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detecting-model-extraction-attacks

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Detect MITRE ATLAS AML.T0024 attacks (model stealing, inversion, membership inference) performed via inference-API abuse, by monitoring per-principal query volume/distribution, rate-limiting and perturbing outputs, and red-teaming your model's extractability. Use for a public or partner inference API needing cloning/inversion/membership-inference detection, or a pre-deployment red-team exercise to measure extraction risk.

When to use

• When you operate a model behind a public or partner inference API and need to detect cloning, inversion, or membership inference.
• When performing a pre-deployment AI red-team exercise to measure how many queries are needed to extract your own model.
• When validating that rate limiting, output perturbation, and confidence-suppression controls actually reduce extractability.
• When investigating anomalous billing/usage spikes that may indicate surrogate-model harvesting.
• When responding to a privacy incident where membership inference against a model is suspected.

When not to use

• querying third-party models without written authorization

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