Validate AI Model - AI Mitigation
AI MitigationValidate that AI models perform as intended by testing for backdoor triggers, potential for data leakage, or adversarial influence. Monitor AI model for concept drift and training data drift, which may indicate data tampering and poisoning.
Overview
A source-backed snapshot of this defense.
Safeguard details
Where this defense applies and how the source classifies it.
- ATLAS ID
- AML.M0008
- Priority score
- 40
Covered techniques
Attacks this defense is designed to help with.
AML.T0043 - Craft Adversarial Data
Validating an AI model against adversarial data can ensure the model is performing as intended and is robust to adversarial inputs.
AML.T0043.004 - Insert Backdoor Trigger
Validating that an AI model does not respond to backdoor triggers can help increase confidence that the model has not been poisoned.
AML.T0057 - LLM Data Leakage
Robust evaluation of an AI model can be used to detect privacy concerns, data leakage, and potential for revealing sensitive information.
AML.T0018 - Manipulate AI Model
Validating an AI model against a wide range of adversarial inputs can help increase confidence that the model has not been manipulated.
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Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.