LLM Vulnerability Detection and Mitigation in Obfuscated Log Files
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Communications networks face inefficiencies in reviewing log files and codebases to identify and mitigate vulnerabilities, requiring a more efficient method to detect and address security and vulnerability issues within large datasets.
Innovation Solution
A vulnerability detection and mitigation system utilizing a large language model (LLM) to process log files, identify vulnerable subsets, generate regular expressions, and compare vector embeddings to efficiently detect and mitigate vulnerabilities across multiple log files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to review log files for vulnerabilities, then comprehensive security coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments log files into fixed-size chunks with overlap regions, processing only relevant portions rather than entire files. This segmentation allows the system to divide large log files into manageable pieces, reducing processing time while maintaining security coverage through the overlap strategy that ensures vulnerable information spanning chunk boundaries is not missed.
Solution Approach 2:
The system extracts and processes only the subset of log file chunks that contain vulnerable information, rather than reviewing entire log files. The LLM identifies and extracts relevant chunks containing potential vulnerabilities, eliminating the need to process irrelevant portions of log files, thus significantly reducing processing time while maintaining comprehensive security detection.
2Measurement precision
If large language models process entire log files, then accurate vulnerability detection is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent divides log files into fixed-size chunks with overlap, allowing the LLM to process smaller, manageable segments rather than entire large log files. This segmentation reduces computational resource requirements while maintaining detection accuracy through the overlap mechanism that ensures vulnerable information is captured even when spanning chunk boundaries.
Solution Approach 2:
The system processes only a partial subset of log file content - specifically, only those chunks identified as containing vulnerable information - rather than processing the entire log file. This partial action approach reduces computational resource consumption while maintaining accurate vulnerability detection by focusing LLM processing power on relevant segments only.
3Reliability
If comprehensive log file review is performed, then all vulnerabilities are detected, but processing efficiency decreases
Solution Approach 1:
The patent implements segmentation of log files into fixed-size chunks with overlap regions, enabling efficient processing while maintaining detection completeness. The segmentation strategy allows the system to process discrete chunks independently, improving processing efficiency, while the overlap ensures that vulnerable information spanning boundaries is not missed, maintaining detection completeness.
Solution Approach 2:
The system extracts and processes only the relevant subset of log file chunks that contain vulnerable information, rather than performing comprehensive review of entire log files. This extraction approach improves processing efficiency by eliminating redundant processing of irrelevant content while maintaining detection completeness through the LLM's ability to identify and process only the necessary chunks.
4Productivity
If fixed chunk size is used for processing, then processing efficiency improves, but vulnerable information spanning chunks may be missed
Solution Approach 1:
The patent implements segmentation with a specific overlap strategy where adjacent chunks overlap by a defined number of lines. This segmentation approach maintains processing efficiency by dividing files into fixed-size manageable chunks while ensuring detection completeness through the overlap mechanism that captures vulnerable information spanning chunk boundaries multiple times.
Solution Approach 2:
The system applies beforehand cushioning by including overlap regions in each chunk that anticipate vulnerable information may span boundaries. This prior cushioning strategy ensures that even if vulnerable information is split across chunks, it is captured in the overlap regions, preventing missed detections while maintaining the efficiency benefits of fixed-size chunk processing.
Data Source
AI summary
Various implementations generally relate to systems and methods for identifying and handling vulnerable information in log files of an enterprise, including receiving a first set of obfuscated log files from a log storage or an application that generated log files in the first set, and processing the first set of obfuscated log files using a large language model (LLM) to identify a subset of the first set of obfuscated log files that contain a vulnerability.