Automated Vulnerability Detection Using ML Root Cause Analysis
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Solution Overview
Problem
Existing software systems face challenges in reliably detecting relevant vulnerabilities, often resulting in a high number of false positives that are difficult to analyze and identify.
Innovation Solution
A computer-implemented method for automated detection of known vulnerabilities in software, which involves extracting a data structure from the software code, identifying dependent software components, evaluating vulnerabilities using a machine learning model to determine root causes, and outputting potentially vulnerable components for further action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated vulnerability detection is performed on software components, then the detection speed and coverage are improved, but the number of false positives increases significantly
Solution Approach 1:
The vulnerability detection process is segmented into multiple stages: initial automated scanning to identify potential vulnerabilities, followed by a secondary verification stage using a machine learning model that analyzes code context, data structures, and vulnerability descriptions to distinguish true positives from false positives. This segmentation allows high-speed initial detection while maintaining accuracy through staged verification.
Solution Approach 2:
A machine learning model serves as an intermediary between the automated vulnerability scanner and the final vulnerability assessment. The model takes vulnerability matches and contextual code information as input, processes them through learned patterns, and outputs a verification result that confirms or refutes the vulnerability. This intermediary layer filters out false positives while preserving true vulnerabilities.
2Adaptability or versatility
If the software complexity increases with more components, then the functionality and adaptability are improved, but the difficulty of tracking and detecting vulnerabilities increases
Solution Approach 1:
The machine learning model is designed with multi-functionality to handle diverse vulnerability types across different software components. It processes various vulnerability descriptions, analyzes different code structures, and evaluates multiple contextual factors uniformly. This universal approach allows the system to maintain effectiveness across complex, heterogeneous software systems without requiring component-specific detection logic.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously learns from verification results. True positives and false positives from initial scans are fed back into the training data, allowing the model to improve its accuracy over time. This feedback loop enables the system to adapt to new vulnerability patterns and maintain high detection accuracy even as software complexity increases.
3Measurement precision
If manual analysis is performed to reduce false positives, then the detection accuracy is improved, but the time consumption and resource requirements increase
Solution Approach 1:
Instead of performing exhaustive manual analysis on all detected vulnerabilities, the system applies partial automated verification using the machine learning model only to cases that require additional validation. The model quickly evaluates contextual information and provides verification results for the majority of cases, reserving detailed manual analysis only for ambiguous or high-priority findings. This partial application of verification significantly reduces time consumption while maintaining high accuracy.
Solution Approach 2:
The machine learning model performs self-service verification by automatically analyzing code context, data structures, and vulnerability descriptions without requiring manual intervention. The model independently evaluates whether a detected vulnerability is a true positive or false positive based on learned patterns, eliminating the need for time-consuming manual review of each finding while maintaining detection accuracy.
Data Source
AI summary
A computer-implemented method for automated detection of known vulnerabilities in a static test of software. The method includes extracting a data structure of a code of the software; identifying software component(s) on which the software depends based on the code, the extracted data structure and/or the software bill of materials of the software; evaluating, for identified software components, whether the software component is associated with a known vulnerability, potentially vulnerable software component{s) resulting; applying, for potentially vulnerable software component(s), a machine learning model to a description associated with the known vulnerability, wherein the machine learning model is trained and configured to determine at least one root cause from at least the description and a prompt; and evaluating the at least one potentially vulnerable software component as vulnerable or as not vulnerable or, optionally, as unevaluable based on the at least one root cause and the extracted data structure.