ConFuzz: Towards Large Scale Fuzz Testing of Smart Contracts in Ethereum
Taiyu Wong, Chao Zhang, Yuandong Ni, Mingsen Luo, Heying Chen, Yufei Yu, Weilin Li, Xiapu Luo, Haoyu Wang
Abstract
Fuzzing is effective at finding vulnerabilities in traditional applications and has been adapted to smart contracts. However, existing fuzzing solutions for smart contracts are not smart enough and can hardly be applied to large-scale testing since they heavily rely on source code or ABI. In this paper, we propose a fuzzing solution ConFuzz applicable to large-scale testing, especially for bytecode-only contracts. ConFuzz adopts Adaptive Interface Recovery (AIR) and Function Information Collection (FIC) algorithm to automatically recover the function interfaces and information, supporting fuzzing smart contracts without source code or ABI. Furthermore, ConFuzz employs a Dependence-based Transaction Sequence Generation (DTSG) algorithm to infer dependencies of transactions and generate high-quality sequences to trigger the vulnerabilities. Lastly, ConFuzz utilizes taint analysis and function information to help detect harmful vulnerabilities and reduce false positives. The experiment shows that ConFuzz can accurately recover over 99.7% of function interfaces and reports more vulnerabilities than state-of-the-art solutions with 98.89% precision and 93.69% accuracy. On all 1.4M unique contracts from Ethereum, ConFuzz found over 11.92% vulnerable contracts. To the best of our knowledge, ConFuzz is the first efficient and scalable solution to test all smart contracts deployed in Ethereum.
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