Threat Intelligence for AI Systems
Understand and mitigate risks across ML model development, deployment, and operation.
Threat Modeling
Mitigation Strategies
Key Threat Concepts
Data Poisoning
Malicious contamination of training datasets to skew model outcomes
Adversarial Attacks
Inputs designed to deceive trained models through perturbations
Model Theft
Extraction of proprietary models through query-based reconstruction
Threat Modeling Framework
STRIDE Attack Vectors
Steps to Analyze
- 1. Map system architecture components
- 2. Identify potential entry points
- 3. Assign risk priorities (DREAD criteria)
- 4. Design mitigation plans
Common ML-Specific Threats
- Backdoor attacks
- Membership inference
- Stealing private weights
- Evasion attacks
- Model bias amplification
Mitigation Strategies
Input Validation
Implement robust data sanitization and anomaly detection before ingestion
Differential Privacy
Add mathematical noise to training data to prevent model overfitting
Adversarial Training
Hardening models against malicious inputs during development
Access Control
Implement least-principle access policies for sensitive ML assets
Security Tools
Foolbox
Adversarial example library for PyTorch/TensorFlow
Gretel
Detect and remediate data bias in training sets
DeepTest
Automated testing framework for ML vulnerability detection