Discover AI Model Family - AI Security Technique
AI Security TechniqueAdversaries may discover the general family of model. General information about the model may be revealed in documentation, or the adversary may use carefully constructed examples and analyze the model's responses to categorize it. Knowledge of the model family can help the adversary identify means of attacking the model and help tailor the attack.
Overview
A source-backed snapshot of this AI security technique.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0014
- Maturity
- feasible
- Priority score
- 19
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelfeasible
- Mapped defenses3 ATLAS mitigation records
- Public examples0 linked case study records
- Research risks0 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
AML.M0002 - Passive AI Output Obfuscation
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
Limit the amount of information an attacker can learn about a model's ontology through API queries.
AML.M0006 - Use Ensemble Methods
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.
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.