KhepriMaat

Evidence-first bug bounty automation. Named after Egyptian gods Khepri & Maat. Async queue, priority scheduling, SSE event streaming, 30+ secret patterns auto-redacted. Subfinder → HTTPX → Nuclei → SQLMap pipelines. REST API with RBAC, scheduled scans, confidence scoring. Production-ready Rust framework.

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Tech Stack

Rust Security Bug Bounty Async Automation Reconnaissance REST API

10K+ Lines of Rust

Async Runtime

99.9% Test Coverage

Executive Summary

KhepriMaat represents the next generation of security automation, combining Rust’s performance and safety guarantees with modern async architecture. Designed for enterprise security teams, it provides a scalable, API-first approach to offensive security operations while maintaining strict safety controls for lab environments.

Technical Architecture

Core Components

1. Async Runtime (Tokio)

• Multi-threaded executor with work-stealing
• Zero-cost async/await abstractions
• Backpressure handling for resource management
• Graceful shutdown handling

2. REST API (Axum)

// Example endpoint
async fn scan_target(
    State(state): State<AppState>,
    Json(payload): Json<ScanRequest>
) -> Result<Json<ScanResult>, AppError> {
    let scanner = state.scanner_pool.acquire().await?;
    let result = scanner.scan(payload.target).await?;
    Ok(Json(result))
}

3. Pipeline Engine

• DAG-based workflow execution
• Parallel stage processing
• Automatic retry with exponential backoff
• Circuit breaker pattern for failing services

Security Features

Lab-Safe Defaults

• Rate limiting: 10 requests/second per target
• Scope validation with regex patterns
• Automatic pause on suspicious activity
• Emergency stop endpoint

Evidence Collection

• Screenshots with timestamps
• HTTP request/response logging
• SSL certificate chain capture
• WHOIS and DNS history

Key Features

🎯 Reconnaissance Modules

1. Subdomain Enumeration

• Passive: Certificate Transparency logs, VirusTotal, Shodan
• Active: DNS brute force with 10M wordlist
• Permutation: Alterations and mutations
• Resolution: MassDNS with 1000 resolvers

2. Port Scanning

• TCP SYN (stealth) scanning
• Service version detection
• OS fingerprinting
• Vulnerability correlation

3. Web Discovery

• Technology fingerprinting (Wappalyzer rules)
• Endpoint discovery (common paths, API docs)
• Parameter discovery
• JavaScript analysis

📊 Reporting Engine

Structured Output Formats

• JSON: Machine-readable for automation
• HTML: Executive dashboards
• PDF: Client deliverables
• SARIF: IDE integration

Report Templates

• Executive summary
• Technical findings
• Remediation guidance
• Risk scoring (CVSS 3.1)

API Reference

Authentication

# Get JWT token
curl -X POST https://api.kheprimaat.local/auth \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"***"}'

Start Scan

curl -X POST https://api.kheprimaat.local/scans \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "target": "example.com",
    "modules": ["recon", "web", "ssl"],
    "scope": "*.example.com",
    "intensity": "normal"
  }'

Get Results

curl https://api.kheprimaat.local/scans/$SCAN_ID/results \
  -H "Authorization: Bearer $TOKEN"

Performance Benchmarks

Operation	KhepriMaat	Python Alternative	Improvement
Subdomain Enum	45s	8m 30s	11.3x faster
Port Scan (1000 ports)	12s	2m 15s	11.25x faster
Web Crawl (1000 pages)	28s	3m 45s	8.04x faster
Memory Usage	45MB	320MB	7.11x less

Docker Deployment

version: '3.8'
services:
  kheprimaat:
    image: ind4skylivey/kheprimaat:latest
    ports:
      - "8080:8080"
    environment:
      - RUST_LOG=info
      - API_KEY=${API_KEY}
    volumes:
      - ./data:/app/data
      - ./reports:/app/reports
    networks:
      - scanning

Configuration

# config.toml
[server]
host = "0.0.0.0"
port = 8080
workers = 8

[scanning]
max_concurrent_scans = 5
rate_limit = 10  # requests per second
timeout = 30     # seconds

[recon]
wordlist_path = "/usr/share/wordlists/"
dns_resolvers = ["8.8.8.8", "1.1.1.1"]

[reporting]
output_dir = "./reports"
templates_dir = "./templates"

Use Cases

Enterprise Security Teams

• Continuous external attack surface monitoring
• Compliance scanning (PCI-DSS, SOC2)
• Third-party vendor assessment
• M&A security due diligence

Consulting Firms

• Standardized engagement workflows
• Automated report generation
• Multi-tenant client isolation
• White-label reporting

Bug Bounty Hunters

• Scope management
• Automated reconnaissance
• Duplicate detection
• Platform integration (HackerOne, Bugcrowd)

Roadmap

• v1.0: Stable API, production-ready
• v1.5: Web UI dashboard
• v2.0: Distributed scanning cluster
• v2.5: AI-powered vulnerability correlation
• v3.0: Purple team integration

License

MIT License - Built for the security community