Smart contract audits

Smart contract audits are an essential process in Web3 security, ensuring that decentralized applications (dApps) function correctly and securely. With smart contracts governing billions of dollars in transactions, any vulnerability in their code can lead to catastrophic financial losses. RugGuardian AI offers an automated, AI-driven auditing system that meticulously analyzes the logic, functionality, and security of smart contracts.

When a new project is launched, RugGuardian AI immediately begins scanning the smart contract code. Using machine learning models and extensive databases of known vulnerabilities, it can detect potential risks in real time. The audit reviews different aspects of the code, from basic syntax errors to more complex logic flaws like re-entrancy, integer overflows, or mismanagement of permissions.

Here is an example of a common vulnerability in Solidity, the primary language for Ethereum smart contracts:

pragma solidity ^0.8.0;

contract Vulnerable {
    mapping(address => uint) public balances;

    function withdraw(uint _amount) public {
        require(balances[msg.sender] >= _amount);
        (bool success, ) = msg.sender.call{value: _amount}("");
        require(success);
        balances[msg.sender] -= _amount;  // Vulnerable to re-entrancy attack
    }
}

In this case, the contract is vulnerable to a re-entrancy attack, where malicious actors can exploit the contract’s behavior and withdraw more funds than they should. RugGuardian AI detects such issues, offering solutions to prevent them.

Key Features of RugGuardian AI Smart Contract Audits:

Automated Code Review: Instantly scans and assesses smart contracts for any known vulnerabilities, providing a thorough audit report.
Real-Time Monitoring: The audit process doesn’t stop after launch. RugGuardian continuously monitors the contract for updates or unusual activities that could introduce new vulnerabilities.
Detailed Reporting: Each audit generates a comprehensive report, listing identified vulnerabilities, security suggestions, and overall risk scores.
Cross-Chain Audits: RugGuardian AI supports projects on multiple chains like Ethereum, Binance Smart Chain, and more, ensuring comprehensive protection across platforms.

Common Vulnerabilities Detected by RugGuardian AI:

Re-entrancy Attacks: Where a malicious contract can repeatedly call back into the vulnerable contract before the initial function is finished.
Integer Overflows and Underflows: Errors when an arithmetic operation results in a number larger or smaller than the allocated storage space.
Unchecked External Calls: Where funds are sent to an untrusted contract without checking the return status, leading to potential losses.

Example of Secure Solidity Code:

pragma solidity ^0.8.0;

contract Secure {
    mapping(address => uint) public balances;

    function withdraw(uint _amount) public {
        require(balances[msg.sender] >= _amount);
        balances[msg.sender] -= _amount;  // Update balance before transferring funds
        (bool success, ) = msg.sender.call{value: _amount}("");
        require(success, "Transfer failed.");
    }
}

The RugGuardian AI platform and all related tools are provided for educational and experimental purposes only. Do not use these codes on production.

This updated version resolves the re-entrancy vulnerability by reducing the balance before making the external call. This is just one of the many protections that RugGuardian AI checks for.

Why Smart Contract Audits Matter

Smart contract audits are critical for ensuring the security of decentralized applications and DeFi projects. Without proper audits, these contracts become easy targets for malicious actors. RugGuardian AI not only performs thorough audits but also enhances them with ongoing monitoring, keeping projects safe long after their initial deployment.

By offering a robust, AI-powered audit system, RugGuardian AI ensures the integrity and trustworthiness of decentralized systems, allowing users to engage with DeFi and Web3 ecosystems confidently.

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Last updated 1 month ago

pragma solidity ^0.8.0; contract Vulnerable { mapping(address => uint) public balances; function withdraw(uint _amount) public { require(balances[msg.sender] >= _amount); (bool success, ) = msg.sender.call{value: _amount}(""); require(success); balances[msg.sender] -= _amount; // Vulnerable to re-entrancy attack } }

Common Vulnerabilities Detected by RugGuardian AI:

Re-entrancy Attacks: Where a malicious contract can repeatedly call back into the vulnerable contract before the initial function is finished.

Integer Overflows and Underflows: Errors when an arithmetic operation results in a number larger or smaller than the allocated storage space.

Unchecked External Calls: Where funds are sent to an untrusted contract without checking the return status, leading to potential losses.

Example of Secure Solidity Code:

pragma solidity ^0.8.0;

contract Secure {
    mapping(address => uint) public balances;

    function withdraw(uint _amount) public {
        require(balances[msg.sender] >= _amount);
        balances[msg.sender] -= _amount;  // Update balance before transferring funds
        (bool success, ) = msg.sender.call{value: _amount}("");
        require(success, "Transfer failed.");
    }
}

The RugGuardian AI platform and all related tools are provided for educational and experimental purposes only. Do not use these codes on production.

This updated version resolves the re-entrancy vulnerability by reducing the balance before making the external call. This is just one of the many protections that RugGuardian AI checks for.

Why Smart Contract Audits Matter

By offering a robust, AI-powered audit system, RugGuardian AI ensures the integrity and trustworthiness of decentralized systems, allowing users to engage with DeFi and Web3 ecosystems confidently.