AI Patch Complexity Classification for Vulnerability Prioritization

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Solution Overview

Problem

Current vulnerability patch complexity assessment is time-consuming, resource-intensive, and prone to inaccuracies due to inconsistent and fragmented information from vendor advisories and vulnerability databases, leading to inefficient prioritization and potential delays in addressing cybersecurity threats.

Innovation Solution

An automated system using artificial intelligence and machine learning models to classify vulnerability patch complexity by analyzing quantitative and qualitative data from sources like MSRC and NVD, identifying complexity indicators such as keywords, product types, and operational steps, and categorizing patches into LOW, MEDIUM, and HIGH complexity levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual assessment of vulnerability patch complexity is performed, then accuracy of complexity evaluation is improved, but time consumption and resource usage increase

Engineering Contradiction:

Improveaccuracy of complexity evaluationVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical assessment with an automated AI-based system that uses machine learning models to evaluate patch complexity. The system processes vulnerability data automatically, extracting features and generating complexity classifications without human intervention, thereby maintaining accuracy while significantly reducing time consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically assessing patch complexity using integrated AI models that process vulnerability data independently. The machine learning models autonomously analyze patch information, generate complexity scores, and provide classifications without requiring manual expert review, allowing the system to serve itself in the assessment process.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive analysis of vulnerability data is performed, then accuracy of patch classification is improved, but processing resources and time increase

Engineering Contradiction:

Improveaccuracy of patch classificationVSAvoidprocessing resources

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts only the most relevant features from comprehensive vulnerability data using AI-based feature extraction techniques. The system identifies and processes only the critical attributes needed for complexity assessment, such as patch size, dependency changes, and vulnerability severity, filtering out redundant information to maintain accuracy while reducing processing resource requirements.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the parameters of data processing by transforming raw vulnerability data into normalized feature representations that are optimized for machine learning models. This parameter transformation allows comprehensive analysis to be performed more efficiently, converting detailed vulnerability information into concise features that maintain classification accuracy with reduced processing resources.

Inventive Principle:

Principle #35Parameter changes

3Quantity of substance

If inconsistent and fragmented vulnerability information is processed, then completeness of vulnerability data is improved, but reliability of complexity assessment decreases

Engineering Contradiction:

Improvecompleteness of vulnerability dataVSAvoidreliability of complexity assessment

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The patent merges multiple fragmented vulnerability data sources into a unified, consistent representation. The AI system integrates information from various vendors and databases, normalizing the data formats and resolving inconsistencies through machine learning models that learn from multiple perspectives, thereby producing reliable complexity assessments from previously fragmented information.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system incorporates feedback mechanisms where machine learning models continuously learn from vulnerability assessment outcomes and adjust their processing of fragmented data. This feedback loop allows the system to refine its ability to handle inconsistent information, improving reliability over time as the models adapt to patterns in vulnerability data from multiple sources.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260003596A1Patch complexity classification

Publication Date: 2026.01.01 CROWDSTRIKE

AI summary

The present disclosure provides an approach of collecting vulnerability data corresponding to a vulnerability of a target product. The approach provides the vulnerability data to an artificial intelligence model that is trained to determine a complexity indicator from the vulnerability data. The complexity indicator corresponds to applying a vulnerability patch to remediate the vulnerability. The approach determines a patch complexity classification by providing the complexity indicator to the artificial intelligence model and, in turn, provides the patch complexity classification to a target system corresponding to the target product.