AI-Generated Source Code Input Refinement for Vulnerability Control
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Solution Overview
Problem
AI-generated source code may contain new vulnerabilities or mutations that are not detected by traditional scanning methods, even after initial removal of issues, due to variations in input parameters.
Innovation Solution
A system that captures multiple sets of input parameters, scans the generated source code for issues, and uses AI algorithms to identify problematic parameters, modifying them to generate code free of vulnerabilities or mutations by learning from identified issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI algorithms generate source code based on input parameters, then source code is produced efficiently, but the generated code may contain new vulnerabilities or mutations that traditional scanning methods cannot detect
Solution Approach 1:
The patent implements a feedback mechanism where the AI algorithm analyzes detected vulnerabilities and mutations in generated code, then uses this information to refine and adjust its future code generation. The system feeds back the identified issues and their contexts to improve the AI model, creating a closed-loop system that continuously learns from its mistakes and reduces vulnerability recurrence.
Solution Approach 2:
The patent applies preliminary action by proactively identifying and analyzing vulnerabilities and mutations in AI-generated code before deployment. The system performs advance scanning and analysis to detect potential issues, allowing corrective actions to be taken before the code is put into production, thereby preventing vulnerabilities from causing harm.
2Reliability
If traditional scanning methods are used to detect issues in training source code, then known issues can be removed, but new mutations of issues may still appear in AI generated code
Solution Approach 1:
The patent employs parameter changes by analyzing various characteristics of vulnerabilities and mutations, such as code patterns, structural features, and contextual attributes. The AI algorithm learns to recognize these parameters and their relationships, enabling it to identify both known issues and new mutations by comparing against learned patterns rather than relying solely on traditional static scanning rules.
Solution Approach 2:
The patent substitutes traditional mechanical scanning methods with an AI-based system that uses machine learning to detect vulnerabilities. Instead of relying on fixed rule-based scanners, the system employs trained AI models that can recognize complex patterns and mutations in code, providing more adaptive and effective vulnerability detection that evolves with new threat patterns.
3Measurement precision
If multiple sets of input parameters are tested to identify problematic patterns, then vulnerability detection accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent applies universality by creating a multi-functional AI system that performs multiple tasks: generating code, scanning for vulnerabilities, analyzing mutations, and learning from results. This unified system handles diverse functions through a single intelligent platform, reducing the need for separate specialized tools and simplifying the overall system architecture while maintaining high detection accuracy.
Data Source
AI summary
A plurality of sets input of parameters are captured. The captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm that generates a plurality of corresponding AI generated source code. Each set of the captured plurality of sets of input parameters comprises one or more input parameters. The plurality of corresponding AI generated source code are scanned to identify an issue. For example, the issue may be a type of malware or a software vulnerability. A second AI algorithm identifies a first input parameter from the plurality of sets of input parameters that is associated with the identified issue. The second AI algorithm modifies, based on the first input parameter, a new first input parameter provided to the first AI algorithm. The first new input parameter is used to generate a new corresponding AI generated source code.