Docker Image Manifest Analysis for Instruction Vulnerability Checks
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Solution Overview
Problem
Existing vulnerability analysis tools for Docker images primarily focus on extracting installed packages and dependencies without running the container, lacking the ability to inspect vulnerabilities in instructions used for creating the image.
Innovation Solution
A Docker image vulnerability inspection device and method that extracts and analyzes instructions from a Docker image's manifest file, classifying them into different types and checking vulnerabilities using a CVE database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vulnerability analysis tools extract installed packages and dependencies without running the docker container, then the inspection process is simple and fast, but the ability to detect vulnerabilities in creation instructions is lost
Solution Approach 1:
The inspection process is segmented into distinct stages: extracting creation instructions from manifest files, classifying instructions by type (RUN, COPY, ADD, etc.), mapping files to classes, setting vulnerability extraction conditions, and checking against CVE database. This segmentation allows comprehensive vulnerability detection while maintaining process clarity and manageability.
Solution Approach 2:
The system performs preliminary extraction and classification of creation instructions from manifest files before vulnerability checking. By pre-processing the instruction set and organizing files into classes with specific vulnerability conditions, the system prepares data structures that enable efficient and comprehensive vulnerability detection without requiring container execution.
2Reliability
If the system extracts and analyzes creation instructions from manifest files, then comprehensive vulnerability inspection is enabled, but the analysis time and processing complexity increase
Solution Approach 1:
The system applies different vulnerability extraction conditions to different file classes based on their specific characteristics. For example, source code files use one set of conditions while configuration files use another. This localized approach ensures comprehensive vulnerability detection for each file type while avoiding unnecessary analysis, thereby reducing overall processing time.
Solution Approach 2:
The system changes the parameters of vulnerability extraction by dynamically selecting different extraction conditions based on the file class and instruction type. This allows the system to adapt its analysis depth and methodology to match the specific requirements of each file type, improving efficiency while maintaining comprehensiveness.
3Measurement precision
If files are classified into multiple classes with different vulnerability extraction conditions, then detection precision is improved, but the device complexity increases
Solution Approach 1:
The system introduces a classification dimension by organizing files into multiple classes (e.g., source code, configuration, data files) with distinct vulnerability extraction conditions. This dimensional organization allows precise vulnerability detection for each file type while maintaining a structured and manageable classification framework that does not excessively increase system complexity.
Data Source
AI summary
Provided is a docker image vulnerability inspection device, which extracts and classifies an instruction by analyzing a manifest file of a docker image, maps a file designated in the instruction to a plurality of classes, sets vulnerability of the file according to an extraction condition preset to each of the plurality of classes, and checks vulnerability of the file according to the vulnerability set to the file based on a CVE database prepared in advance.