Smart Contract Vulnerability Detection using Prompt Engineering with Reasoning Models

Abstract

The increasing deployment of smart contracts has drawn significant attention to the urgent need for robust and scalable vulnerability detection techniques to mitigate substantial financial risks associated with their immutable nature on blockchain platforms. This paper introduces structured reasoning prompts using agent-role chaining for vulnerability detection that utilizes model capacity to enhance smart contract security through zero-shot and structured prompt engineering without fine-tuning. By carefully defining agent roles and embedding explicit reasoning steps within structured prompts for large language models (LLMs), the proposed method exploits the inherent reasoning capabilities of LLMs to identify security flaws in smart contracts without extensive model retraining. Experimental results demonstrate the effectiveness of the system in achieving competitive performance compared to existing vulnerability detection techniques, highlighting the potential of prompt engineering as an efficient and adaptable strategy for enhancing smart contract security.