Security Audit

AgentScope AI leverages advanced large language models (LLMs) to enhance security audits for blockchain-based systems. By integrating state-of-the-art natural language processing and code analysis, our platform not only detects vulnerabilities in smart contracts but also provides actionable insights for remediation. This document outlines the complete process—from model initialization to the execution of security logic—with practical code examples.

Initializing LLM Models

The initialization process is crucial for setting up the LLM inference engine, which powers the security audit module. The following pseudo-code demonstrates how the system initializes the model:

javascript // Initializing LLM models in AgentScope AI

const { loadModel, configureInference } = require('llm-sdk');
const config = require('./config/llmConfig');

function initializeLLM() {
    // Load model weights from the specified provider (e.g., OpenAI, HuggingFace)
    const model = loadModel(config.modelPath, {
        provider: config.provider,
        precision: config.precision, // 'fp16' or 'fp32'
    });

    // Configure inference parameters for optimized performance
    const inferenceEngine = configureInference(model, {
        maxTokens: config.maxTokens,
        temperature: config.temperature,
        topP: config.topP,
    });

    return inferenceEngine;
}

module.exports = initializeLLM();

Supported Models and Providers

AgentScope AI supports a variety of LLM models, enabling flexibility and high performance. The current supported models include:

• OpenAI GPT Series: Suitable for detailed natural language explanations and code analysis.

• HuggingFace Transformers: Offers versatility for fine-tuning on specific blockchain security tasks.

• In-House Custom Models: Tailored for enhanced vulnerability detection and audit recommendations.

Each provider is selected based on its performance characteristics and compatibility with our security audit framework.

String Processing for Security Audits

Robust string processing is essential for analyzing smart contract code and generating detailed audit reports. The following sample function demonstrates basic string tokenization and normalization used in our system:

javascript // Example: String processing for smart contract code analysis

function processCode(inputCode) {
    // Normalize input by converting to lowercase and removing extra whitespace
    let normalizedCode = inputCode.toLowerCase().trim();
    
    // Tokenize the code based on whitespace and special characters
    let tokens = normalizedCode.split(/[\s,;()]+/);
    
    return tokens;
}

module.exports = processCode;

Solidity Code Example for Security Audit Logic

The following Solidity snippet is an example of the security logic used during an audit process. This code calculates input amounts, adjusts balances, and ensures that the computed product of adjusted balances meets or exceeds the expected product, thereby validating the integrity of the transaction:

solidity // SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

library SafeMath {
    function mul(uint256 a, uint256 b) internal pure returns (uint256) {
        if (a == 0) return 0;
        uint256 c = a * b;
        require(c / a == b, "Multiplication overflow");
        return c;
    }
}

contract SecurityAudit {
    using SafeMath for uint256;

    uint256 private constant FEE_MULTIPLIER = 100000;
    uint256 private constant FEE_SUBTRACTOR = 50;
    uint256 private constant MIN_PRODUCT_MULTIPLIER = 1000**2;

    function auditLogic(
        uint256 balance0,
        uint256 balance1,
        uint256 _reserve0,
        uint256 _reserve1,
        uint256 amount0Out,
        uint256 amount1Out
    ) public pure returns (bool) {
        uint256 amount0In = balance0 > _reserve0 - amount0Out 
            ? balance0 - (_reserve0 - amount0Out) : 0;
        uint256 amount1In = balance1 > _reserve1 - amount1Out 
            ? balance1 - (_reserve1 - amount1Out) : 0;
        require(amount0In > 0 || amount1In > 0, "INSUFFICIENT_INPUT_AMOUNT");

        uint256 balance0Adjusted = balance0.mul(FEE_MULTIPLIER).sub(amount0In.mul(FEE_SUBTRACTOR));
        uint256 balance1Adjusted = balance1.mul(FEE_MULTIPLIER).sub(amount1In.mul(FEE_SUBTRACTOR));

        require(balance0Adjusted.mul(balance1Adjusted) >= _reserve0.mul(_reserve1).mul(MIN_PRODUCT_MULTIPLIER), "AUDIT_FAILED");
        return true;
    }
}