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🛡️ ReconX

Go Version Python Version CI License Status Security

ReconX is a professional-grade reconnaissance framework designed for high-scale asset discovery, attack surface mapping, and continuous monitoring. Developed as a high-performance Go engine, it orchestrates a curated ecosystem of industry-standard security tools—primarily written in Go and supplemented by specialized Python modules—to provide a scalable and production-ready platform for Bug Bounty hunters, Red Teams, and Security Researchers.


🚀 Core Industrial Features

Unlike traditional recon scripts, ReconX is built as an orchestrated engine:

  • 🧩 Task-Based Architecture: Every module implements a strict Task interface, ensuring a decoupled and extensible pipeline.
  • ⚡ Memory-First Session: Modules communicate via a shared in-memory Session object, drastically reducing disk I/O and increasing speed.
  • 🔄 Resumable Scans: Use the -resume flag to pick up exactly where a previous scan left off.
  • 👁️ Continuous Monitoring: Scheduled execution via -monitor -interval <time> to detect new assets in real-time.
  • 🔐 Secure API Management: API keys are stored outside the project directory (~/.config/reconx/) and managed via a dedicated --setup-api CLI.
  • 🛠️ Self-Healing Dependencies: Automatically detects and attempts to install missing Go/Python tools from official sources.

🗺️ The Reconnaissance Pipeline

ReconX orchestrates 17 specialized phases to provide a complete view of the target's attack surface:

Phase Focus Primary Tools Output
1-2 Asset Discovery subfinder, chaos, github-subdomains subdomains.json
3 DNS Resolution dnsx dns.json
4 Port Discovery naabu, nmap ports.json
5 HTTP Discovery httpx http.json, alive.txt
6 Content Discovery katana crawl.json, urls.txt
7-8 Intel & JS gau, waymore, xnLinkFinder historical.json, javascript.json
9-11 Secrets & Cloud trufflehog, cloud-enum, uncover secrets.json, cloud.json, osint.json
12 Visual Recon gowitness screenshots/, index.html
13 Vuln Discovery nuclei nuclei.json
14-17 Analysis & Ops Internal Engine report.md, diff.md, .scan_history.json

🛠️ Installation

📦 Quick Install (Recommended)

Download the latest pre-compiled binary for your OS from the Releases page.

💻 Build from Source

Prerequisites: Go (latest stable), Python 3.x (with uv).

# Clone the repository
git clone https://github.com/Kronoscba/reconx.git
cd reconx

# Build the binary
go build -o reconx cmd/reconx/main.go

# Configure API Keys (Shodan, Censys, etc.)
./reconx --setup-api

🤖 CI/CD & Automation

ReconX utilizes GitHub Actions to maintain industrial software standards:

  • Continuous Integration: Every push and PR is automatically validated through linting, unit testing, and build checks to ensure stability.
  • Automated Multi-Platform Releases: On every version tag (v*), the framework is cross-compiled for Linux, macOS, and Windows (amd64 & arm64) and uploaded automatically to GitHub Releases.

📖 Usage Examples

Full Pipeline Execution

Run the entire 17-phase pipeline against a target:

./reconx -target example.com

Targeted Task Execution

Execute only a specific module (e.g., just the DNS resolution phase):

./reconx -target example.com -task dnsres

Resuming an Interrupted Scan

./reconx -resume

Continuous Monitoring

Run the pipeline every 24 hours and alert on new findings:

./reconx -target example.com -monitor -interval 24h

🏗️ Architecture

ReconX follows a clean, modular Go structure:

  • cmd/: Entry points and CLI logic.
  • internal/engine/: The orchestrator managing task sequencing, state, and resumability.
  • internal/discovery, internal/dnsres, etc.: Decoupled modules implementing the Task interface.
  • pkg/: Shared utility libraries.

🤝 Contributing

ReconX is designed to be extended. To add a new module:

  1. Implement the Task interface in a new package within internal/.
  2. Register the task in the engine pipeline.
  3. Add necessary tool dependencies to the deps manager.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Professional reconnaissance framework for attack surface mapping, asset discovery, and vulnerability scanning. High-performance Go engine orchestrating a modular security pipeline.

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