Binary Image Post-Compilation Manipulation Detection
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Solution Overview
Problem
Existing methods fail to effectively detect post-compilation manipulation of binary software components, which can introduce security risks and vulnerabilities, especially in complex systems like automotive environments where access to source code is limited.
Innovation Solution
A method and system for binary static analysis that assesses binary images for junk instruction sets, such as NOP instructions, XOR operations, and memory manipulation, flagging manipulated code and using a library of junk instructions to improve detection over time, allowing for risk assessment and potential blocking of compromised software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary static analysis is performed to detect post-compilation manipulation, then security reliability is improved, but detection precision is insufficient without access to source code
Solution Approach 1:
The patent applies the principle of color changes by using visual indicators (flags) to mark manipulated binary code. The system identifies suspicious patterns in binary images and applies visual markers to indicate potential manipulation, making security issues visible without requiring source code access. This allows security personnel to quickly identify and prioritize suspicious binaries for further analysis.
2Difficulty of detecting and measuring
If comprehensive binary analysis methods are used to detect manipulation, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by breaking down the binary analysis process into distinct modules: pattern recognition engines that identify suspicious code patterns, verification modules that validate findings, and library comparison systems that cross-reference against known manipulation signatures. This modular approach enables comprehensive detection capability while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary library of known manipulation patterns and signatures that mediates between the raw binary analysis and the detection decision. This library acts as a reference database that translates complex binary patterns into interpretable security assessments, reducing the complexity of the overall detection system while maintaining high detection capability.
3Object-affected harmful factors
If post-compilation manipulation is detected and blocked, then security risk is reduced, but productivity decreases due to additional verification steps
Solution Approach 1:
The patent applies partial action by implementing risk-based verification where not all binaries undergo the same level of inspection. Low-risk binaries with clean signatures receive minimal verification, while high-risk binaries trigger more comprehensive analysis. This graduated approach reduces security risks through targeted verification while minimizing productivity impact on legitimate software.
Solution Approach 2:
The system implements feedback loops where detection results and blocked binaries are fed back into the analysis engine to refine future detections. This continuous learning mechanism improves the accuracy of risk assessment over time, allowing the system to become more selective and reduce false positives, thereby maintaining security while improving productivity through better-risk differentiation.
Data Source
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AI summary
A method at a computing device for identification post compilation manipulation of a binary image, the method including assessing the binary image for at least one junk instruction set; determining that the at least one junk instruction set is found within the binary image; and flagging the binary image as being manipulated based on the determining.