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Smart contracts are a core component of blockchain systems, but their immutability makes failures in deployed code catastrophic and irreversible. Existing work classifies vulnerabilities, defects, and attacks, yet the observable failures of smart contracts, the deviations from intended behavior or the expected ledger state seen once a contract is deployed, have been largely disregarded.
Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to build by hand. We propose an approach that uses Large Language Models (LLMs) to automatically inject vulnerabilities into Solidity smart contracts, and demonstrate it in a case study targeting 49 vulnerability types from OpenSCV.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
