Adaptive Multi-Agent Explainable Smart Contract Auditing Framework (AMESCAF): A Hybrid Approach Integrating Static Analysis, Symbolic Execution, Formal Verification, and Explainable Artificial Intelligence for Next-Generation Blockchain Security

Abstract

 Smart contracts are a crucial element in blockchain technology, enabling the decentralisation and automation of digital transactions. However, re-entrancy, access control, integer overflows and logic errors all pose a danger to their security and reliability. Previously available audit solutions rely on a single analytical approach, resulting in insufficient detection coverage, high false-positive rate and poor explainability. We propose an AMESCAF to eliminate the aforementioned problems, which adopts static analysis, dynamic testing, symbolic execution, formal verification and AI-assisted reasoning. Common knowledge base and consensus validation method enhances vulnerabilities detection rate and minimizes the duplicate vulnerability discovery. Moreover, there is a context-aware risk assessment model which categorizes vulnerabilities according to technical severity, vulnerability exploitability, business impact and confidence scores. The explainability module gives visual hints on cures. The prototype has been analyzed with respect to the benchmark intelligent contracts, and benchmarked against the current smart contract auditing tools. Safe smart contract auditing was performed by comparing the proposed framework in terms of detection accuracy and analysis efficiency, achieving a 96.4% accuracy, 95.2% recall, 4.1% false positive rate, and 95.8% F1-score, demonstrating superior results.