CyberTrust AI: An LLM-Based Framework for Automated Smart Contract Vulnerability Detection, Classification, and Remediation

Abstract

Smart contract vulnerabilities have caused documented financial losses exceeding $6 billion across decentralized finance (DeFi) ecosystems between 2020 and 2024. Existing automated security toolsincluding Slither, Mythril, and Manticoreemploy rule-based static analysis that systematically fails to detect contextual, indirect, and semantically complex vulnerability patterns that are commonly exploited in production attacks. This paper presents CyberTrust AI, a production-deployed security analysis framework that applies large language model (LLM) inference via Anthropic's Claude Sonnet to perform contextual, semantic vulnerability analysis of Solidity smart contracts. The system identifies critical vulnerability classes including reentrancy attacks, integer overflow and underflow, unprotected self-destruct, unchecked external call return values, tx.origin authentication abuse, timestamp manipulation, and access control logic flawsgenerating severity-classified structured findings, natural language attack vector explanations, automated Solidity remediation code, and cryptographically verifiable onchain NFT trust scores. The complete system has been deployed at cybersheild-sooty.vercel.app and implements seven production capabilities: multi-mode audit analysis (security audit, threat simulation, gas optimization), realtime streaming analysis output, batch multi-file contract auditing, side-by-side contract diff comparison, conversational AI chat assistance for vulnerability Q&A, and a public trust score leaderboard. We conducted preliminary evaluation on 35 annotated Solidity contracts covering five canonical vulnerability types, demonstrating that LLM-based contextual analysis successfully detects all vulnerability instances while conventional static analysis tools miss approximately 13% of contextual casesparticularly indirect reentrancy patterns and access control logic errors requiring cross-function semantic reasoning. A full quantitative comparative evaluation is in progress.