Skip to main content

Automated reconnaissance tool with AI report generation. Subdomain enumeration, endpoint crawling, and vulnerability pattern detection.

Project description

HB-Recon

PyPI version PyPI - Downloads Python License Stars

Automated reconnaissance workflow with AI-ready report generation.

Perfect for bug bounty hunters, pentesters, and security researchers.


⚠️ Important Notice

HB-Recon is a workflow automation tool — it orchestrates external security tools and generates structured reports.

What it does:

  • ✅ Automates reconnaissance workflows
  • ✅ Chains multiple tools together
  • ✅ Generates AI-ready JSON reports
  • ✅ Organizes scan results

What it does NOT do:

  • ❌ Include built-in scanning capabilities
  • ❌ Auto-install required tools
  • ❌ Work without dependencies

Features

Subdomain Enumeration — subfinder integration
Live Host Detection — httpx for alive checks
Technology Stack Scan — WhatWeb detection
Endpoint Crawling — Katana deep crawl (depth 3)
Vulnerability Patterns — gf pattern matching (XSS, SQLi, IDOR)
AI Report Generation — Structured JSON with risk scoring


Prerequisites

System Requirements

  • OS: Linux / WSL (Windows Subsystem for Linux)
  • Python: 3.8+
  • Go: 1.19+ (for tool installation)

Required External Tools

You MUST install these tools before using hb-recon:

Tool Purpose Installation
subfinder Subdomain enumeration go install -v github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest
httpx HTTP probe go install -v github.com/projectdiscovery/httpx/cmd/httpx@latest
katana Web crawler go install -v github.com/projectdiscovery/katana/cmd/katana@latest
gf Pattern matcher go install github.com/tomnomnom/gf@latest
whatweb Tech detection sudo apt install whatweb (Debian/Ubuntu)

Quick Install (All Tools)

# Install Go tools
go install -v github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest
go install -v github.com/projectdiscovery/httpx/cmd/httpx@latest
go install -v github.com/projectdiscovery/katana/cmd/katana@latest
go install github.com/tomnomnom/gf@latest

# Install WhatWeb
sudo apt install whatweb

# Verify installations
subfinder -version
httpx -version
katana -version
gf -h
whatweb --version

Installation

Option 1: PyPI (Recommended)

pip install hb-recon

Option 2: From Source

git clone https://github.com/infohlaingbwar/hb-recon.git
cd hb-recon
pip install -e .

Usage

Interactive Mode

python -m hb_recon

Example:

=======================================================
  Auto Recon -> AI Ready
=======================================================

[>] Domain: example.com

[+] Subfinder + Httpx
[v] Done (5.2s)

[*] WhatWeb + Katana (parallel)...
[+] Katana (crawl)
[v] Done (12.4s)

[+] gf (XSS/SQLi/IDOR patterns)
[v] Done (1.8s)

[√] Total: 19.4s

Output Structure

recon_example.com/
├── subdomains.txt      # All discovered subdomains
├── alive.txt           # Live hosts (200, 301, 403)
├── urls.txt            # Crawled endpoints (depth 3)
├── xss.txt             # XSS-prone endpoints
├── sqli.txt            # SQLi-prone endpoints
├── idor.txt            # IDOR-prone endpoints
├── tech_stack.txt      # Technology detection
└── ai_report.json      # AI-ready structured report

AI Report Format

The tool generates ai_report.json with structured data perfect for AI analysis:

{
  "target": "example.com",
  "timestamp": "2026-06-20T13:45:00",
  "summary": {
    "subdomains": 15,
    "alive_hosts": 8,
    "endpoints": 324,
    "technologies": 12,
    "high_risk": 3,
    "medium_risk": 7,
    "low_risk": 15
  },
  "endpoints": [
    {
      "url": "https://admin.example.com/api/users?id=123",
      "category": "idor",
      "risk": "high",
      "params": ["id"]
    }
  ],
  "tech_stack": {
    "server": "nginx/1.18.0",
    "frameworks": ["React", "Node.js"],
    "cms": "WordPress 6.2"
  }
}

Use with AI:

# After scan
cat recon_example.com/ai_report.json | pbcopy
# Paste into ChatGPT/Claude: "Analyze this recon data for vulnerabilities"

Workflow Logic

Input: Domain
    ↓
1. Subdomain Enumeration (subfinder)
    → hackertarget, waybackarchive sources
    ↓
2. Live Detection (httpx)
    → Filter 200, 301, 403 status codes
    ↓
3. Parallel Execution:
    ├─→ Tech Stack (WhatWeb)
    └─→ Endpoint Crawl (Katana depth=3)
    ↓
4. Pattern Detection (gf)
    ├─→ XSS patterns
    ├─→ SQLi patterns
    └─→ IDOR patterns
    ↓
5. AI Report Generation
    → Risk scoring
    → Category grouping
    → JSON export

Example Workflow

# 1. Install hb-recon
pip install hb-recon

# 2. Run scan
python -m hb_recon
# Enter: bugcrowd.com

# 3. Wait 30-60 seconds

# 4. Check results
cd recon_bugcrowd.com
cat ai_report.json

# 5. Analyze with AI
# Copy ai_report.json content to ChatGPT/Claude

Security Notice

⚠️ Only use on authorized targets.

This tool is for:

  • Bug bounty programs (with scope)
  • Authorized penetration testing
  • Your own infrastructure

Unauthorized scanning is illegal and violates:

  • Computer Fraud and Abuse Act (CFAA)
  • Most countries' cybercrime laws
  • Bug bounty program rules

You are responsible for your actions.


Troubleshooting

"Command not found" errors

Problem: Tool binaries not in PATH

Solution:

# Add Go bin to PATH (add to ~/.bashrc or ~/.zshrc)
export PATH="$HOME/go/bin:$PATH"

# Reload shell
source ~/.bashrc

"Platform Error" on Windows

Problem: hb-recon requires Linux/WSL

Solution:

# Use WSL (Windows Subsystem for Linux)
wsl -d kali-linux
pip install hb-recon
python -m hb_recon

Network timeouts

Problem: Slow/unstable connection

Solution:

# Increase timeout in cli.py
# Default: timeout=300 (5 minutes)

Contributing

Contributions welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Submit a pull request

License

MIT License - See LICENSE


Author

Hlaing Bwar


Made with ❤️ for the bug bounty community

Disclaimer: This tool is for educational and authorized testing only. Misuse may result in legal consequences.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hb_recon-1.0.2.tar.gz (14.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hb_recon-1.0.2-py3-none-any.whl (11.6 kB view details)

Uploaded Python 3

File details

Details for the file hb_recon-1.0.2.tar.gz.

File metadata

  • Download URL: hb_recon-1.0.2.tar.gz
  • Upload date:
  • Size: 14.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.14

File hashes

Hashes for hb_recon-1.0.2.tar.gz
Algorithm Hash digest
SHA256 40412474f028b5b8d5b043819f62a1b26b180cb20cdf1b1692ad0ee3d49320c2
MD5 eb6cd57e463948c1ff475467ecc668c0
BLAKE2b-256 52ef122400baf67a7b7b3222f2834060aa07f10ad632f7c87447ef67a738ec72

See more details on using hashes here.

File details

Details for the file hb_recon-1.0.2-py3-none-any.whl.

File metadata

  • Download URL: hb_recon-1.0.2-py3-none-any.whl
  • Upload date:
  • Size: 11.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.14

File hashes

Hashes for hb_recon-1.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f521b9bac5fd7ee133eddae647fa8ac6a9a65d901a807148a5948492c292f5fa
MD5 8f9111a83878827c370f28f7a7f70364
BLAKE2b-256 5d4080954ee6b042f8ef4ca4b1f9c739ff3286f59ee5a30b42868cdbcc075f30

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page