The following versions of NLP-Email-Categorizer are currently supported with security updates:

We recommend using the latest version from the repository to ensure you have the most recent security fixes and improvements.

If you discover a security vulnerability in NLP-Email-Categorizer, we appreciate your help in disclosing it responsibly. Please follow these steps:

1. Do Not Disclose Publicly: Avoid sharing details of the vulnerability in public forums, such as GitHub issues, social media, or other platforms, until it has been addressed.
2. Contact the Maintainer Privately:
   • Create a private issue or discussion on the GitHub repository.
   • Include a detailed description of the vulnerability, steps to reproduce, and potential impact.
3. Response Time:
   • You can expect an initial response within 48 hours.
   • We will work with you to validate and address the issue promptly.
4. Disclosure:
   • Once the vulnerability is fixed, we will coordinate with you on public disclosure, if appropriate.
   • Credit will be given for your discovery in release notes, unless you prefer anonymity.

To keep your use of NLP-Email-Categorizer secure:

• Use Trusted Sources: Download or clone the project only from the official GitHub repository.
• Secure Dependencies: Regularly update dependencies (e.g., scikit-learn, nltk) to their latest secure versions using pip install --upgrade.
• Input Validation: The notebooks process user-provided datasets and text inputs. Avoid using untrusted datasets to prevent injection or parsing issues.
• Run in Trusted Environments: Execute notebooks in secure environments (e.g., local Jupyter, trusted Colab instances) to avoid exposing sensitive data.
• Dataset Privacy: Ensure your dataset does not contain sensitive information (e.g., personal email subjects), as the notebooks do not encrypt data.
• Model Storage: Store saved models (*.joblib) and zip files securely, as they may contain serialized data from your dataset.

NLP-Email-Categorizer relies on the following third-party libraries, which may have their own security policies:

• pandas, numpy, scikit-learn, nltk, matplotlib, seaborn, joblib, ipywidgets

Check the respective project pages for security advisories and ensure you’re using the versions specified in the notebooks or their latest secure releases.

Thank you for helping keep NLP-Email-Categorizer secure!