Binary Code Similarity Analysis for Software Supply Chain Risk
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Solution Overview
Problem
The transformation from source code to binary files introduces security risks that are difficult to identify, exacerbated by software complexity and interdependencies, with compromised firmware undermining all other security layers.
Innovation Solution
A machine learning-based approach that unpacks software packages into components, extracts features, and uses ensemble models to score supply chain risks, including open-source software control, vulnerability, license, development, and support categories, generating reports or dashboards for identified risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to analyze binary code, then security risk detection capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments binary code analysis into distinct processing stages: unpacking software packages into components, extracting features from components, generating embeddings, and analyzing with specialized machine learning models. This segmentation allows complex security analysis to be broken into manageable steps that can be optimized independently.
Solution Approach 2:
The patent introduces intermediate representations (embeddings) as mediators between binary code and machine learning models. These embeddings transform raw binary features into a standardized format that preserves semantic meaning while reducing computational complexity for the subsequent analysis stages.
2Measurement precision
If comprehensive feature extraction is performed on binary components, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary feature extraction and embedding generation on binary components before the actual security analysis. By pre-processing and organizing features into embeddings in advance, the system reduces the computational burden during the critical detection phase, thereby improving processing speed without sacrificing detection accuracy.
3Reliability
If multiple machine learning models are used to score different risk categories, then comprehensive risk assessment is improved, but system complexity increases
Solution Approach 1:
The patent employs a unified machine learning framework that handles multiple risk categories (open-source software control, vulnerability, license, development, and support) through a common architecture. This universal approach allows the system to assess comprehensive risks while avoiding the complexity of entirely separate analysis systems for each risk type.
4Ease of operation
If binary code is analyzed without source code access, then security vulnerabilities are detected, but analysis precision is reduced
Solution Approach 1:
The system creates detailed embeddings as copies or representations of the binary code's semantic structure. These embeddings capture essential features and relationships from the binary code, enabling comprehensive analysis without requiring access to the original source code, thus maintaining operational flexibility while preserving analysis precision.
Data Source
AI summary
A software package is received and unpacked into a plurality of components. Features are extracted from each component which are indicative (i.e., useful, etc.) in determining whether such component presents a software supply chain risk. The extracted features are consumed by one or more machine learning models to determine a level of supply chain risk associated with the component. This determined level of supply chain risk can be provided to a consuming application or process. Component identities can also be identified using machine learning or other similarity analyses. In some cases, embeddings are used to characterize risk and/or provide component identities. Related apparatus, systems, techniques and articles are also described.