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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.

skills/detecting-model-extraction-attacks/SKILL.md

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# Detecting Model Extraction Attacks

> **Authorized Use Only:** The extraction, inversion, and membership-inference techniques described here are intended for defenders testing their own models and for red teams operating under written authorization. Querying a third-party model to clone it, reconstruct its training data, or infer membership without permission may violate terms of service, copyright, and privacy law.

## Overview

Model extraction is the family of attacks in which an adversary abuses a model's **inference API** to steal value that the model owner intended to keep private. MITRE ATLAS catalogs these under **AML.T0024 — Exfiltration via AI Inference API**, in the *Exfiltration* tactic, with three sub-techniques:

- **AML.T0024.000 — Infer Training Data Membership** (membership inference): the adversary determines whether a specific record was part of the training set, a privacy violation that can expose, for example, whether a patient's record trained a medical model.
- **AML.T0024.001 — Invert AI Model** (model inversion): the adversary reconstructs representative training inputs (e.g., faces, text) by exploiting confidence scores returned by the API.
- **AML.T0024.002 — Extract ML Model** (model stealing): the adversary repeatedly queries the victim model, collects (input, prediction) pairs, and trains a *surrogate* model offline that mimics the victim's decision boundary — avoiding the per-query cost of a Machine-Learning-as-a-Service offering and stealing the owner's intellectual property.

All three share a common signal: an attacker must send **many queries**, often crafted to probe the decision boundary (high-entropy, near-boundary, synthetic, or systematically grid-sampled inputs), and frequently requests **full confidence vectors / logits** rather than just the top label. Detection therefore centers on per-principal query monitoring, input-distribution analysis, and confidence-exposure controls, while defense centers on rate limiting, output perturbation, and reducing the information returned per query. This skill follows the MITRE ATLAS technique definition for AML.T0024 (https://atlas.mitre.org/techniques/AML.T0024) and the NIST AI RMF MEASURE function (MEASURE-2.6, security and resilience of the AI system).

## When to Use