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Maiks71/authorized-recon
authorized-recon is a machine learning model from Maiks71. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
An automated Python-based reconnaissance tool for authorized security testing, bug-bounty reconnaissance, endpoint discovery, subdomain enumeration, parameter inventory, and passive security assessment.
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Updated Aug 25, 2026
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From the Hugging Face model README
An automated Python-based reconnaissance tool for authorized security testing, bug-bounty reconnaissance, endpoint discovery, subdomain enumeration, parameter inventory, and passive security assessment.
aiohttpClone the repository:
git clone YOUR_REPOSITORY_URL
Enter the project directory:
cd authorized-recon
Install dependencies:
pip install -r requirements.txt
python recon.py example.com
python recon.py example.com example.org
Create a targets.txt file:
example.com
example.org
your-authorized-domain.com
Then run:
python recon.py -i targets.txt
python recon.py example.com -o reports
The tool generates reports for each target.
Example:
reports/
├── example.com.json
├── example.com.html
├── example.org.json
├── example.org.html
└── summary.json
The JSON report contains structured reconnaissance data including:
The HTML report provides a human-readable view of:
The tool focuses on reconnaissance and passive security assessment.
Its workflow is:
Target
↓
Subdomain Discovery
↓
DNS Resolution
↓
Live Host Detection
↓
Endpoint Discovery
↓
Parameter Extraction
↓
Passive Security Checks
↓
JSON + HTML Reports
The current version performs non-destructive checks for indicators such as:
These findings are indicators for manual review and should not automatically be considered confirmed vulnerabilities.
This tool is intended only for systems that you own or are explicitly authorized to test.
Before scanning a bug-bounty target, verify that:
Do not use this tool against systems without authorization.
This project is designed around reconnaissance and passive assessment.
It does not intentionally perform:
The author is not responsible for misuse of this software.
Users are responsible for ensuring that their testing activities comply with applicable laws, contracts, bug-bounty program rules, and authorization requirements.
Subdomain discovery depends on publicly available Certificate Transparency information and therefore cannot guarantee discovery of every subdomain.
Endpoint discovery is also dependent on links, JavaScript references, and known paths exposed by the target.
Passive findings require manual verification before being treated as security vulnerabilities.
Current version:
2.0
The project currently focuses on automated reconnaissance and passive security assessment.
Future versions may improve endpoint discovery, reporting, scope management, crawling efficiency, and result prioritization.
MIT License
Copyright (c) 2026
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.