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AI Security Fundamentals

Introduction to AI/ML Security

AI security focuses on protecting machine learning systems throughout their lifecycle - from data collection to model deployment and inference.

AI/ML Attack Taxonomy

Data-Level Attacks

Attack Description Impact
Data Poisoning Injecting malicious samples into training data Model learns wrong patterns
Data Extraction Stealing training data through model queries Privacy breach
Label Flipping Changing labels in training data Model misclassification

Model-Level Attacks

Attack Description Impact
Model Extraction Recreating model through API queries IP theft
Model Inversion Reconstructing training data from model Privacy violation
Membership Inference Determining if data was used in training Privacy leakage

Inference-Level Attacks

Attack Description Impact
Adversarial Examples Crafted inputs causing misclassification System manipulation
Evasion Attacks Bypassing ML-based security controls Security bypass
Prompt Injection Manipulating LLM behavior through input Unauthorized actions

Security Controls for AI Systems

Preventive Controls

  • Input validation - Sanitize and validate all inputs
  • Access control - Restrict model and data access
  • Differential privacy - Add noise to protect training data
  • Secure aggregation - Protect data in federated learning

Detective Controls

  • Anomaly detection - Monitor for unusual queries
  • Model monitoring - Track performance drift
  • Audit logging - Record all model interactions

Corrective Controls

  • Model retraining - Update models when attacks detected
  • Rollback mechanisms - Revert to known-good model versions

Secure ML Development Lifecycle

Data Collection → Data Preparation → Model Training → Model Evaluation → Deployment → Monitoring
      ↓                 ↓                  ↓                ↓               ↓            ↓
 Data Privacy     Data Validation    Robust Training   Adversarial     Access       Drift
 Assessment       & Sanitization     Techniques        Testing         Controls     Detection

Best Practices

  1. Treat models as sensitive assets - Apply same controls as production code
  2. Validate training data integrity - Hash and sign datasets
  3. Implement rate limiting - Prevent model extraction attempts
  4. Use ensemble methods - Increase robustness against adversarial attacks
  5. Regular security testing - Include adversarial testing in CI/CD