TensorSuggestLite is an experimental machine learning utility. This policy defines how security vulnerabilities are handled.

As an experimental project, security updates are provided on a best-effort basis without guarantees of timeliness or completeness.

TensorSuggestLite is not intended for production systems or security-sensitive environments. It is designed for:

• Local experimentation
• Educational purposes
• Prototype development

This application:

• Executes arbitrary Python code (TensorFlow model training)
• Reads and writes local files
• Loads untrusted configuration files (JSON/YAML/TOML)
• Has no input sanitization for ML training data
• Has no authentication or access controls

Do not:

• Deploy as a web service
• Process untrusted or sensitive data
• Run with elevated privileges
• Use in multi-tenant environments

Report security issues to:

security.sxnnyside@sxnnysideproject.com

Do not report security vulnerabilities via public GitHub Issues.

Provide:

1. Detailed description of the vulnerability
2. Steps to reproduce
3. Affected versions (if known)
4. Proof of concept (if applicable)
5. Potential impact assessment
6. Suggested mitigation (if any)

1. Acknowledgment: Within 72 hours of report receipt
2. Assessment: Maintainers evaluate severity and impact
3. Resolution:
   • Critical: Best effort within 30 days
   • High: Best effort within 60 days
   • Medium/Low: Addressed in regular development cycle
4. Disclosure: After fix is released or 90 days, whichever comes first

We follow coordinated disclosure:

• Reporters are kept informed of progress
• Fixes are developed privately
• Public disclosure occurs after patch availability or 90 days
• Reporter receives credit unless anonymity is requested

TensorFlow and other dependencies may have known vulnerabilities. Users are responsible for:

• Reviewing dependency security advisories
• Updating dependencies in their deployments
• Testing compatibility after updates

The application processes configuration files without extensive validation. Maliciously crafted files could:

• Cause crashes (denial of service)
• Consume excessive resources
• Trigger unexpected behavior

Mitigation: Only process files from trusted sources.

Generated models, tokenizers, and label encoders:

• Are not signed or verified
• May contain embedded information from training data
• Should not be distributed if trained on sensitive data

The PyQt6 GUI:

• Has no authentication
• Trusts the local filesystem
• Provides full file system access via file dialogs

Mitigation: Run only on trusted local machines.

Users must:

• Comply with applicable laws and regulations
• Respect data privacy and intellectual property
• Not use for malicious purposes
• Understand and accept the experimental nature and limitations

CoreRed Project provides TensorSuggestLite "as is" without warranty. Security issues do not constitute a breach of contract or liability. See LICENSE for full terms.

For legal inquiries related to security: legal.sxnnyside@sxnnysideproject.com

Version: 1.0
Last Updated: 2026-02-03
Contact: security.sxnnyside@sxnnysideproject.com