Record summary
A quick snapshot of what this page covers.
Tactics1Attacker goals connected to this method.
Mitigations3Defenses that may help against this attack.
AI risks0Research-backed risks connected to this topic.
Attack context
How this AI attack works in practice.
- ATLAS ID
- AML.T0014
- Priority score
- 19
Maturity: feasible
Discovery
Mitigations
Defenses that may help against this attack.
AML.M0002 - Passive AI Output Obfuscation
DeploymentML Model Evaluation
Suggested approaches:
- Restrict the number of results shown
- Limit specificity of output class ontology
- Use randomized smoothing techniques
- Reduce the precision of numerical outputs
AML.M0004 - Restrict Number of AI Model Queries
Business and Data UnderstandingDeployment+1 more
Limit the amount of information an attacker can learn about a model's ontology through API queries.
AML.M0006 - Use Ensemble Methods
ML Model Engineering
Use multiple different models to fool adversaries of which type of model is used and how the model used.
Case studies
Examples from public reports and exercises.
No case studies found. No public example is connected to this attack in the current data.
Source
Where this page information comes from.
Original source
Original source links
Open the public records and source datasets used for this page.