$AiRacleX$: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation

Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu, Rick Siow Mong Goh, David Lo

Abstract: Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure and fair transactions. However, poorly integrated oracles remain susceptible to manipulation, enabling attackers to exploit smart contract logic for unfair asset valuation and financial gain. While many such vulnerabilities are only detected after deployment, smart contracts are typically immutable once deployed, making post-hoc fixes costly or infeasible. This highlights the critical need for detecting oracle manipulation risks before deployment. In this paper, we propose $AiRacleX$, a novel LLM-driven framework that enables pre-deployment detection of price oracle manipulation vulnerabilities by leveraging the complementary strengths of multiple large language models (LLMs). Our approach begins with domain-specific knowledge extraction, where an LLM model synthesizes precise insights about price oracle vulnerabilities, eliminating the need for profound expertise from developers or auditors. This knowledge forms the foundation for a second LLM model to generate structured, context-aware Chain-of-Thought prompts, which guide a third LLM model in accurately identifying manipulation patterns in smart contracts. We evaluate $AiRacleX$ on 60 known vulnerabilities from 44 real-world DeFi exploits and Code4rena projects spanning 2021–2023. The results show that $AiRacleX$ achieves a 2.58 times improvement in recall over the state-of-the-art GPTScan, with comparable precision. Our framework also demonstrates strong extensibility and efficiency, and supports deployment with open-source LLMs to enhance security and reduce operational cost.

External IDs:dblp:journals/tsc/GaoWWLGL26