Record summary
A quick snapshot of what this page covers.
Attack context
How this AI attack works in practice.
- ATLAS ID
- AML.T0043.001
- Priority score
- 61
Mitigations
Defenses that may help against this attack.
AML.M0015 - Adversarial Input Detection
Monitor queries and query patterns to the target model, block access if suspicious queries are detected.
AML.M0019 - Control Access to AI Models and Data in Production
Access controls on model APIs can deny adversaries the access required for black-box optimization methods.
AML.M0010 - Input Restoration
Input restoration adds an extra layer of unknowns and randomness when an adversary evaluates the input-output relationship.
AML.M0003 - Model Hardening
Hardened models are more robust to adversarial inputs.
AML.M0002 - Passive AI Output Obfuscation
Obfuscating model outputs reduces an adversary's ability to create effective adversarial inputs.
AML.M0004 - Restrict Number of AI Model Queries
Restricting the number of queries to the model limits or slows an adversary's ability to perform black-box optimization attacks.
AML.M0006 - Use Ensemble Methods
Using an ensemble of models increases the difficulty of crafting effective adversarial data and improves overall robustness.
Case studies
Examples from public reports and exercises.
Microsoft Edge AI Evasion
The Azure Red Team performed a red team exercise on a new Microsoft product designed for running AI workloads at the edge. This exercise was meant to use an automated system to continuously manipulate a target image to cause the ML model to produce misclassifications.
Botnet Domain Generation Algorithm (DGA) Detection Evasion
The Palo Alto Networks Security AI research team was able to bypass a Convolutional Neural Network based botnet Domain Generation Algorithm (DGA) detector using a generic domain name mutation technique. It is a generic domain mutation technique which can evade most ML-based DGA detection modules. The generic mutation technique evades most ML-based DGA detection modules DGA and can be used to test the effectiveness and robustness of all DGA detection methods developed by security companies in the industry before they is deployed to the production environment.
Source
Where this page information comes from.
Original source
Original source links
Open the public records and source datasets used for this page.