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

2026-04-20

AI Summary

Existing security tools struggle to accurately identify monetizable exploitable vulnerabilities (MEVuls) in smart contracts that can be leveraged in financial attacks, often generating excessive false positives. This work formally defines MEVuls for the first time and introduces FAUDITOR, a fuzzing framework that integrates learned auditing knowledge through a synergistic combination of finance-interface-guided testing, natural language processing of audit reports, and a self-learning search strategy to enable precise and efficient vulnerability detection. FAUDITOR outperforms state-of-the-art tools in both instruction coverage and vulnerability discovery speed, successfully uncovering 220 zero-day MEVuls.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Responsible Web: Audits of web technologies, standards, platforms, and applications
• Security and Privacy: Cryptocurrency and smart contracts
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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.

Problem

• Monetarily Exploitable Vulnerabilities
• Smart Contracts
• Security
• Financial Exploits
• Vulnerability Detection

Innovation

• Monetarily Exploitable Vulnerabilities
• Smart Contract Fuzzing
• Auditor Knowledge Learning
• NLP-guided Testing
• Self-learning Fuzzer