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GhostEyes: Next-Gen Reconnaissance Toolkit for Red Teams & Pentesters


GitHub: https://github.com/6lackRaven/GhostEyes


GhostEyes is an asynchronous-first, modular offensive reconnaissance toolkit built for penetration testers, red team operators, and security researchers. It combines network and web recon capabilities with streamlined report generation — all within a modern, clean codebase.


What’s New in v2.1.0

  • Fixed asyncio.run() nesting issues (no more runtime conflicts)
  • Colorized CLI output, quiet mode, and customizable output paths
  • --examples flag for quick onboarding
  • Clean structure, ready for future v3 enhancements (plugin system, structured logs, etc.)

Key Features

Network Reconnaissance

  • ARP subnet scanning
  • VLAN discovery and sampling
  • Layer 2 traceroute
  • DHCP traffic monitoring
  • Real-time device tracking

Web Reconnaissance

  • Subdomain brute-force
  • Directory brute-force
  • Technology stack detection

Reporting

  • Generate reports in TXT, JSON, HTML, and CSV formats
  • Supports multiple output types from previous scans

CLI User Experience

  • Support for --version, --examples, --quiet, --output
  • Colorized terminal output
  • Single asyncio.run() ensures compatibility with async workflows

Installation

bashCopyEditgit clone https://github.com/6lackRaven/GhostEyes.git
cd GhostEyes
pip install -r requirements.txt

Python 3.10 or higher is recommended.

Quick Start

bashCopyEdit# Show help
python ghosteyes.py -h

# Show version
python ghosteyes.py --version

# View example usage
python ghosteyes.py --examples


Command Overview

net — Network Reconnaissance

bashCopyEditpython ghosteyes.py net -i [--scan CIDR | --vlan | --track | --trace IP | --dhcp] [--duration N] [--output FILE]

OptionDescription
-i, --interfaceSpecify network interface (default: eth0)
-s, --scanARP scan a subnet (e.g., 192.168.1.0/24)
-v, --vlanDiscover VLANs
-t, --trackTrack live devices
-r, --traceLayer 2 traceroute to a target IP
-d, --dhcpCapture DHCP traffic
--durationDuration (seconds) for vlan, track, or dhcp
--outputOutput file path (default: scan_net.json)

Examples:

bashCopyEdit# ARP scan
python ghosteyes.py net -i eth0 --scan 192.168.1.0/24 --output my_scan.json

# VLAN discovery
python ghosteyes.py net -i eth0 --vlan --duration 120

# Device tracking
python ghosteyes.py net -i wlan0 --track --duration 60 --quiet


web — Web Reconnaissance

bashCopyEditpython ghosteyes.py web -u https://example.com [OPTIONS]

OptionDescription
-u, --urlTarget URL (required)
-s, --subdomainsSubdomain brute-force wordlist
-b, --bruteforceDirectory brute-force wordlist
-T, --techDetect web technology stack
-w, --workersNumber of concurrent workers (default: 50)
--outputOutput file (default: scan_web.json)

Examples:

bashCopyEdit# Subdomain scan with tech detection
python ghosteyes.py web -u https://example.com -s subdomains.txt -T

# Directory brute-force
python ghosteyes.py web -u https://example.com -b dirs.txt --quiet


report — Generate Recon Reports

bashCopyEditpython ghosteyes.py report -f scan_net.json -t html json

OptionDescription
-f, --fileInput file path (must be a JSON scan result)
-t, --typeOutput format(s): txt, json, html, csv, all

Examples:

bashCopyEdit# Generate HTML and JSON reports
python ghosteyes.py report -f scan_net.json -t html json

# Generate all report formats
python ghosteyes.py report -f scan_web.json -t all


Output Files

Scan TypeDefault Output File
Networkscan_net.json
Webscan_web.json

Use --output to customize the filename and path.


Roadmap Toward v3

Planned enhancements include:

  • Plugin-based architecture
  • Structured logging (JSON with log levels)
  • Config file support via .toml or .yaml
  • Improved concurrency with timeouts and cancellation
  • Packaging via PyPI (pip install ghosteyes)
  • Self-contained binaries and REST API/Web UI interface

Details available in CHANGELOG.md.


GhostEyes is provided strictly for ethical security research and authorized testing. By using this tool, you agree to:

  • Only scan targets you own or are explicitly authorized to test
  • Comply with local and international laws (e.g., CFAA, GDPR)
  • Accept full responsibility for any actions performed

The authors disclaim liability for misuse or damage caused by this tool.


License

Released under the MIT License.


Contact


Contributing

Interested in contributing?

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/my-feature
  3. Write clean, tested code
  4. Submit a pull request with a clear description

Follow the existing code style and maintain respectful collaboration.

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