LLM Malware Detection Reasoning for Explainable Verdicts
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Solution Overview
Problem
Existing malware detection systems, including AI/ML solutions, struggle to provide accurate and explainable verdicts on malware and benign samples, often leading to false positives and a lack of understanding of detection reasoning.
Innovation Solution
Implementing a Large Language Model (LLM) powered detection reasoning solution that provides real-time explanations for malware detection verdicts, applicable to both static and dynamic detection mechanisms, enhancing inline and offline reporting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection methods are used, then detection speed is maintained, but detection accuracy and explainability deteriorate due to inability to provide clear reasoning for verdicts
Solution Approach 1:
The patent introduces an LLM as an intermediary component between the malware detection system and the end user. The LLM receives detection results and generates human-understandable explanations, acting as a mediator that translates technical detection outcomes into comprehensible reasoning without affecting the core detection accuracy of the underlying system.
Solution Approach 2:
The system segments the detection process into distinct functional components: the core detection engine that provides accurate verdicts, and the LLM-based explanation generator that provides interpretability. This segmentation allows each component to specialize in its strength while working together to resolve the contradiction between accuracy and explainability.
2Adaptability or versatility
If sophisticated obfuscation techniques are used by malware authors, then malware evasion capability improves, but detection reliability deteriorates
Solution Approach 1:
The LLM provides feedback by analyzing detection results and generating explanations that reveal the reasoning behind verdicts. This feedback mechanism allows security analysts to understand why certain obfuscated malware was detected, improving the overall reliability of the detection system by making its decision-making process transparent and verifiable.
3Loss of information
If LLM-powered reasoning is added to detection systems, then explanation quality improves, but system complexity increases
Solution Approach 1:
The LLM component serves multiple functions simultaneously: it generates explanations for detection verdicts, analyzes malware characteristics, and provides reasoning for security analysts. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while delivering high-quality explanations.
4Productivity
If real-time reasoning is implemented, then detection responsiveness improves, but processing time increases
Solution Approach 1:
The system performs preliminary detection using traditional fast methods first, then selectively applies LLM-powered reasoning only when needed for complex cases or when explanation quality is required. This preliminary action approach maintains fast detection responsiveness for straightforward cases while providing detailed reasoning when necessary, balancing processing time with explanation quality.
Data Source
AI summary
Various techniques for LLM powered detection reasoning solutions are disclosed. In some embodiments, a system, a process, and/or a computer program product for an LLM powered detection reasoning solution includes monitoring network traffic at a security platform, wherein the security platform generates a sample based on the monitored network traffic; sending the sample to a security service to generate a Large Language Model (LLM) powered detection and reason, wherein the LLM is prompted to automatically generate a malware or benign verdict and a reason for explaining the verdict; and reporting the LLM powered detection and reason.