An LLM framework with tailored prompts and a new dataset of 31,165 annotated instances achieves 0.92 positive recall and 0.85 negative recall for detecting 13 smart contract vulnerability categories.
A context-driven approach for co-auditing smart contracts with the support of gpt-4 code interpreter
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Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts
An LLM framework with tailored prompts and a new dataset of 31,165 annotated instances achieves 0.92 positive recall and 0.85 negative recall for detecting 13 smart contract vulnerability categories.