GenAI Security Scanner - Technical Documentation

🏗️ Technical Architecture

Multi-Layer Security Testing Framework

Input Layer

Multi-modal Input Processing

Analysis Engine

Pattern Recognition & ML Models

Detection Layer

11 Specialized Detectors

Scoring Engine

Risk Assessment & Metrics

📊 Markov Chain Analysis

Statistical Modeling

Our system uses Markov chain models to analyze text generation patterns:

N-gram pattern detection (up to 3-gram)
Kneser-Ney smoothing for unseen sequences
Transition probability analysis
Entropy-based anomaly detection

📈 Statistical Deviation Analysis

Proven Methods

Statistical techniques for behavioral anomaly detection:

Z-score calculations (2σ threshold)
95% confidence interval analysis
IQR-based outlier detection
Moving window statistical analysis

🔐 Pattern Recognition

Industry Standard

Pattern matching and validation techniques:

Regular expression pattern matching
SHA-256 hashing for fingerprinting
Similarity scoring (Levenshtein distance)
Template matching with variations

⚡ Efficient Processing

Optimized

Performance optimization techniques:

Asynchronous request handling
Caching for repeated patterns
Batch processing capabilities
Memory-efficient data structures

🔬 Advanced Detection Algorithms

Prompt Injection Detection

Techniques Used:

Pattern matching with known injection signatures
Input sanitization and validation
Recursive depth analysis for nested prompts
Unicode normalization & encoding detection
Template matching with variations

Reliable

Detection Method

Jailbreak Prevention

Defense Mechanisms:

Behavioral deviation analysis (z-scores)
Markov chain pattern detection
Instruction hierarchy monitoring
Response integrity checking
Pattern analyzer with safe baselines

11

Attack Vectors Covered

Hallucination Detection

Verification Methods:

Pattern-based fact checking
Consistency verification across responses
Statistical confidence scoring
Known facts database comparison
Response coherence analysis

Pattern

Based Detection

Data Leakage Prevention

Protection Layers:

PII pattern recognition (Regex-based)
Similarity scoring for memorization detection
Known sensitive pattern matching
Output filtering for sensitive data
Response sanitization checks

GDPR

Compliant

DoS Resilience Testing

Stress Testing Components:

Adaptive load generation algorithms
Resource consumption monitoring
Queue depth analysis
Timeout handling verification
Rate limiting effectiveness testing

10K+

Requests/Test

Bias & Fairness Analysis

Evaluation Metrics:

Demographic parity measurement
Equalized odds calculation
Disparate impact assessment
Counterfactual fairness testing
Intersectional bias detection

360°

Coverage

📈 Performance & Scale

< 100ms

Average Response Time

1M+

Tests Executed Daily

99.99%

Uptime SLA

Real-time

Streaming Results

✅ OWASP LLM Top 10 Compliance

OWASP Category	Coverage	Detection Methods	Complexity
LLM01: Prompt Injection	✓	23+ Detection Patterns	🔥🔥🔥🔥🔥
LLM02: Insecure Output Handling	✓	Output Validation Engine	🔥🔥🔥🔥
LLM03: Training Data Poisoning	✓	Backdoor Detection Algorithms	🔥🔥🔥🔥🔥
LLM04: Model Denial of Service	✓	Resource Exhaustion Testing	🔥🔥🔥🔥
LLM05: Supply Chain Vulnerabilities	✓	Dependency Analysis	🔥🔥🔥
LLM06: Sensitive Information Disclosure	✓	PII Detection + DLP	🔥🔥🔥🔥🔥
LLM07: Insecure Plugin Design	✓	Plugin Security Validation	🔥🔥🔥
LLM08: Excessive Agency	✓	Permission Boundary Testing	🔥🔥🔥🔥
LLM09: Overreliance	✓	Hallucination Detection	🔥🔥🔥🔥
LLM10: Model Theft	✓	Extraction Attack Detection	🔥🔥🔥🔥🔥

💻 Integration Example

// Enterprise Integration Example
const scanner = new GenAISecurityScanner({
    provider: 'openai',
    model: 'gpt-4',
    config: {
        // Advanced Configuration
        parallelism: 10,           // Concurrent test execution
        timeout: 30000,            // 30s timeout per test
        adaptiveTesting: true,     // ML-powered test optimization
        confidenceThreshold: 0.95, // High confidence requirement
        enableCaching: true,       // Performance optimization
        baselineSamples: 100      // Statistical significance
    }
});

// Real-time security monitoring
const results = await scanner.comprehensiveScan({
    tests: ['all'],  // Full OWASP compliance testing
    realtime: true,  // Stream results via SSE
    verbose: true    // Detailed logging
});

console.log(`Security Score: ${results.score}/100`);
console.log(`Vulnerabilities: ${results.vulnerabilities.length}`);

🚀 Innovative Features

🔄 Adaptive Testing

Machine learning algorithms that learn from previous scans to optimize test selection and reduce false positives.

📡 Real-time Streaming

Server-Sent Events (SSE) architecture for live security test monitoring with sub-second latency.

🎯 Precision Scoring

Multi-dimensional scoring algorithm considering severity, exploitability, and business impact.

🔍 Deep Analysis

Goes beyond surface-level testing with recursive payload detection and context-aware analysis.

⚙️ Auto-remediation

Provides actionable remediation steps with code examples and configuration templates.

📊 Analytics Dashboard

Comprehensive reporting with trend analysis, risk heatmaps, and compliance tracking.