability detection tools to date.

Findings

Tools vary significantly in accuracy and reliability.

Combining multiple tools improves vulnerability detection.

Up to 76.78% vulnerabilities found with combined tools.

Abstract

The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances we manually annotated with line-level vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the effectiveness of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78\% found vulnerabilities taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and the empirical evaluation of the greatest number of tools to date.