Embedded Software Analyzer for Binary Vulnerability Detection
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Solution Overview
Problem
System developers face challenges in detecting vulnerabilities in embedded software components, particularly when source code is not accessible, as they rely on binary software, which can lead to security risks and exploitation.
Innovation Solution
An embedded software analyzer (ESA) is developed to detect vulnerabilities using techniques like fuzzing and symbolic execution, predicting vulnerabilities, and prioritizing analysis through machine learning models, generating optimized inputs, and generating smart seeds for improved analysis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If developers use binary software components without source code access, then device deployment and integration are simplified, but security vulnerability detection becomes difficult
Solution Approach 1:
The patent introduces an intermediary system consisting of a instrumentation engine and analysis engine that acts as a mediator between the binary software component and the developer. This intermediary automatically instruments the binary code to insert telemetry code, collects runtime data, and analyzes it for vulnerabilities, thereby enabling security detection without requiring source code access or direct developer intervention in the binary modification process
Solution Approach 2:
The patent performs preliminary actions by automatically instrumenting the binary software component before deployment. The system pre-analyzes the binary code structure, pre-inserts appropriate telemetry instrumentation, and pre-configures the analysis framework, so that when the software runs, vulnerability detection is already in place without requiring later manual intervention
2Reliability
If comprehensive vulnerability analysis is performed on binary software, then security reliability is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing analysis on specific high-risk areas identified through automated static analysis, rather than performing exhaustive analysis of the entire binary. The system instruments only the critical paths and functions that are most likely to contain vulnerabilities, collecting telemetry data selectively from these targeted areas, thereby achieving effective security detection with reduced analysis time
Solution Approach 2:
The patent implements feedback mechanisms where the analysis engine continuously monitors runtime telemetry data and adjusts its analysis focus dynamically. When suspicious patterns are detected, the system intensifies analysis in those specific areas; when areas are confirmed safe, analysis is reduced or stopped, creating an adaptive feedback loop that optimizes the balance between detection thoroughness and analysis time
Data Source
AI summary
An embedded software analyzer (ESA) detects vulnerabilities in software, such as embedded software items (e.g., software that is pre-loaded onto a device or component). Vulnerabilities may include flaws, glitches, weaknesses, and/or other elements that may prevent correct operation or allow an attacker to access or exploit the software (and/or associated devices, components, systems, etc.) The ESA may utilize techniques such as fuzzing and symbolic execution to identify vulnerabilities. The ESA may predict vulnerabilities and prioritize analysis based on the prediction. The ESA may use smart seeding to optimize fuzzing or other analysis. Such smart seeding may include generating optimized inputs for a particular software item. The ESA may apply machine learning to perform, optimize, and/or otherwise implement such analysis techniques.