Study Examines LLMs' Role in Static Malware Analysis
/ 4 min read
Quick take - A recent study has demonstrated that large language models (LLMs) can enhance static malware analysis by achieving up to 90.9% accuracy in explaining malware functionality, while also identifying challenges and potential improvements for integrating LLMs into cybersecurity practices.
Fast Facts
- Large language models (LLMs) show promise in enhancing static malware analysis, achieving up to 90.9% accuracy in explaining malware functionality.
- A study involving six static analysts assessed LLM-generated explanations, revealing both benefits and challenges in integrating LLMs into static analysis workflows.
- Key research questions focused on the usefulness of LLM outputs, the impact of prompt formulation, and the challenges of applying LLM support in static analysis.
- Participants rated LLM outputs positively for fluency and relevance, but raised concerns about confidentiality and the potential for irrelevant explanations due to poorly structured prompts.
- Future research aims to develop local LLMs to address confidentiality issues and further evaluate LLM effectiveness in static analysis, emphasizing that LLMs should complement, not replace, traditional methods.
Large Language Models in Cybersecurity
Large language models (LLMs) are increasingly being recognized for their potential applications across various domains, including cybersecurity. A recent study has highlighted the potential of LLMs to enhance static malware analysis, a task traditionally known for its complexity and the expertise it demands.
Study Overview
The study demonstrated the effectiveness of LLMs in supporting static analysis, achieving an accuracy of up to 90.9% in explaining malware functionality. Six static analysts participated in a pseudo static analysis task as part of the study. They utilized LLM-generated explanations to assess the practical applicability of this technology.
Researchers employed questionnaires and interviews to gather comprehensive feedback. This approach helped identify both the challenges and the essential functions required for integrating LLMs as support tools in static analysis.
Static malware analysis involves various methods, including surface analysis, dynamic analysis, and static analysis itself. Static analysis is notably less susceptible to evasion techniques. However, it is primarily conducted manually due to usability issues with existing automation tools. The study aimed to explore the potential of LLMs in the cybersecurity realm, given their high performance in natural language processing tasks.
Research Questions
The research was structured around four core questions:
- RQ1: Can LLMs generate useful explanations for static analysis?
- RQ2: How does the formulation of prompts affect the explanatory capabilities of LLMs?
- RQ3: What is the practical usefulness of LLM outputs for analysts?
- RQ4: What challenges are associated with the application of LLM support in static analysis?
For the evaluation, the malware sample selected was Babuk, which includes a total of 107 functions, 62 of which are specific to the malware. The methodology involved assessing the accuracy of LLM-generated explanations against published analysis articles.
Findings and Future Directions
Various prompts were employed to gauge the LLM’s performance. The user study followed a structured procedure, encompassing preliminary explanations, analysis tasks, post-test questionnaires, and interviews. Analysts worked within a unified analysis environment utilizing Ghidra, allowing them to view LLM explanations alongside decompiled results.
The post-test questionnaire evaluated participants’ backgrounds, familiarity with malware, and the perceived quality of the LLM outputs. Interviews provided deeper insights into their experiences and challenges encountered when using LLMs in static analysis.
Findings revealed that LLMs could produce explanations beneficial for static analysis. However, the accuracy of outputs varied depending on the type of input provided. Participants generally rated LLM outputs positively regarding fluency, relevance, informativeness, and practicality. Some participants expressed concerns about the potential for irrelevant or confusing explanations, particularly when prompts were poorly structured.
Confidentiality was a significant issue, as participants raised concerns about using external LLMs with sensitive information. Participants also noted that obfuscation and junk code could negatively impact the accuracy of LLM-generated explanations. Suggestions for improvement included offering a general overview of malware and enhancing the integration of LLM outputs into analysis tools.
The study underscored the necessity for usability and functionality in designing systems that incorporate LLMs for static analysis. Future research will focus on developing local LLMs to mitigate confidentiality concerns, with further assessment of the effectiveness of LLMs in static analysis also planned.
The conclusion drawn from the study indicated that while LLMs can augment static analysis efforts, they should not be regarded as a complete replacement for traditional analysis methods.
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