model-extraction

To test AI model resistance to extraction attacks, you can simulate controlled probing scenarios including query-based extraction, distillation, embedding theft, and architecture probing to evaluate defense responses.

Surrogate model fidelity during security testing is measured using detection rates, embedding similarity, and surrogate behavior metrics to evaluate defense effectiveness and vulnerability patterns.

Yes, you can evaluate detection mechanisms for embedding theft and distillation attacks by logging defense responses and analyzing structured detection indicators during simulated extraction attempts.

The best way to assess vulnerability patterns in deployed AI models is running authorized security tests that apply configurable attack techniques and generate structured reporting metrics.

Yes, model extraction testing supports architecture probing scenarios alongside query-based extraction, distillation, and embedding theft to provide comprehensive risk assessment for deployed AI models.

You need to run extraction attack simulations for risk assessment when evaluating how resilient deployed AI models are to unauthorized extraction attempts and strengthening safeguards against identified vulnerabilities.