AI-Driven Vulnerability Detection

Overview

Glitch Gremlin uses machine learning to enhance its vulnerability detection capabilities. The ML model analyzes both static and dynamic program features to identify potential security issues.

Features

Static Analysis

• Code pattern recognition

• Control flow analysis

• State variable tracking

• Cross-program invocation detection

Dynamic Analysis

• Transaction trace analysis

• Memory access patterns

• Instruction sequence modeling

• Error pattern detection

Model Architecture

The vulnerability detection model uses a deep neural network with:

• Input layer: Dense (128 units, ReLU activation) for processing 20 program features

• Regularization: Dropout layer (0.2) to prevent overfitting

• Hidden layer: Dense (32 units, ReLU activation) for pattern recognition

• Output layer: Dense with softmax activation for multi-class vulnerability prediction

Key features:

• Input features include transaction patterns, memory access, error rates

• Confidence scoring for each prediction

• Pattern analysis for detailed vulnerability insights

• Model persistence with save/load capabilities

Usage

import { GlitchSDK, TestType } from '@glitch-gremlin/sdk';

const sdk = new GlitchSDK({
    cluster: 'devnet',
    wallet: yourWallet
});

// Create request with ML configuration
const request = await sdk.createChaosRequest({
    targetProgram: "Your program ID",
    testType: TestType.EXPLOIT,
    duration: 300,
    intensity: 7,
    params: {
        mlConfig: {
            confidenceThreshold: 0.8,
            featureExtraction: {
                includeStaticAnalysis: true,
                includeDynamicTraces: true
            }
        }
    }
});

// Get results with ML predictions
const results = await request.waitForCompletion();
console.log('ML Predictions:', results.mlPredictions);

Model Training

The model is trained on:

• Known vulnerability patterns

• Historical exploit data

• Community-submitted test cases

• Synthetic program traces

Confidence Scores

ML predictions include confidence scores (0-1):

• 0.9-1.0: Very high confidence

• 0.7-0.9: High confidence

• 0.5-0.7: Medium confidence

• <0.5: Low confidence

Best Practices

1. Start with high confidence thresholds (0.8+)

2. Enable both static and dynamic analysis

3. Review ML predictions alongside traditional test results

4. Contribute validated findings back to the training dataset

Next Steps

• Customize ML Parameters

• Contribute Training Data

• ML Model Architecture