Machine Learning Security

ML security addresses attacks and defenses specific to machine learning systems, including adversarial examples, data poisoning, and model extraction.

Machine learning security covers the unique vulnerabilities in ML systems that don't exist in traditional software.

Attack categories: - Evasion Attacks: Adversarial inputs that fool models - Poisoning Attacks: Corrupting training data - Model Extraction: Stealing model through queries - Model Inversion: Extracting training data from models - Membership Inference: Determining if data was in training set

Defense strategies: - Adversarial training (train on adversarial examples) - Input validation and preprocessing - Model monitoring for anomalies - Differential privacy in training - Rate limiting model access - Watermarking for IP protection

Secure ML lifecycle: - Validate data sources and integrity - Secure training infrastructure - Test for adversarial robustness - Monitor production deployments

Why It Matters

As ML models make increasingly consequential decisions—from credit scoring to medical diagnosis—attacks targeting these models can cause real-world harm. Adversarial examples that are imperceptible to humans can completely fool ML systems, and data poisoning during training can introduce persistent backdoors. Organizations deploying ML in production must treat model security with the same rigor as application security.

Key Points

ML attacks are fundamentally different from traditional exploits

Adversarial examples can be imperceptible to humans

Data poisoning can happen during training

Model monitoring essential for production

Differential privacy helps protect training data

Applicable Compliance Frameworks

ISO 27001

Related Terms

LLM Security

LLM security addresses the unique risks of deploying Large Language Models, including prompt injection, data leakage, and adversarial attacks on AI systems.

AI Risk Management

AI risk management systematically identifies, assesses, and mitigates risks unique to artificial intelligence systems throughout their lifecycle.