Vulnerable Software Component Identification Through Patch Comparison
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Solution Overview
Problem
The existing process for identifying vulnerable software components is manual, time-consuming, and lacks efficiency and accuracy, particularly when dealing with multiple programming languages and versions, necessitating a more automated and reliable method to reduce the time to patch vulnerabilities and improve information quality.
Innovation Solution
A computer-implemented method for identifying vulnerable software components through source-code or bytecode parsing and text analysis, which involves unpacking software packages, retrieving vulnerability database entries, and comparing patch changes to determine vulnerable versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of vulnerable software components is performed, then accuracy can be maintained through expert analysis, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system enables automated self-identification of vulnerable software components by comparing source code against vulnerability patterns and databases, eliminating the need for manual expert analysis while maintaining identification accuracy through algorithmic pattern recognition and automated code parsing
Solution Approach 2:
Manual expert analysis is replaced with automated computational systems including source code parsers, vulnerability pattern matchers, and database comparison algorithms that systematically identify vulnerable components without human intervention, dramatically reducing time while preserving accuracy
2Adaptability or versatility
If comprehensive source code analysis is performed across multiple programming languages, then coverage of vulnerable components improves, but system complexity and processing requirements increase
Solution Approach 1:
The system implements a universal source code analysis platform that handles multiple programming languages (Java, JavaScript, Go, C#, .NET, Rust, PHP) through a single integrated architecture, using language-agnostic vulnerability pattern matching and standardized code representation methods to achieve broad coverage without proportionally increasing complexity
Solution Approach 2:
The system adapts to different programming languages by dynamically adjusting analysis parameters such as syntax patterns, code structure expectations, and vulnerability signature formats, allowing the same core engine to efficiently analyze diverse languages without requiring separate complex analysis systems for each language
3Productivity
If automated identification methods are implemented, then processing speed and efficiency improve, but accuracy may decrease due to false positives from imperfect pattern matching
Solution Approach 1:
The system incorporates feedback mechanisms where identification results are validated against multiple data sources including vulnerability databases, security feeds, and cross-referenced information from multiple programming languages, allowing the system to learn from false positives and continuously improve accuracy while maintaining high processing speed
Solution Approach 2:
The system combines results from multiple independent analysis sources and cross-references vulnerability information across different programming languages and databases, using aggregation and consensus algorithms to filter false positives and confirm true vulnerabilities, thereby maintaining high speed while improving accuracy through combined evidence
Data Source
AI summary
An automated identification of vulnerable software components is presented. The objective to find a method for the automatic identification of CVE affected software components through source-code parsing and text analysis. The method shall reduce manual work to a minimum and ensure a high level of data quality. The objective is solved by the computer-implemented method for identifying vulnerable software, comprising: receiving a software package; unpacking source code files for multiple versions of the software package; retrieving an entry for the software package from a vulnerability database; retrieving a patch for the software package; determining changes made by the patch to source code files of the software package; and determining whether the changes were made to a given version of software and reporting the given version of software as vulnerable in response to a determination the changes were absent from the given version of software.