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Machine Learning Security: Threats, Attacks, and Evaluation

Security Objectives for Machine Learning

Explore the four core security objectives of machine learning systems: confidentiality, integrity, availability, and privacy. This lesson helps you understand how to define system boundaries, identify potential threats, and recognize concrete evidence of security failures specific to ML APIs. You'll learn to distinguish between similar concepts like privacy and confidentiality and apply a practical checklist to evaluate ML security claims effectively.

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Let’s ground four key security objectives in one running example before naming them. An image-classification API can feel simple until you decide what the endpoint returns and what the service promises. A POST /classify that returns only a top-1 label exposes less than a response that returns top-kk probabilities, a confidence score, or internal embeddings that a downstream service might use for search. The same endpoint can also carry an expectation that it responds in tens of milliseconds and stays up even when many clients query it at once.

Those interface and operational choices already create a security problem that is bigger than “don’t get hacked”. More output detail can leak more about the model and data, and tighter latency and uptime expectations make denial of service more damaging. To talk about these risks precisely, ML systems commonly use four objectives as a vocabulary: confidentiality, integrity, ...