We know your AI is intelligent

But is it secure?

Testing AI/ML systems requires domain knowledge. At Payatu, our AI/ML domain experts have orchestrated ways to help you secure your intelligent application against esoteric and potentially severe security and privacy threats.

ML Security assessment coverage

Understanding the Application
- Use-case
- Product Capabilities
- Implementations
Attack Surface Identification
- Understanding the ML Pipeline
- Gather Test Cases If Any
Threat Modeling
- Actors and Entity Boundaries
- Possible Attacks identification on Exposed endpoints
- Possible attack vectors
Model Endpoints
- Understand ways with which end users communicate with model
- Simulate end user interaction
Adversarial Learning Attack
- Craft inputs to bypass fool classifiers
- Use custom built tools
- Automated generation of theoretically infinite zero day samples as possible
Model Stealing Attack
- Model deployed locally or remotely
- Reverse engineer deployed application
- Custom built scripts for black-box model stealing attacks
Model Skewing and Data poisoning Attack
- Simulate Feedback loops abused by attackers
- Quantify the skewness of model
Model Inversion and inference
- Get access to model via valid or compromised communication channels
- Infer sensitive samples from training dataset from model
Framework/ Network/Application assessment
- Identify traditional vulnerabilities in application
- Leverage them for above attacks
Reporting and Mitigation
- Comprehensive Mitigation Proposal
- Work With Developer/SME for implementations