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CONTRACTFIX: A Framework for Automatically Fixing Vulnerabilities in Smart Contracts

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arxiv 2307.08912 v2 pith:3JINKJD3 submitted 2023-07-18 cs.SE

classification cs.SE
keywords contractssmartvulnerabilitiescontractfixtoolautomaticallycontractdetected
verification ladder T0 review T1 audit T2 compute T3 formal

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abstract

The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, deploying smart contracts with detected vulnerabilities (known to developers) are not acceptable, and fixing all the detected vulnerabilities is needed, which incurs high manual labor cost without effective tool support. To fill this need, in this paper, we propose ContractFix, a novel framework that automatically generates security patches for vulnerable smart contracts. ContractFix is a general framework that can incorporate different fix patterns for different types of vulnerabilities. Users can use it as a security fix-it tool that automatically applies patches and verifies the patched contracts before deploying the contracts. To address the unique challenges in fixing smart contract vulnerabilities, given an input smart contract, \tool conducts our proposed ensemble identification based on multiple static verification tools to identify vulnerabilities that are amenable for automatic fix. Then, ContractFix generates patches using template-based fix patterns and conducts program analysis (program dependency computation and pointer analysis) for smart contracts to accurately infer and populate the parameter values for the fix patterns. Finally, ContractFix performs static verification that guarantees the patched contract is free of vulnerabilities. Our evaluations on $144$ real vulnerable contracts demonstrate that \tool can successfully fix $94\%$ of the detected vulnerabilities ($565$ out of $601$) and preserve the expected behaviors of the smart contracts.

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Cited by 1 Pith paper

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  1. SoK: Towards Effective Automated Vulnerability Repair

    cs.CR 2025-01 conditional novelty 6.0 of 10

    This SoK benchmarks ten automated vulnerability repair tools across C/C++ and Java and concludes that learning-based methods are strong on synthetic benchmarks but trail non-learning methods on real-world vulnerabilit...

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