Empirical evaluation of 20 vulnerability detection tools for Solidity smart contracts on a new line-level annotated dataset of 2,182 instances identifies a 3-tool combination achieving 76.78% detection in under one minute on average.
arXiv preprint arXiv:2501.07058 (2025)
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LLMs for smart contract security analysis show lexical bias from identifier names causing high false positives, with prompting creating precision-recall trade-offs, positioning them as complements rather than replacements for static analysis tools.
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An empirical analysis of vulnerability detection tools for solidity smart contracts
Empirical evaluation of 20 vulnerability detection tools for Solidity smart contracts on a new line-level annotated dataset of 2,182 instances identifies a 3-tool combination achieving 76.78% detection in under one minute on average.
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Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions
LLMs for smart contract security analysis show lexical bias from identifier names causing high false positives, with prompting creating precision-recall trade-offs, positioning them as complements rather than replacements for static analysis tools.