We release patches for security vulnerabilities in the following versions:

We take the security of BILLIONS ML Prediction System 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: security@yourdomain.com
• GitHub Security Advisories: Report a vulnerability

Please include the following information in your report:

• Type of vulnerability
• Full paths 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

• Initial Response: Within 48 hours
• Status Update: Within 7 days
• Fix Timeline: Depends on severity (Critical: 7 days, High: 14 days, Medium: 30 days)

1. Protect Your API Keys

   • Never commit .env files to version control
   • Use environment variables for sensitive data
   • Rotate API keys periodically

2. Keep Dependencies Updated

   pip install --upgrade -r requirements.txt

3. Use Virtual Environments

   • Isolate project dependencies
   • Prevent system-wide package conflicts

4. Validate Input Data

   • Never trust external data sources completely
   • Implement data validation and sanitization

5. Secure Database Access

   • Use strong passwords for production databases
   • Limit database user permissions
   • Enable encryption for sensitive data

1. Code Reviews

   • All code changes should be reviewed
   • Look for security vulnerabilities during reviews

2. Input Validation

   • Validate all user inputs
   • Sanitize data before database operations
   • Prevent SQL injection and XSS attacks

3. Dependency Management

   • Keep dependencies up to date
   • Monitor for security advisories
   • Use tools like pip-audit or safety

4. Secrets Management

   • Never hardcode credentials
   • Use environment variables
   • Exclude sensitive files in .gitignore

5. Error Handling

   • Don't expose sensitive information in error messages
   • Log errors securely
   • Implement proper exception handling

• Alpha Vantage free tier: 5 requests/minute, 500 requests/day
• Yahoo Finance: No official rate limits, but implement respectful delays
• Implement caching to minimize API calls

• Stock market data is public information
• User predictions and settings are stored locally
• No personal information is collected or transmitted

This project relies on several third-party packages. We recommend:

• Regularly updating dependencies
• Reviewing security advisories
• Using virtual environments

• Default SQLite database is stored locally
• For production: Use encrypted connections
• Implement access controls for sensitive data
• Regular backups recommended

We use the following tools to maintain security:

• GitHub Dependabot: Automated dependency updates
• CodeQL: Static analysis for vulnerabilities
• Flake8: Python code linting
• pip-audit: Python package vulnerability scanner

Before deploying to production:

• All API keys stored in environment variables
• .env file added to .gitignore
• Dependencies updated to latest secure versions
• Database connection uses encryption (if applicable)
• Input validation implemented
• Error messages don't expose sensitive information
• Logging configured securely
• Rate limiting implemented for APIs
• HTTPS enabled for web deployment
• Security headers configured

# Install pip-audit
pip install pip-audit

# Scan for vulnerabilities
pip-audit

# Install security linters
pip install bandit safety

# Run security checks
bandit -r funda/ db/
safety check

• OWASP Top 10
• Python Security Best Practices
• SQLAlchemy Security
• Dash Security

We thank the security researchers and contributors who help keep BILLIONS secure.

Security is everyone's responsibility. If you see something, say something! 🔒

Report a Vulnerability