Enhancing Ethereum smart-contracts static analysis by computing a precise Control-Flow Graph of Ethereum bytecode

The immutable nature of Ethereum transactions, and consequently Ethereum smart-contracts, has stimulated the proliferation of many approaches aiming at detecting defects and security issues before the deployment of smart-contracts on the blockchain. Indeed, the actions performed by smart-contracts instantiated on the blockchain, possibly involving substantial financial value, cannot be undone.Unfortunately, smart-contracts source code is not always available, hence approaches based on static analysis have very often to face the problem of inspecting the compiled Ethereum Virtual Machine (EVM) bytecode, retrieved directly from the blockchain. However, due to the intrinsic complexity of EVM bytecode (especially in jumps address resolution), the state-of-the-art static analysis-based solutions have poor accuracy in the automated detection of Ethereum smart-contracts programming defects and vulnerabilities. This paper presents a novel approach based on symbolic execution of the EVM operands stack that allows to resolve jumps address in the EVM bytecode and to construct a precise Control-Flow Graph (CFG) of compiled smart-contracts. Many static analysis techniques are based on a CFG-based representation of the smart-contract to validate, and would therefore benefit from our approach. We have implemented the CFG reconstruction algorithm in a tool called EtherSolve. Then, we have validated the tool on a large dataset of real-world Ethereum smart-contracts, showing that EtherSolve extracts more precise CFGs, w.r.t. state-of-the-art available approaches. Finally, we have extended EtherSolve with two detectors for two of the most prominent Ethereum smart-contracts vulnerabilities (Reentrancy and Tx.origin). Experimental results show that exploiting the proposed CFG reconstruction static analysis, leads to more accurate vulnerabilities detection, w.r.t. state-of-the-art security tools.Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.(c) 2023 Elsevier Inc. All rights reserved.