LLM Source Code Auditing for Security Vulnerability Remediation
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Solution Overview
Problem
Existing security vulnerability identification and remediation processes in software are laborious and costly due to the manual auditing and fixing of false positives, which are time-consuming and require skilled personnel, even when automated testing is used.
Innovation Solution
Utilizing large language models (LLMs) to analyze security vulnerabilities identified by testing, providing automated auditing and generating recommended fixes, thereby merging the processes of vulnerability identification and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual auditing and fixing of security vulnerabilities is performed, then security accuracy is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent introduces an LLM-based intermediary system that acts as a mediator between automated vulnerability detection and human security experts. The LLM analyzes false positive vulnerabilities, provides audit opinions, and generates remediation recommendations, thereby reducing the time and labor required for manual auditing while maintaining high security accuracy through intelligent automation.
2Reliability
If skilled personnel are used for vulnerability remediation, then security quality is improved, but labor cost increases
Solution Approach 1:
The patent enables the system to serve itself by implementing automated vulnerability remediation through LLM-generated code fixes. The system automatically analyzes vulnerabilities, generates remediation code, applies fixes to the source code, and verifies the corrections without requiring continuous human intervention, thereby reducing labor costs while maintaining security quality through intelligent self-service capabilities.
3Productivity
If automated security testing is used, then productivity is improved, but false positive rate increases requiring more manual auditing
Solution Approach 1:
The patent implements a feedback mechanism where the LLM analyzes false positive vulnerabilities detected by automated security testing, provides audit opinions on their validity, and generates remediation recommendations. This feedback loop allows the system to process high volumes of vulnerability detections efficiently while filtering out false positives through intelligent analysis, thereby maintaining high productivity without being overwhelmed by false positives.
Data Source
AI summary
Information is received that pertains to a security vulnerability of a program identified by security testing. The information includes the security vulnerability and the source code responsible for the security vulnerability. Based on the information pertaining to the security vulnerability, a prompt is generated to input to a large language model (LLM). The prompt is generated to solicit a response from the LLM including whether the security vulnerability is an actual security vulnerability; a justification as to why the LLM has indicated that the security vulnerability is an actual security vulnerability or not; and in a case in which the security vulnerability is an actual security vulnerability, a recommended fix to resolve the security vulnerability.