TL;DR
This paper provides a comprehensive review of current research in fuzzing smart contracts, practical evaluation of 13 advanced smart contract fuzzing tools and an in-depth empirical study to evaluate state-of-the-art contract fuzzers.
Findings demonstrate that the proposed framework provides a transparent and effective approach for smart contract vulnerability detection, supporting the development of trustworthy blockchain security analysis systems.
The results demonstrate that the proposed framework provides accurate, interpretable, and transparent vulnerability detection for Ethereum smart contracts and can assist blockchain developers and security auditors in improving blockchain security analysis.
We propose CMod, an economic model for analyzing the economic security of decentralized finance (DeFi) smart contract code. CMod defines the notions of economic value, intended-return conditions, and unintended single-transaction return, and reasons about economic security by proving the absence of unintended single-tr...
A conceptual taxonomy establishing an explicit correspondence between classical and smart contract fuzzing is developed and an architectural taxonomy of representative fuzzers unified under a generalised waypoint architecture is presented that exposes key feedback domains and composition gaps.
Smart contracts, which are fundamental to DeFi financial systems, are vulnerable to exploitable bugs that can cause substantial monetary losses. A recent study shows that over 80% of these exploitable bugs, primarily functional bugs, evade detection by existing tools. Automatically identifying functional bugs in smart...
Deep learning-enabled Code Sentinel offers interactive, actionable insights to developers by shifting the paradigm from passively signalling vulnerabilities to demonstratively analysing security, building a safer dApp ecosystem.
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