Machine Learning Security

Machine Learning Security is a specialized field focused on protecting machine learning systems from threats, vulnerabilities, and attacks throughout their lifecycle. It involves securing the data, models, and infrastructure used in ML applications against adversarial manipulation, data poisoning, model theft, and privacy breaches. This discipline ensures that ML systems are robust, trustworthy, and resilient in real-world deployments.

Also known as: ML Security, AI Security, Adversarial Machine Learning, Secure ML, MLSec

Why learn Machine Learning Security?

Developers should learn Machine Learning Security when building or deploying ML models in sensitive or high-stakes environments, such as finance, healthcare, or autonomous systems, to prevent malicious exploitation and ensure compliance with regulations. It is crucial for mitigating risks like adversarial attacks that can cause models to make incorrect predictions, data leakage that compromises privacy, and model inversion that reveals training data. Mastery of this skill helps create secure, reliable AI applications that maintain integrity and user trust.

Learning Resources

• docs: OWASP Machine Learning Security Top 10
• course: Adversarial Machine Learning - Coursera Course
• docs: Machine Learning Security - MITRE ATT&CK Framework
• video: Practical Attacks on Machine Learning Systems - YouTube Tutorial
• book: The Security of Machine Learning - Book by Ling Huang et al.