Exfiltration via AI Inference API
Description
Adversaries may exfiltrate private information via AI Model Inference API Access.
AI Models have been shown leak private information about their training data (e.g. Infer Training Data Membership, Invert AI Model).
The model itself may also be extracted (Extract AI Model) for the purposes of AI Intellectual Property Theft.
Exfiltration of information relating to private training data raises privacy concerns.
Private training data may include personally identifiable information, or other protected data.
- ATLAS ID
- AML.T0024
- Maturity
- RealizedUsed by real adversaries against AI systems in the wild.
- Platforms
- Predictive AIGenerative AIAgentic AI
Sub-techniques
Mitigations
- AML.M0004Limit AI Service Query Volume and Rate
Limit the volume of API queries in a given period of time to regulate the amount and fidelity of potentially sensitive information an attacker can learn.
- AML.M0019Control Access to AI Models and Data in Production
Adversaries can use unrestricted API access to build a proxy training dataset and reveal private information.
- AML.M0024AI Telemetry Logging
Telemetry logging can help identify if sensitive data has been exfiltrated.
- AML.M0035AI Red Team
Exercise inference interfaces for membership inference, model inversion, and functional extraction. Use findings to improve privacy controls, authentication, output restriction, rate limits, and monitoring.
Case studies that use it
No ATLAS case study records this technique yet. Browse all case studies.