Digital Asset Risk Sentinel

AgentScope AI leverages advanced large language models (LLMs) to empower its Digital Asset Risk Sentinel. This module is dedicated to the automated detection and categorization of risks associated with digital assets and smart contracts. By analyzing smart contract code and associated metadata, the system identifies potential vulnerabilities, supply risks, and operational issues, ensuring comprehensive risk management for blockchain projects.

Initializing LLM Models

To enable the Digital Asset Risk Sentinel, AgentScope AI initializes its LLM models with robust configurations. The following pseudo-code demonstrates the initialization process:

javascript // Initializing LLM models in AgentScope AI

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

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

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

    return inferenceEngine;
}

module.exports = initializeRiskSentinelLLM();

Supported Models and Providers

AgentScope AI supports a range of high-performance LLM models for its risk analysis engine. These include:

• OpenAI GPT Series: Renowned for their advanced language understanding and ability to process complex code structures.

• HuggingFace Transformers: Versatile models that can be fine-tuned for domain-specific risk assessment.

• Custom In-House Models: Tailored solutions optimized for detecting blockchain vulnerabilities and risk factors.

Each provider is chosen based on its performance characteristics and compatibility with AgentScope AI’s security framework.

String Processing for Risk Assessment

Effective string processing is crucial for extracting meaningful data from smart contract code and associated textual information. The following function demonstrates a basic string processing routine:

javascript // Example function for processing risk-related strings

function processRiskString(inputText) {
    // Normalize input: convert to lowercase and remove extra whitespace
    let normalized = inputText.toLowerCase().trim();
    
    // Tokenize based on whitespace and punctuation
    let tokens = normalized.split(/[\s,;]+/);
    
    // Further processing (e.g., filtering, stop-word removal) can be applied here
    return tokens;
}

module.exports = processRiskString;

Risk Analysis Logic

AgentScope AI’s Digital Asset Risk Sentinel evaluates various risk factors within a smart contract. Below is a Solidity code snippet demonstrating logic similar to our risk categorization approach:

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 RiskSentinel {
    using SafeMath for uint256;

    // Risk category thresholds and multipliers
    uint256 constant CRITICAL_THRESHOLD = 1;
    uint256 constant HIGH_THRESHOLD = 2;
    uint256 constant MEDIUM_THRESHOLD = 1;

    // Example risk data (for demonstration purposes)
    uint256 public totalRisks = 5;
    // Risk breakdown: [Mintability, Supply, Security, Operation]
    uint256[4] public riskScores = [1, 2, 1, 1];

    // Descriptive risk labels
    string[] public riskDescriptions = [
        "Anyone can mint unlimited tokens. Critical Supply Security",
        "Circulating supply mismatches the whitepaper. High Supply",
        "This token contract has a centralized minting authority. High Supply Operation",
        "Potential centralization issues. Medium Centralization"
    ];

    // Function to perform risk validation based on adjusted balances and thresholds
    function validateRisk(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(100000).sub(amount0In.mul(50));
        uint256 balance1Adjusted = balance1.mul(100000).sub(amount1In.mul(50));

        require(balance0Adjusted.mul(balance1Adjusted) >= uint256(_reserve0).mul(_reserve1).mul(1000**2), "RISK_VALIDATION_FAILED");
        return true;
    }
}

Explanation of Risk Analysis Logic

• Risk Categorization: The system evaluates different risk factors by comparing input amounts against reserve values, adjusting balances based on predetermined multipliers, and ensuring the product of adjusted balances meets specified thresholds.

• Risk Breakdown: The example outlines risk scores and descriptions that correspond to specific risk categories such as mintability, supply consistency, and operational security.