We release patches for security vulnerabilities in the following versions:

We take the security of Sentiment Analysis LSTM 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 email to: [your.email@example.com]

You should receive a response within 48 hours. If for some reason you do not, please follow up via email to ensure we received your original message.

Please include the following information:

• Type of vulnerability (e.g., XSS, SQLi, etc.)
• Full paths of source file(s) related to the vulnerability
• The location of the affected source code (tag/branch/commit or direct URL)
• Any special configuration required to reproduce the issue
• Step-by-step instructions to reproduce the issue
• Proof-of-concept or exploit code (if possible)
• Impact of the issue, including how an attacker might exploit it

• We will acknowledge receipt of your vulnerability report
• We will send you a more detailed response indicating the next steps
• We will work with you to understand and resolve the issue
• We will keep you informed about our progress
• We will credit you in the security advisory (if you wish)

When using this package in production:

• Store trained models in secure locations
• Use access controls for model files
• Validate model integrity before loading

• Always validate user input before processing
• Set appropriate text length limits
• Sanitize inputs to prevent injection attacks

• Use HTTPS in production
• Implement rate limiting
• Add authentication and authorization
• Configure CORS appropriately
• Use environment variables for sensitive configuration

• Regularly update dependencies
• Use safety to check for known vulnerabilities
• Pin dependency versions in production

• Don't log sensitive user data
• Implement proper data retention policies
• Comply with GDPR/CCPA if applicable

• Don't run containers as root
• Use minimal base images
• Scan images for vulnerabilities
• Keep base images updated

• HTTPS enabled
• Authentication implemented
• Rate limiting configured
• Input validation in place
• Dependencies updated
• Security headers configured
• Logging and monitoring enabled
• Secrets stored securely (not in code)
• Regular security audits scheduled
• Incident response plan in place

• Keep TensorFlow updated to latest stable version
• Be aware of potential model poisoning attacks
• Validate model files from untrusted sources

• Configure CORS appropriately for your use case
• Implement proper authentication
• Use HTTPS in production
• Enable rate limiting

We use the following tools to maintain security:

• Bandit: Python security linter
• Safety: Dependency vulnerability checker
• Pre-commit hooks: Automated security checks
• GitHub Security Alerts: Dependency vulnerability notifications
• CodeQL: Code security analysis (via GitHub Actions)

When we receive a security report, we will:

1. Confirm the problem and determine affected versions
2. Audit code to find similar problems
3. Prepare fixes for all supported versions
4. Release new versions as soon as possible

If you have suggestions on how this process could be improved, please submit a pull request or open an issue.