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AI Logic Security

This section covers security considerations specific to AI-powered features in Firebase applications, including prompt injection protection and output validation.

Overview

AI features in Firebase applications introduce unique security challenges that require specialized protection mechanisms. This section covers best practices for securing AI logic and preventing abuse.

Common Security Issues

Core AI Security

Rate Limiting and Abuse Prevention

Best Practices

Input Validation

  1. Sanitize all user inputs before using in prompts
  2. Implement input length limits to prevent resource exhaustion
  3. Use allowlists for sensitive operations instead of denylists
  4. Validate input format and structure before processing

Output Security

  1. Sanitize AI-generated content before displaying to users
  2. Implement output filtering for sensitive information
  3. Log and monitor AI interactions for security analysis
  4. Rate limit AI operations per user and globally

Access Control

  1. Enable App Check for all AI endpoints
  2. Implement proper authentication for AI features
  3. Use Firebase Security Rules to control AI data access
  4. Monitor AI usage patterns for anomalies

AI-Specific Threats

Prompt Injection

  • Users manipulating prompts to bypass restrictions
  • Injection of malicious instructions into AI context
  • Social engineering through crafted prompts

Data Poisoning

  • Malicious training data affecting model behavior
  • User-provided context contaminating responses
  • Adversarial inputs causing model failures

Resource Exhaustion

  • Expensive AI operations consuming excessive resources
  • Repeated requests leading to billing spikes
  • Model abuse for cryptocurrency mining or other purposes

Monitoring and Detection

  • Track AI request patterns and volumes
  • Monitor for unusual prompt structures
  • Alert on excessive resource consumption
  • Log all AI interactions for security analysis