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

arXiv: https://arxiv.org/abs/2502.06348 · HTML: https://mlsanity.com/paper/2502.06348/

## Key facts

- Cited by 3 papers in the map (data through 13 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Understanding the Impact of Negative Prompts: When and How Do They Take Effect?](https://mlsanity.com/paper/2406.02965/) (2024)
- [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019)
- [Do you still need a manual smart contract audit?](https://mlsanity.com/paper/2306.12338/) (2023)
- [sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts](https://mlsanity.com/paper/2004.08563/) (2020)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [SoK: Transparent Dishonesty: front-running attacks on Blockchain](https://mlsanity.com/paper/1902.05164/) (2019)
- [SoK: Oracles from the Ground Truth to Market Manipulation](https://mlsanity.com/paper/2106.00667/) (2021)
- [FORAY: Towards Effective Attack Synthesis against Deep Logical Vulnerabilities in DeFi Protocols](https://mlsanity.com/paper/2407.06348/) (2024)
- [Safeguarding DeFi Smart Contracts against Oracle Deviations](https://mlsanity.com/paper/2401.06044/) (2024)

## Built on by (most cited first, 3 of 3)

- [TxRay: Agentic Postmortem of Live Blockchain Attacks](https://mlsanity.com/paper/2602.01317/) (2026)
- [Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2601.08641/) (2026)
- [Can Artificial Intelligence solve the blockchain oracle problem? Unpacking the Challenges and Possibilities](https://mlsanity.com/paper/2507.02125/) (2025)

## Abstract (arXiv)

Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure transactions, yet these oracles are highly vulnerable to manipulation, enabling attackers to exploit smart contract vulnerabilities for unfair asset valuation and financial gain. Detecting such manipulations traditionally relies on the manual effort of experienced experts, presenting significant challenges. In this paper, we propose a novel LLM-driven framework that automates the detection of price oracle manipulations by leveraging the complementary strengths of different LLM models (LLMs). Our approach begins with domain-specific knowledge extraction, where an LLM model synthesizes precise insights about price oracle vulnerabilities from top-tier academic papers, 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 validate the effectiveness of framework through experiments on 60 known vulnerabilities from 46 real-world DeFi attacks or projects spanning 2021 to 2023. The best performing combination of LLMs (Haiku-Haiku-4o-mini) identified by AiRacleX demonstrate a 2.58-times improvement in recall (0.667 vs 0.259) compared to the state-of-the-art tool GPTScan, while maintaining comparable precision. Furthermore, our framework demonstrates the feasibility of replacing commercial models with open-source alternatives, enhancing privacy and security for developers.