Capturing Monetarily Exploitable Vulnerability in Smart Contracts via Auditor Knowledge-Learning Fuzzing

• URL: http://arxiv.org/abs/2604.18395v1
• Date: Mon, 20 Apr 2026 15:17:45 GMT
• Title: Capturing Monetarily Exploitable Vulnerability in Smart Contracts via Auditor Knowledge-Learning Fuzzing
• Abstract summary: FAUDITOR is a fuzzer designed to detect Monetarily Exploitable Vulnerabilities (MEVuls) in smart contracts.<n>MEVuls stem from vulnerabilities arising from exploitable implementations in monetary transactions or value-transfer logic.<n>In our evaluation, FAUDITOR impressively reveals 220 zero-day MEVuls.
• Score: 11.486008179945374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart contracts extended blockchain functionality beyond simple transactions, powering complex applications like decentralized finance (DeFi). However, this complexity introduces serious security challenges, including price manipulation and inflation attacks. Despite the development of various security tools, the rapid rise in financially motivated exploits continues to pose a significant threat to the blockchain ecosystem. These financially motivated exploits often stem from Monetarily Exploitable Vulnerabilities (MEVuls), which refer to vulnerabilities arising from exploitable implementations in monetary transactions or value-transfer logic. Due to their complexity, intricate chains of function calls, multifaceted logic, and diverse manifestations across different smart contracts, MEVuls are particularly challenging for current security tools to identify. Instead of providing actionable insights, existing tools frequently generate excessive warnings that overwhelm developers without effectively mitigating risks. To address the challenge of recognizing MEVuls, we first formalize MEVuls based on common real-world financial exploits. Then, we introduce FAUDITOR, a specialized fuzzer designed to detect MEVuls in smart contracts. The key insight is that leveraging smart contracts' finance-related interfaces directly exposes critical vulnerabilities, making detection more targeted. We further integrate auditors' reports using NLP to extract valuable insights on exploitation patterns, enabling a more informed search strategy. Additionally, FAUDITOR employs a self-learning mechanism that refines its detection strategies over time, allowing it to improve based on prior fuzzing results. In our evaluation, FAUDITOR impressively reveals 220 zero-day MEVuls. Meanwhile, compared to existing fuzzers, FAUDITOR detects vulnerabilities faster and achieves better instruction coverage.

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