Binary Component Risk Scoring for Software Supply Chains
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Solution Overview
Problem
The transformation from source code to a binary file representation introduces security risks that are difficult to identify and assess, exacerbated by the increasing complexity of software packages and their interdependencies, with compromised firmware undermining all other security layers.
Innovation Solution
A machine learning-based approach is employed to unpack software packages into components, extract features, and use machine learning models to determine supply chain risks, including ensemble models and embeddings that preserve code semantics, generating reports or dashboards for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to analyze binary components, then security risk detection capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the software package into individual binary components for separate analysis. Each component is processed independently through the machine learning pipeline, allowing parallel computation and reducing the overall computational burden while maintaining detection precision across the entire package.
Solution Approach 2:
The system performs preliminary feature extraction from binary components before applying machine learning models. By pre-processing the binary data into meaningful features (such as code characteristics, metadata, and structural properties), the system reduces the dimensionality of input data and accelerates the subsequent risk assessment process.
2Measurement precision
If comprehensive feature extraction is performed on binary components, then risk assessment accuracy is improved, but processing speed decreases
Solution Approach 1:
The system implements a two-stage feature extraction approach where critical features are extracted first for immediate risk assessment, and additional features are extracted subsequently for deeper analysis. This allows the system to provide timely risk assessments while maintaining the option for comprehensive analysis when needed.
Solution Approach 2:
The system dynamically adjusts the depth and scope of feature extraction based on the specific component being analyzed and the risk context. By changing extraction parameters adaptively, the system optimizes the balance between extraction comprehensiveness and processing efficiency for different scenarios.
3Adaptability or versatility
If multiple machine learning models are deployed to cover different risk categories, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system employs a hierarchical model architecture where a general-purpose machine learning model performs initial risk screening, and specialized models are selectively applied only to components flagged as potentially risky. This multi-functional approach ensures comprehensive coverage across different risk categories while avoiding the overhead of running all models on every component.
Solution Approach 2:
The system introduces an intermediary layer that manages multiple machine learning models, coordinating their execution and integrating their outputs. This intermediary architecture simplifies the overall system complexity by providing a unified interface and workflow management, while still enabling comprehensive risk detection through multiple specialized models.
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.