Code Sentinel: AI-Driven Smart Contract Attack Simulation for Vulnerability Detection

Abstract

Smart contracts are the cornerstone of the swelling network of decentralized applications (dApps), but their mutability is a major security issue. After deployment, any vulnerability in the smart contract code cannot be easily fixed, resulting in high-profile exploits and significant monetary losses. Existing security analysis tools primarily rely on static analysis and symbolic execution, producing abstract warnings and vulnerability reports. There is, however, a gap in these tools: they do not show how an identified vulnerability can be practically exploited, leaving developers with no real sense of the actual risk or attack vehicle. To mitigate this shortcoming, we present Code Sentinel, a new system that combines AI-based vulnerability identification with an automated attack simulation engine. Code Sentinel uses a supervised learning algorithm, trained on large datasets of vulnerable and secure Solidity contracts, to provide precise classification of a wide range of security flaws. More importantly, it uses a reinforcement learning-driven engine that can model the attacker's behaviour and identify the best exploit strategies for the vulnerabilities it identifies. This process creates concrete exploit scenarios that show the exact sequence of transactions and state changes that lead to a compromise. Deep learning-enabled Code Sentinel offers interactive, actionable insights to developers by shifting the paradigm from passively signalling vulnerabilities to demonstratively analysing security, building a safer dApp ecosystem.