GhostEyes v2.1.0 — Offensive Reconnaissance Toolkit

Author: 6lackRaven
Status: Active • Stable • Async-first
Docs: See DOCUMENTATION.md • Changelog in CHANGELOG.md

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🧠 What is GhostEyes?

GhostEyes is an async-first, modular offensive reconnaissance toolkit for network and web recon, plus report generation — designed for penetration testers, red teams, and researchers.

• No nested asyncio.run() errors – fixed in v2.1.0
• Colorized CLI, quiet mode, custom output path, and examples flag
• Clean codebase, ready for further v3 refactors (plugins, structured logs, etc.)

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✨ Key Features

• Network Recon

  • ARP subnet scanning
  • VLAN discovery & sampling
  • Layer 2 traceroute
  • DHCP snooping
  • Real-time device tracking

• Web Recon

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

• Reports

  • Generate TXT / JSON / HTML / CSV from prior scans

• CLI UX

  • --version, --examples, --quiet, --output
  • Colorized output
  • One single asyncio.run() (no runtime loop conflicts)

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📦 Installation

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

Python 3.10+ recommended.

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🚀 Quick Start

Show help

python ghosteyes.py -h

Show version

python ghosteyes.py --version

Show practical examples

python ghosteyes.py --examples

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⚙️ Commands & Flags

Global Flags

Flag Description

• --version Show version and author, then exit
• --examples Show common usage examples
• --quiet Suppress verbose logs

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net — Network Reconnaissance

Usage:

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

Options:

Option Description

• -i, --interface Network interface (default: eth0)
• -s, --scan CIDR ARP scan a subnet (e.g., 192.168.1.0/24)
• -v, --vlan Discover VLANs
• -t, --track Track live devices
• -r, --trace IP Layer 2 traceroute to target
• -d, --dhcp Capture DHCP traffic
• --duration N Duration in seconds for vlan/track/dhcp (default: 300)
• --output FILE Where to save results (default: scan_net.json)

Examples:

ARP scan

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

VLAN discovery (2 minutes)

python ghosteyes.py net -i eth0 --vlan --duration 120

Track devices (quiet mode)

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

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web — Web Reconnaissance

Usage:

python ghosteyes.py web -u [-s WORDLIST] [-b WORDLIST] [-T] [-w N] [--output FILE]

Options:

Option Description

• -u, --url Target URL (required)
• -s, --subdomains Subdomain brute-force wordlist
• -b, --bruteforce Directory brute-force wordlist
• -T, --tech Detect technology stack
• -w, --workers Number of concurrent workers (default: 50)
• --output FILE Where to save results (default: scan_web.json)

Examples:

Subdomain scan + tech detection

python ghosteyes.py web -u https://example.com -s subdomains.txt -T

Directory brute-force (quiet mode)

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

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report — Report Generation

Usage:

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

Options:

Option Description

• -f, --file Input scan results file (JSON)
• -t, --type Output formats: txt, json, html, csv, or all

Examples:

HTML + JSON output

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

Everything

python ghosteyes.py report -f scan_web.json -t all

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📂 Output

Unless overridden via --output, scans are saved to:

scan_net.json for net

scan_web.json for web

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🗺️ Roadmap (towards v3)

Plugin system & runtime module discovery

Structured JSON logging and log levels

Config file support (ghosteyes.toml / YAML)

Parallelized scans with cancellation and timeouts

Packaging (pip install ghosteyes) & self-contained binaries

REST API / Web UI

Read more in CHANGELOG.md.

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⚠️ Legal & Ethical Disclaimer

GhostEyes is intended strictly for authorized security testing and educational use. By using this tool, you accept full responsibility for your actions.

Only scan targets you own or have explicit written permission to test.

Comply with applicable laws (e.g., CFAA, GDPR).

The author(s) are not liable for misuse or damage.

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📜 License

MIT — see the LICENSE file.

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💬 Contact

• Author: 6lackRaven
• Reddit: Raven_Obsidian
• Telegram: RavenObsidian
• Facebook: Raven Obsidian
• Youtube: Raven Obsidian

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🤝 Contributing

Contributions are welcome!

1. Fork the repo

2. Create a feature branch: git checkout -b feature/your-feature

3. Commit clean, tested code

4. Open a PR with a clear description

Please follow the established code style & be respectful.

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❤️ Support / Donations

If you’d like to support continued open-source development:

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