Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2024
Abstract
Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz , a machine learning guided smart contract fuzzing framework. The machine learning models are trained with novel features (e.g., word vectors and instructions) and are used to filter likely benign program paths. Comparing with existing static tools, machine learning model is proven to be more robust, avoiding directly adopting manually-defined rules in specific tools. We compare xFuzz with three state-of-the-art tools on 7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities, of which 15 vulnerabilities are exposed for the first time. Furthermore, our approach is shown to be efficient in detecting non-cross-contract vulnerabilities as well—using less than 20% time as that of other fuzzing tools, xFuzz detects twice as many vulnerabilities.
Keywords
Smart Contract, Fuzzing, Cross-contract Vulnerability, Machine Learning
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Dependable and Secure Computing
Volume
21
Issue
2
First Page
515
Last Page
529
ISSN
1545-5971
Identifier
10.1109/TDSC.2022.3182373
Publisher
Institute of Electrical and Electronics Engineers
Citation
XUE, Yinxing; YE, Jiaming; ZHANG, Wei; SUN, Jun; MA, Lei; WANG, Haijun; and ZHAO, Jianjun.
xFuzz: Machine learning guided cross-contract fuzzing. (2024). IEEE Transactions on Dependable and Secure Computing. 21, (2), 515-529.
Available at: https://ink.library.smu.edu.sg/sis_research/9213
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TDSC.2022.3182373