AI Submodule Training for Model Capture Attack Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Adversaries can capture AI models by sending iterative queries, creating a secondary dataset from input-output pairs, and training a parallel model, posing risks to the original developer through business disadvantages and intellectual property loss.
Innovation Solution
Implement a submodule within the AI system that preprocesses input data into subsets, uses multiple models to identify attack vectors, and locks out users or modifies outputs if information gain exceeds a threshold, while notifying the owner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AI system uses a single model for processing input data, then the system complexity is low and ease of operation is maintained, but the system becomes vulnerable to model capture attacks by adversaries
Solution Approach 1:
The AI system is segmented into multiple independent models (first model and second model) that process input data differently. This segmentation allows the system to detect inconsistencies between model outputs as indicators of attack vectors, thereby improving reliability against model capture while maintaining manageable complexity through modular architecture
Solution Approach 2:
A submodule is introduced as an intermediary component that receives outputs from multiple models and determines whether they represent attack vectors. This intermediary layer protects the core AI models by analyzing their outputs and identifying patterns indicative of adversarial queries, resolving the contradiction between protection and complexity
2Measurement precision
If the system processes all input data through multiple models to detect attack vectors, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by using multiple models selectively rather than processing all data uniformly through all models. The submodule determines when attack vectors are present by comparing outputs, applying the full detection mechanism only when necessary, thus improving detection precision while controlling processing time through conditional execution
Solution Approach 2:
The system changes parameters by adjusting the threshold for determining attack vectors and modifying how input data is processed based on detected patterns. By dynamically adjusting detection sensitivity and processing depth, the system maintains high detection accuracy while optimizing processing time based on the actual threat level
3Loss of information
If the system locks out users or modifies outputs to prevent model capture, then intellectual property protection is improved, but user convenience and system usability deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring model outputs and user queries through the submodule. When potential attack vectors are detected, the system provides feedback by locking out users or modifying outputs. This feedback mechanism protects intellectual property while maintaining user convenience through automated, context-aware responses rather than blanket restrictions
Data Source
AI summary
A method of training a submodule and preventing capture of an AI module is disclosed. Input data is received from at least one user through an input interface. It is transmitted through a blocker module to an AI module, which computes a first output data by executing a first model based on the input data. Input data is pre-processed by a submodule to obtain at least one subset of the input data. This submodule is trained using methods steps. The input data and the at least one subset of the input data are processed by the submodule to identify an attack vector from the input data. The identification information of the attack vector is sent to the information gain module.