Binary Code Description Using a File Encoder and LLM
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Solution Overview
Problem
Binary software code is not easily readable or analyzable by humans, making it difficult to assess security risks such as malware presence effectively.
Innovation Solution
A system utilizing a file encoder model and a large language model (LLM) to generate a natural language description of binary software code, trained on a data lake of text descriptions related to malware and other software aspects, enabling automated and accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary software code is used for security analysis, then malware detection capability is improved, but readability and analyzability by humans deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising a file encoder model and a large language model that translates binary code into natural language descriptions. The file encoder model processes binary code to extract features, and the LLM generates human-readable descriptions of security risks, malware behavior, and code functionality. This intermediary translation layer resolves the contradiction by maintaining the analytical power of binary code while providing human-friendly explanations.
2Measurement precision
If manual analysis of binary code is performed, then accuracy of security assessment is improved, but time consumption increases
Solution Approach 1:
The patent replaces the mechanical process of manual binary code analysis with an automated machine learning system. The file encoder model and LLM automatically process binary code, extract security features, and generate comprehensive security assessments without human intervention. This substitution maintains high accuracy through sophisticated model architecture while dramatically reducing time consumption through automated processing.
Solution Approach 2:
The system enables self-service automated analysis where the machine learning models independently process binary code and generate security reports without requiring manual analysis. The models automatically identify malware characteristics, assess security risks, and produce detailed descriptions, freeing analysts from time-consuming manual examination while maintaining assessment accuracy.
3Measurement precision
If complex machine learning models are used for binary code analysis, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into two distinct model components: a file encoder model for processing binary code and extracting features, and a large language model for generating natural language descriptions. This segmentation allows each component to be optimized for its specific function, improving overall detection accuracy while making the system more manageable and interpretable compared to a single monolithic complex model.
Data Source
AI summary
Systems, methods, and software can be used to generate natural language description of a software code. In some aspects, a method includes: processing a binary code by using a file encoder model to obtain a file embedding vector; and processing the file embedding vector to obtain a text description of the binary code by using a large language model (LLM).