  We investigate the feasibility of employing large language models (LLMs) for
conducting the security audit of smart contracts, a traditionally
time-consuming and costly process. Our research focuses on the optimization of
prompt engineering for enhanced security analysis, and we evaluate the
performance and accuracy of LLMs using a benchmark dataset comprising 52
Decentralized Finance (DeFi) smart contracts that have previously been
compromised.
  Our findings reveal that, when applied to vulnerable contracts, both GPT-4
and Claude models correctly identify the vulnerability type in 40% of the
cases. However, these models also demonstrate a high false positive rate,
necessitating continued involvement from manual auditors. The LLMs tested
outperform a random model by 20% in terms of F1-score.
  To ensure the integrity of our study, we conduct mutation testing on five
newly developed and ostensibly secure smart contracts, into which we manually
insert two and 15 vulnerabilities each. This testing yielded a remarkable
best-case 78.7% true positive rate for the GPT-4-32k model. We tested both,
asking the models to perform a binary classification on whether a contract is
vulnerable, and a non-binary prompt. We also examined the influence of model
temperature variations and context length on the LLM's performance.
  Despite the potential for many further enhancements, this work lays the
groundwork for a more efficient and economical approach to smart contract
security audits.
