We take security seriously.

If you discover a security vulnerability in HYPERML, please do not open a public GitHub issue.

Instead, report it responsibly by contacting the project maintainer via private communication.

Include the following details:

• Description of the vulnerability
• Steps to reproduce
• Potential impact
• Suggested mitigation (if known)

You will receive an acknowledgment within a reasonable timeframe.

Only the latest release of HYPERML is actively supported with security updates.

Users are encouraged to keep dependencies up to date.

• Never upload sensitive datasets or credentials
• Avoid committing .env files or secrets
• Use virtual environments
• Keep PyCaret, Streamlit, and dependencies updated
• Validate uploaded datasets before production deployment

When deploying to Streamlit Cloud or other platforms:

• Avoid exposing internal model files publicly
• Restrict access if handling sensitive datasets
• Monitor resource usage to prevent abuse

We appreciate responsible disclosure and will credit reporters when appropriate.

Thank you for helping keep HYPERML secure 🔒