We take the security of AI_PRATICE seriously. If you believe you have found a security vulnerability, please report it to us as described below.

Please do NOT report security vulnerabilities through public GitHub issues.

Instead, please report them via one of the following methods:

1. GitHub Security Advisories (Preferred)

   • Go to the Security tab of this repository
   • Click "Report a vulnerability"
   • Fill out the form with details about the vulnerability

2. Email

   • Send an email to the repository maintainer through GitHub
   • Include the word "SECURITY" in the subject line
   • Provide detailed information about the vulnerability

Please include the following information in your report:

• Type of vulnerability (e.g., code injection, dependency vulnerability, etc.)
• Full path(s) of source file(s) related to the vulnerability
• Location of the affected source code (tag/branch/commit or direct URL)
• Step-by-step instructions to reproduce the issue
• Proof-of-concept or exploit code (if possible)
• Impact of the vulnerability, including how an attacker might exploit it
• Any potential solutions or mitigations you've identified

• Initial Response: Within 48 hours of report submission
• Status Update: Within 7 days with assessment and expected timeline
• Resolution: Varies based on severity and complexity
  • Critical: 1-7 days
  • High: 7-14 days
  • Medium: 14-30 days
  • Low: 30-90 days

This repository follows these security practices:

• Dependency Management: Regular updates to third-party libraries
• Code Review: All contributions are reviewed before merging
• Static Analysis: Automated security scanning where applicable
• Best Practices: Following Python and ML security guidelines

When using this repository:

1. Keep Dependencies Updated

   pip install --upgrade numpy pandas matplotlib seaborn scikit-learn mlxtend networkx umap-learn scipy

2. Use Virtual Environments

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Validate Data Sources

   • Always validate and sanitize external data inputs
   • Be cautious with untrusted CSV files or datasets

4. Review Code Before Execution

   • Understand what each script does before running it
   • Be especially careful with scripts that access external resources

• We will respond to your report within 48 hours with next steps
• We will keep you informed throughout the resolution process
• We will credit you in the security advisory (unless you prefer to remain anonymous)
• We will not take legal action against you if you:
  • Make a good faith effort to avoid privacy violations and data destruction
  • Do not exploit the vulnerability beyond what is necessary for verification
  • Report the vulnerability to us promptly

We ask that you:

• Give us reasonable time to fix the vulnerability before public disclosure
• Do not access, modify, or delete data belonging to others
• Do not perform actions that could negatively impact users or services
• Do not publicly disclose the vulnerability until we have issued a fix

We value the security community's efforts. Security researchers who responsibly disclose vulnerabilities will be:

• Acknowledged in the security advisory (with permission)
• Listed in our Hall of Fame (if applicable)
• Given credit in release notes for the fix

• OWASP Top 10
• CWE - Common Weakness Enumeration
• Python Security Best Practices

For any security-related questions or concerns, please:

• Open a private security advisory on GitHub
• Contact the maintainers through GitHub

Thank you for helping keep AI_PRATICE and its users safe! 🙏