By identifying and addressing common code patterns that trigger false positives in Solidity vulnerability detection tools, developers can significantly improve the efficiency of security audits and focus on genuine threats.
SMARTINTENTNN is a novel deep learning-based tool that effectively detects malicious intent hidden within smart contracts by analyzing code functionality and highlighting suspicious patterns.
To protect users from financial losses due to malicious smart contracts, this paper introduces SMARTINTENTNN, a novel deep learning model that effectively detects unsafe developer intents hidden within smart contract code.
Smart contracts must be secure and trusted-by-design to eliminate the need for third-party trust.
SmartML proposes a modeling language for smart contracts to enhance security and prevent reentrancy vulnerabilities.
ACFIX enhances GPT-4 model to repair AC vulnerabilities in smart contracts by mining common RBAC practices and utilizing context information.