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- Towards Effective Guidance of Smart Contract Fuzz Testing Based on . . .
Due to such serious impacts, there has been extensive research on the detection of smart contract vulnerabilities Fuzz testing [3,4] (or fuzzing), which is a popular software testing technique, is also widely used to identify vulnerabilities in smart contracts
- Fuzzing with Echidna
Fuzzing is a well-known technique in the security industry to discover bugs in software Here you will get to know some tools available for fuzz testing and have a basic understanding of how Echidna works
- Fuzz and Invariant Tests: Full Explainer - Cyfrin
Implementing fuzz testing is a vital step to ensure the security and robustness of smart contracts By understanding the invariants of a system and using fuzz testing to validate them, developers can prevent potential attacks and create a safer Web 3 ecosystem
- The Dynamics of Smart Contract Auditing: A Dive into Bug Detection and . . .
Smart Contract Auditing is a meticulous process that demands a keen eye and strategic application of tools In this article, we will dive into bug detection, the role of automated tools, and the bottlenecks of manual auditing, particularly in fuzz testing
- ACOFuzz: An ant colony algorithm-based fuzzer for smart contracts
Fuzz testing is highly automated and more convenient to use, the many advantages of fuzz testing also make it an important direction in the research of Ethereum smart contract security
- How Does Symbolic Execution Differ from Fuzz Testing in Smart Contracts . . .
Fuzz testing for smart contracts typically involves generating a high volume of varied transaction data and observing the contract’s behavior This process can be stateful, meaning the fuzzer considers the contract’s current state when generating subsequent inputs
- Fuzzing For L1 Protocols And Smart Contracts: Detecting Vulnerabilities
Fuzzing is an automated testing technique for blockchain protocols and smart contracts It generates an extreme number of semi-random inputs and feeds them to the tested system
- MLFuzzer: a fuzzing approach based on generative adversarial networks . . .
Smart contracts, composed of many programming languages, have become increasingly popular However, they are susceptible to logical defects and security risks, which can result in financial damages and compromise the integrity of the blockchain This work aims to use machine-learning techniques to automate the production of inputs for fuzzing, specifically for generation-based fuzzing to be
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