AI Mapping Analysis for Compromised Software Tool Detection
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Solution Overview
Problem
Existing software security measures fail to detect malicious code inserted into software via tools during the compilation or execution process, as they primarily focus on identifying malware in the source code, neglecting potential vulnerabilities in the software tools themselves.
Innovation Solution
Implementing an AI algorithm to generate and compare mappings between input code and corresponding software output, identifying abnormal manipulations by software tools, and flagging compromised tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software security measures focus only on identifying malware in source code, then the detection process remains simple and fast, but malicious code inserted via software tools during compilation or execution cannot be detected
Solution Approach 1:
The detection system is segmented into multiple specialized AI algorithms, each responsible for analyzing different aspects of software tool behavior. The system divides the monitoring task into: generating expected output mappings, comparing actual vs expected mappings, and identifying anomalies. This segmentation enables comprehensive security coverage while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary AI-based mapping system that acts as a mediator between software tools and the detection process. Instead of directly analyzing complex software tool operations, the system uses AI-generated mappings as an intermediate representation to compare expected versus actual behavior. This intermediary layer simplifies the detection mechanism while enhancing security monitoring capabilities.
2Reliability
If AI algorithms are used to generate and compare mappings between input code and software output, then malicious code insertion can be detected in real-time, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-generating expected output mappings using AI algorithms before actual software tool execution. These expected mappings are created in advance and stored for comparison purposes. When software tools execute, the system compares actual outputs against pre-computed expectations, enabling rapid real-time detection without requiring complex analysis during the critical execution phase.
Solution Approach 2:
The patent uses copying by creating AI-generated copies or representations of expected software tool outputs. Instead of analyzing the actual complex execution process in real-time, the system creates simplified copy representations (mappings) of expected behavior and compares them against actual outputs. This copying approach reduces computational overhead while maintaining detection accuracy.
3Reliability
If comprehensive monitoring of software tool manipulation is implemented, then all malicious code insertions can be detected, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The patent replaces traditional mechanical or rule-based monitoring systems with AI-based algorithms. Instead of using complex predefined rules and manual analysis mechanisms to detect malicious code, the system employs machine learning models that automatically learn and identify patterns of malicious manipulation. This substitution reduces system complexity by replacing cumbersome mechanical detection processes with adaptive intelligent systems.
Solution Approach 2:
The monitoring system implements self-service by using AI algorithms that automatically adapt and improve their detection capabilities without requiring manual configuration or intervention. The system self-trains on observed software tool behaviors, automatically updates its understanding of normal versus malicious patterns, and continuously refines its detection accuracy. This self-service approach reduces operational complexity while maintaining comprehensive monitoring.
Data Source
AI summary
A current software tool is identified. The current software tool is used to manage and/or create a current corresponding software. For example, the current software tool may be a compiler and the current corresponding software may be a binary executable. A current mapping is generated between code provided to the current software tool and the current corresponding software using a first Artificial Intelligence (AI) algorithm. A comparison between the current mapping and a learned mapping is made to determine if the current software tool is manipulating the current corresponding software in an abnormal way. The learned mapping is based on historical code input into historical software tools and corresponding historical code output from the historical software tools. In response to determining that the current software tool is manipulating the current corresponding software in an abnormal way, the current software tool is identified as being compromised or likely compromised.