Automated Code Vulnerability Detection and Remediation
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Solution Overview
Problem
Current methods for detecting and remedying code vulnerabilities in enterprise systems are inefficient, requiring extensive manual intervention and resource usage, leading to potential errors and incomplete vulnerability elimination.
Innovation Solution
A comprehensive solution utilizing pattern matching and machine learning to identify and apply fixes for code vulnerabilities, by comparing code fragments to a knowledge base containing known vulnerabilities and solutions, and generating code vectors to automate the remediation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual security scans and vulnerability fixes are performed by developers, then security compliance is achieved, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs automated vulnerability scanning and remediation without requiring manual developer intervention. The code analysis engine automatically detects vulnerabilities, and the remediation engine automatically applies fixes, allowing the system to service itself rather than requiring human operators for each scanning and fixing cycle.
Solution Approach 2:
The patent replaces the mechanical manual process of developers performing security scans and applying fixes with an automated computational system. The code analysis engine and remediation engine use algorithms and machine learning models to perform functions that previously required human developers, substituting mechanical human action with automated electronic processing.
2Reliability
If manual vulnerability fixes are performed iteratively, then security standards are met, but human intervention leads to errors and incomplete elimination of vulnerabilities
Solution Approach 1:
The system implements a feedback loop where the code analysis engine continuously monitors code for vulnerabilities, the remediation engine applies fixes, and the analysis engine re-scans to verify elimination. This closed-loop feedback system ensures vulnerabilities are completely eliminated and prevents errors by automatically verifying each fix rather than relying on manual validation.
Solution Approach 2:
The automated system performs vulnerability detection and remediation without human intervention, eliminating errors that arise from manual processes. The system self-corrects by automatically applying remediation code and verifying its effectiveness, ensuring complete and accurate vulnerability elimination without the mistakes inherent in manual iterative fixing.
3Measurement precision
If comprehensive security scans are applied to codebases, then vulnerability detection improves, but computer resource usage increases
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on vulnerability patterns beforehand. The code analysis engine uses pre-trained models to quickly identify vulnerabilities in new code, avoiding the need for exhaustive scanning each time. This preliminary preparation enables fast, accurate detection without heavy resource consumption during actual scanning operations.
Solution Approach 2:
The patent replaces resource-intensive traditional static analysis methods with machine learning-based detection. The trained models efficiently identify vulnerability patterns with high accuracy using minimal computational resources compared to exhaustive scanning, substituting brute-force mechanical analysis with intelligent pattern recognition.
4Reliability
If developers manually perform security scans and fixes, then human oversight is maintained, but productivity decreases due to extensive manual effort
Solution Approach 1:
The system performs automated vulnerability scanning and remediation without requiring developer time and effort. The code analysis engine and remediation engine handle security tasks autonomously, freeing developers to focus on core development work while maintaining continuous security monitoring and automatic fixing, thereby dramatically improving productivity without sacrificing security oversight.
Data Source
AI summary
A method includes receiving code for computer programming, determining whether at least a portion of the code comprises at least one vulnerability, and comparing at least the portion of the code comprising the at least one vulnerability to a knowledge base. The knowledge base comprises (i) a plurality of code fragments comprising a plurality of vulnerabilities; and (ii) a plurality of solutions to prevent corresponding ones of the plurality of vulnerabilities. The method further includes identifying, based on the comparing, a code fragment of the plurality of code fragments matching at least the portion of the code comprising the at least one vulnerability, and executing a solution of the plurality of solutions corresponding to the identified code fragment to prevent the at least one vulnerability in at least the portion of the code.