  The surge in the adoption of smart contracts necessitates rigorous auditing
to ensure their security and reliability. Manual auditing, although
comprehensive, is time-consuming and heavily reliant on the auditor's
expertise. With the rise of Large Language Models (LLMs), there is growing
interest in leveraging them to assist auditors in the auditing process
(co-auditing). However, the effectiveness of LLMs in smart contract co-auditing
is contingent upon the design of the input prompts, especially in terms of
context description and code length. This paper introduces a novel
context-driven prompting technique for smart contract co-auditing. Our approach
employs three techniques for context scoping and augmentation, encompassing
code scoping to chunk long code into self-contained code segments based on code
inter-dependencies, assessment scoping to enhance context description based on
the target assessment goal, thereby limiting the search space, and reporting
scoping to force a specific format for the generated response. Through
empirical evaluations on publicly available vulnerable contracts, our method
demonstrated a detection rate of 96\% for vulnerable functions, outperforming
the native prompting approach, which detected only 53\%. To assess the
reliability of our prompting approach, manual analysis of the results was
conducted by expert auditors from our partner, Quantstamp, a world-leading
smart contract auditing company. The experts' analysis indicates that, in
unlabeled datasets, our proposed approach enhances the proficiency of the GPT-4
code interpreter in detecting vulnerabilities.
