Binary Risk Scoring for Software Supply Chain Remediation
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Solution Overview
Problem
Existing technologies struggle to detect and remediate security risks in software supply chains, particularly at the firmware layer, where vulnerabilities can subvert trust anchors and undermine security controls, due to the complexity of modern software packages and compilation processes.
Innovation Solution
A machine-learning-based approach that analyzes executable machine code to detect, characterize, and remediate risks without requiring human-readable source code, using semantics-preserving analysis to generalize across compilers and architectures, and incorporates risk scoring, component identification, and remediation workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vulnerability scanning tools are used, then they can detect known vulnerabilities, but they fail to detect novel or obfuscated malicious code due to reliance on signature matching
Solution Approach 1:
The patent transforms the detection parameters from high-level source code features to low-level binary characteristics including opcode sequences, control flow patterns, and data flow relationships. This parameter transformation enables the model to detect malicious patterns across different compilation settings and processor architectures while maintaining high detection accuracy for both known and novel vulnerabilities
Solution Approach 2:
The patent segments the binary analysis into distinct functional components including function-level representations, control flow graph analysis, and data flow relationship extraction. This segmentation allows the system to process complex binaries systematically while preserving semantic relationships, enabling accurate detection that generalizes across different software components and vulnerability types
2Loss of information
If source code analysis is performed, then semantic understanding is achieved, but access to human-readable source code is not always available and compilation can introduce new weaknesses
Solution Approach 1:
Instead of analyzing source code and translating to binary, the patent inverts the approach by directly analyzing compiled binaries and reconstructing semantic representations. This inversion eliminates dependency on source code availability while preserving semantic meaning through learned representations of control flow, data flow, and program behavior patterns that generalize across compilation variations
3Reliability
If firmware security controls are implemented, then trust anchors are established, but compromised firmware can subvert these controls undermining subsequent security investments
Solution Approach 1:
The patent implements preliminary security verification by analyzing firmware binaries before deployment to identify malicious patterns and vulnerabilities. The trained detection models scan firmware images, extract binary features, and flag suspicious patterns that indicate compromised trust anchors, enabling preventive action before the firmware is installed and could subvert security controls
Solution Approach 2:
The patent introduces an intermediary analysis layer between firmware compilation and deployment that uses machine learning models to detect malicious patterns. This intermediary system acts as a security gatekeeper, analyzing binary characteristics without requiring source code access, and can identify compromised firmware that would otherwise subvert trust anchors and undermine subsequent security investments
4Quantity of substance
If complex software packages with nested interdependencies are analyzed, then comprehensive coverage is achieved, but the attack surface expands and difficulty of identifying vulnerabilities increases
Solution Approach 1:
The patent develops a universal binary analysis framework that processes diverse software components including firmware images, system libraries, and application binaries through a single unified model. The system extracts standardized binary features and control flow patterns that are architecture-agnostic, enabling comprehensive analysis of complex software packages with nested dependencies without proportionally increasing system complexity
Data Source
AI summary
A software package is received and unpacked into multiple components comprising plural functions. Each function is lifted from machine code into static single-assignment intermediate representation and tokenized to produce semantics-preserving embeddings. Intermediate-representation data-flow features are extracted, including detection of constant static variables on a stack, stack reaching definitions, uninitialized variables, and intra-procedural aliases. For each component, the embeddings and features are input to a machine-learning model trained on semantic properties derived from a corpus of software packages to generate a software supply chain risk level. Data characterizing the risk level is provided to a consuming application. When the risk level satisfies a remediation criterion, a remediation action is initiated, including generation of a source-code patch recommendation for an identified root-cause function, insertion of a runtime guard into the component, or issuance of a security advisory for distribution to a security operations dashboard.