Embedded Device Firmware Injection for Automated Vulnerability Mitigation
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Solution Overview
Problem
Legacy embedded devices are vulnerable to attacks due to lack of defensive technologies, proprietary nature, and the absence of automated patching mechanisms, making them susceptible to exploitation and unauthorized modifications.
Innovation Solution
A method for injecting security software components into firmware binaries to identify and mitigate vulnerabilities through live forensic information and static analysis, enabling dynamic and offline defenses to create secure firmware binaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security software components are injected into firmware binaries to detect vulnerabilities, then firmware security is improved, but device complexity increases
Solution Approach 1:
Security software components are injected into and nested within the firmware binary structure. The security components operate within the existing firmware framework, allowing vulnerability detection without requiring a complete firmware redesign. This nesting approach adds security functionality while minimizing structural disruption.
Solution Approach 2:
The firmware is segmented into functional regions, with security software components injected into specific segments. This allows the security detection mechanism to operate on particular firmware portions without affecting the entire firmware structure, thereby managing complexity through localized modifications.
2Measurement precision
If live forensic information and static analysis are aggregated to identify vulnerabilities, then vulnerability detection accuracy is improved, but information processing time increases
Solution Approach 1:
Static analysis of firmware binaries is performed in advance before deployment, identifying potential vulnerability patterns and creating reference profiles. This preliminary analysis stores pre-processed security information that can be quickly matched against live forensic data during runtime, reducing real-time analysis requirements.
Solution Approach 2:
The system aggregates feedback from both live forensic information (runtime behavior) and static analysis (pre-deployment characteristics). This multi-source feedback mechanism cross-validates vulnerability detections, improving accuracy while distributing the analytical workload between offline and online phases.
3Object-affected harmful factors
If security modifications are applied to create secure firmware binaries, then attack vector reduction is improved, but manufacturing complexity increases
Solution Approach 1:
An automated firmware modification system acts as an intermediary between the original firmware binary and the final secure firmware. This intermediary automatically injects security components and applies modifications based on detected vulnerabilities, reducing manual intervention requirements and standardizing the security hardening process.
Solution Approach 2:
The firmware modification process is designed to be self-service, where the security system automatically identifies vulnerabilities, selects appropriate mitigations, and applies modifications without requiring extensive manual security expert intervention. This automation reduces manufacturing complexity despite the added security modifications.
Data Source
AI summary
Systems and methods for securing embedded devices via both online and offline defensive strategies. One or more security software components may be injected into firmware binary to create a modified firmware binary, which is functionally- and size-equivalent to the original firmware binary. The security software components may retrieve live forensic information related to embedded devices for use in live hardening of the modified firmware binary while the embedded device is online, dynamically patching the firmware. In addition, the live forensic information may be aggregated with other analytical data identifying firmware vulnerabilities. A vulnerability identification and mitigation system can then identify and inject modifications to the original firmware binary to develop secure firmware binary, which may be imaged and loaded onto one or more embedded devices within a network.


