ML Security Evaluation Framework Lifecycle Threat Modelling
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Solution Overview
Problem
Existing approaches to threat modelling for Machine Learning (ML) systems lack a holistic treatment of assessment methodologies, metrics, and reporting models, failing to account for the adversary's view across all stages of the ML life cycle, thereby exposing ML systems to larger attack surfaces.
Innovation Solution
A computer-implemented method and system for performing security evaluation on a machine learning model, involving determining a taxonomy of the model and its environment at various stages of its lifecycle, generating assumptions based on this taxonomy, and performing adversarial tests to identify failure modes and their effects on subsequent lifecycle stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing threat modelling approaches are used for ML systems, then the assessment process is simplified, but the attack surface of ML systems increases due to lack of holistic treatment
Solution Approach 1:
The patent segments the ML lifecycle into distinct stages (data collection, model training, deployment, monitoring) and applies threat modelling to each stage separately. This allows comprehensive coverage of all attack surfaces while maintaining manageable assessment complexity through structured phase-by-phase analysis.
Solution Approach 2:
The patent introduces a new dimension of assessment by incorporating the adversary's perspective across all ML lifecycle stages, rather than only evaluating from a defender's viewpoint. This multi-dimensional approach comprehensively identifies attack surfaces without proportionally increasing assessment complexity.
2Object-affected harmful factors
If comprehensive threat modelling across all ML lifecycle stages is implemented, then the attack surface is reduced, but the assessment process becomes more complex
Solution Approach 1:
The patent divides the comprehensive threat modelling task into manageable segments corresponding to different ML lifecycle stages. Each stage is assessed independently using standardized procedures, reducing the perceived complexity while maintaining comprehensive coverage of all attack surfaces.
Solution Approach 2:
The patent develops a universal threat modelling framework that can be applied across all ML lifecycle stages using consistent methodologies and metrics. This multi-functional approach reduces assessment complexity by reusing the same core assessment tools and processes across different stages rather than developing stage-specific complex procedures.
3Object-affected harmful factors
If adversary-centric threat models are used, then all stages of ML lifecycle are covered, but the defence process slows down
Solution Approach 1:
The patent performs threat modelling and identifies potential attack vectors during the design and development phases of the ML system, before deployment. This preliminary action allows security measures to be integrated early, reducing the need for extensive post-deployment defence processes and accelerating the overall defence timeline.
Solution Approach 2:
The patent implements continuous monitoring and feedback mechanisms that track the ML system's performance and security posture throughout its lifecycle. This real-time feedback enables rapid detection and response to threats, maintaining comprehensive coverage while speeding up the defence process through automated alerting and response protocols.
Data Source
AI summary
This invention relates to a computer-implemented method and system for performing security evaluation on a machine learning (ML) model. The method includes determining a taxonomy of the ML model and of the environment in which the machine learning model is implemented at one or more stages in the model's lifecycle. The method additionally includes generating, based on the determined taxonomy, a set of assumptions about the ML model and the environment. An adversarial test attack is performed on the ML model at a stage in its lifecycle, based at least in part on the set of assumptions, and one or more failure modes in the ML model are identified based on the result of the first adversarial attack. The system may include a threat modelling component, an assessment component, a reporting component, and a risk mitigation component.