Jyane: Detecting Reentrancy vulnerabilities based on path profiling method

Abstract

Ethereum is essentially a transaction-driven state machine, and a smart contract is a piece of executable code on Ethereum. Compared with the scripting language on Bitcoin, the smart contract language solidity, which is Turing-complete and the ex-pressive capabilities are very powerful. However, this attribute also brings many potential security threats, vulnerabilities, and various other issues. In this paper, we propose a novel smart contract security technology, named Jyane, to detect the Reentrancy vulnerability, which is one of the most threatening vulnerabilities to smart contracts. More importantly, Our tool-Jyane is the first path profiling solution for smart contracts. Firstly, we use EVM (Ethereum Virtual Machine) binary bytecode to construct control flow graphs (CFG), then use the improved Ball-Larus Path profiling algorithm (BLPP) to generate IDs for acyclic paths. Finally, after profiling the constructed paths, the suspicious paths can be detected successfully. We evaluate Jyane and other technology through comprehensive test and comparison; the results show that Jyane can profile the actual execution path of smart contracts to detect vulnerabilities with a low false-positive rate accurately. From the results of the evaluation, Jyane marked 27 of 1,226 Ethereum smart contracts selected in 2016 and 2017 as vulnerable contracts, included the vulnerability of the DAO contract which once led to a $60 million loss. Furthermore, compared with some other existing detection tools, Jyane shows broader detection range for Reentrancy vulnerabilities with lower time overhead.