AST Vulnerability Signatures for Evolving Codebase Correlation
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Solution Overview
Problem
Existing static code analysis methods redundantly identify potential vulnerabilities in updated software applications, creating additional burdens on testers due to the lack of efficient correlation across evolving codebases.
Innovation Solution
A static code analysis engine correlates vulnerabilities across an evolving codebase by using contextual analysis of the abstract syntax tree (AST) to generate unique signatures for vulnerabilities, reducing duplicate vulnerability identification through the use of metadata and hash functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static code analysis is performed on updated software applications, then vulnerabilities can be identified, but duplicate vulnerability identification increases tester burden
Solution Approach 1:
The system performs preliminary action by generating vulnerability signatures from the initial codebase before updates are applied. These pre-generated signatures are stored and used for comparison in subsequent analyses of updated code, allowing the system to quickly identify whether newly detected vulnerabilities are actually new or duplicates of previously identified issues.
Solution Approach 2:
The system creates copies of vulnerability signatures (metadata representations of vulnerability characteristics) from the initial analysis and stores them for future comparison. When analyzing updated code, the system compares newly detected vulnerability signatures against these stored copies to identify duplicates, thereby reducing tester burden without sacrificing identification accuracy.
2Reliability
If comprehensive vulnerability scanning is performed on updated codebases, then all potential vulnerabilities are detected, but the quantity of duplicate vulnerabilities increases
Solution Approach 1:
The system implements feedback by comparing vulnerability signatures detected in updated code against previously stored signatures from the initial codebase. This feedback mechanism allows the system to automatically identify and filter duplicate vulnerabilities, maintaining complete detection of new vulnerabilities while eliminating redundant findings from the output.
Solution Approach 2:
The system changes parameters by using signature-based comparison (metadata characteristics) rather than simple text-based vulnerability matching. By comparing structured signature parameters such as vulnerability type, location, and contextual information, the system can accurately distinguish between duplicate vulnerabilities and genuinely new issues, reducing the quantity of duplicates while maintaining detection completeness.
3Quantity of substance
If vulnerability signatures are generated using contextual analysis, then duplicate vulnerabilities are reduced, but the complexity of the analysis process increases
Solution Approach 1:
The system applies segmentation by breaking down vulnerability analysis into distinct components: generating vulnerability signatures from contextual AST analysis, storing these signatures in a database, and comparing new signatures against stored ones. This segmentation allows the complex task of duplicate detection to be divided into manageable, automated steps that reduce overall process complexity despite the sophisticated signature generation involved.
Solution Approach 2:
The system introduces an intermediary element - the vulnerability signature (metadata representation) - that mediates between the complex contextual analysis of code and the simpler task of duplicate identification. By translating complex code context into standardized signature formats, the system enables automated comparison and duplicate detection without requiring direct complex analysis for every vulnerability check.
Data Source
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AI summary
Methods, apparatus, and systems for characterizing vulnerabilities of an application source code are disclosed. Steps for characterizing vulnerabilities include traversing a representation of the application source code, generating a signature of a potential vulnerability of the application source code, and determining characteristics of the potential vulnerability based on a correlation between the generated signature of the potential vulnerability and previously stored signatures of potential vulnerabilities.