Automated ML Model Vulnerability Assessment for Adversarial Testing

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Solution Overview

Problem

The increasing availability of publicly available machine learning models on platforms like Hugging Face and GitHub makes them vulnerable to adversarial attacks, necessitating a scalable and automated vulnerability assessment system due to the impracticality of manual testing.

Innovation Solution

The ML-RAY framework provides a multi-layered architecture for automated vulnerability assessment, including a hardware, framework and libraries, data, model management, security assessment, and user interface layer, supporting various frameworks and libraries, and performing adversarial attacks to evaluate model robustness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual vulnerability assessment is performed on each ML model, then assessment thoroughness is improved, but time consumption and labor cost increase significantly

Engineering Contradiction:

Improvevulnerability assessment thoroughnessVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automated self-assessment of ML models through autonomous execution of adversarial attacks and vulnerability detection algorithms, eliminating the need for manual intervention while maintaining comprehensive assessment coverage

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual vulnerability assessment processes are replaced with automated computational systems that execute security tests, analyze model responses, and generate vulnerability reports through algorithmic operations rather than human manual testing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive adversarial attack testing is performed on all public ML models, then security coverage is improved, but computational resources and testing time increase

Engineering Contradiction:

Improvesecurity coverageVSAvoidtesting efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The vulnerability assessment process is divided into distinct modular layers including data preprocessing, model loading, adversarial attack execution, vulnerability analysis, and report generation, allowing parallel processing and efficient resource utilization across multiple models simultaneously

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements a universal automated assessment framework that can evaluate multiple different ML model architectures and types using the same adversarial attack methodologies and analysis procedures, enabling scalable security testing across diverse model portfolios

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12518025B1Systems and methods for automatic vulnerability assessment of machine learning models

Publication Date: 2026.01.06 FLORIDA INTERNATIONAL UNIVERSITY

AI summary

Systems, methods, and frameworks for automatic vulnerability assessment of machine learning models are provided. The fully automated model-agnostic framework can systemically perform comprehensive adversarial testing across a wide range of public machine learning models. The modular and multi-layered architecture enhances scalability for large-scale vulnerability assessment allowing for more time and resource-efficient approach than existing technologies and tools.