SolAnalyser: A Framework for Analysing and Testing Smart Contracts

Abstract

Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools-Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.

Systematic Review of Security Vulnerabilities in Ethereum Blockchain Smart Contract

“…In this section, we have reviewed Ethereum Smart Contract vulnerabilities [9], [31]- [45], [122], [123], [131], [132], [138]- [142] with some well-known attacks/example [32], [46]- [49], detection tools [30], [37], [50]- [58], [125], [133]- [137], [143], and their suggested preventive methods [30], [47], [59]- [63], [124], [126]. Researchers classified these vulnerabilities based on different criteria such as seriousness, root cause, flaws in solidity, security flaws, privacy flaws, performance flaws, flaws in EVM [27] byte code, and blockchain [64] characteristics.…”

“…This paper presents a deep insight into each vulnerability as well as its detection tools, real life attacks and prevention mechanisms. Academic Haskell E-EVM [25] Academic Python EtherTrust [104] Academic java EthIR [105] Academic Python FSolidM [106] Academic Java Script Gasper [42] *SDSLabs Go KEVM [107] Academic Python MAIAN [59] Academic Python Manticore [108] *Trail of Bits Python Mythril [72] *ConsenSys Python Osiris [61] Academic Python Oyente [10] Community Python Porosity [109] *Comae Technologies C++ Rattle [110] *Trail of Bits Python ReGaurd [76] *Chieftin Lab C++ Remix-IDE [111] Community Java Script SASC [112] *Fujitsu Python sCompile [113] Academic C++ Securify [71] Academic Java SmartCheck [69] Academic Java Solgraph [114] *Raine Revere Java Script SolMet [93] Academic Java teEther [26] Academic Python Vandal [115] Academic Python Zeus [116] *IBM Research India C++ * Tool development company name [129] Harz et al [130] Angelo et al [54] Chen et al [145] Luu et al [10] Atzei et al [48] Tang et al [144] Durieux et al [145] at a very fast speed. Ethereum blockchain design and EVM features pose several security challenges [38], [117]-[121].…”

Challenges and Common Solutions in Smart Contract Development

“…In works related to code analysis, selected flaws in smart contract code (e.g., overflow and underflow [20] or reentrancy [22]) and their detection using formal methods have been researched (e.g., [132], [163], [164]). For automated detection of these flaws, software tools have been proposed that perform formal verification (e.g., [163], [165], [166]), dynamic code analysis (e.g., [132], [133], [152]), static code analysis (e.g., [23], [24], [167]- [169]), or machine learning using classifiers like XGBoost or AdaBoost (e.g., [25]). These tools are designed to support developers in improving their code by identifying recurring flaws in smart contract code (e.g., by using formalized patterns of code flaws).…”

“…Besides code analysis, research has proposed approaches and tools for software testing (e.g., [24], [171]- [173]). Related works offer valuable and practical insights that support smart contract developers in improving their code through different testing strategies and tools.…”

“…These solutions can be largely distinguished into automated verification and coding support. For automated verification, existing research presents software tools (e.g., MadMax [14] or ReGuard [22]) for automatically identifying flaws in smart contract code (e.g., using static analysis [23], dynamic analysis [24], or machine learning [25]) and increasing code quality. Nevertheless, the applicability of automated verification to smart contract code is limited in terms of comprehensiveness because most formal verification tools apply static patterns with the Bitcoin documentation [3] and find Bitcoin's smart contract capabilities not Turing-complete.…”