标题： LLMalMorph：使用大型语言模型生成变种恶意软件的可行性研究 显示英文标题

标题： LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models

摘要： 大型语言模型（LLMs）已经改变了软件开发和自动化代码生成。 受这些进展的启发，本文探讨了LLMs在修改恶意软件源代码以生成变体方面的可行性。 我们引入了LLMalMorph，这是一个半自动框架，利用LLMs的语义和语法代码理解来生成新的恶意软件变体。 LLMalMorph从恶意软件源代码中提取功能级信息，并使用自定义设计的提示词以及策略性定义的代码转换来引导LLM生成变体，而无需资源密集型的微调。 为了评估LLMalMorph，我们收集了10个不同类型的Windows恶意软件样本，其复杂性和功能各不相同，并生成了618个变体。 我们的全面实验表明，可以在一定程度上降低这些恶意软件变体的杀毒引擎检测率，同时保留恶意软件的功能。 此外，尽管没有针对任何基于机器学习（ML）的恶意软件检测器进行优化，但一些变体在基于ML的恶意软件分类器上也实现了显著的攻击成功率。 我们还讨论了当前LLM在从源代码生成恶意软件变体方面的局限性，并评估了这项新兴技术在更广泛的恶意软件变体生成背景中的地位。 显示英文摘要

摘要： Large Language Models (LLMs) have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages semantical and syntactical code comprehension by LLMs to generate new malware variants. LLMalMorph extracts function-level information from the malware source code and employs custom-engineered prompts coupled with strategically defined code transformations to guide the LLM in generating variants without resource-intensive fine-tuning. To evaluate LLMalMorph, we collected 10 diverse Windows malware samples of varying types, complexity and functionality and generated 618 variants. Our thorough experiments demonstrate that it is possible to reduce the detection rates of antivirus engines of these malware variants to some extent while preserving malware functionalities. In addition, despite not optimizing against any Machine Learning (ML)-based malware detectors, several variants also achieved notable attack success rates against an ML-based malware classifier. We also discuss the limitations of current LLM capabilities in generating malware variants from source code and assess where this emerging technology stands in the broader context of malware variant generation.