Large language models (LLMs) have revolutionized how we interact with
machines. However, this technological advancement has been paralleled by the
emergence of "Mallas," malicious services operating underground that exploit
LLMs for nefarious purposes. Such services create malware, phishing attacks,
and deceptive websites, escalating the cyber security threats landscape. This
paper delves into the proliferation of Mallas by examining the use of various
pre-trained language models and their efficiency and vulnerabilities when
misused. Building on a dataset from the Common Vulnerabilities and Exposures
(CVE) program, it explores fine-tuning methodologies to generate code and
explanatory text related to identified vulnerabilities. This research aims to
shed light on the operational strategies and exploitation techniques of Mallas,
leading to the development of more secure and trustworthy AI applications. The
paper concludes by emphasizing the need for further research, enhanced
safeguards, and ethical guidelines to mitigate the risks associated with the
malicious application of LLMs.

探讨大型语言模型在恶意服务 Mallas 的滥用方面的有效性和漏洞，以及针对已识别漏洞生成代码和解释文本的微调方法。研究旨在揭示 Mallas 的操作策略和攻击技术，从而为开发更安全可信赖的人工智能应用程序提供指导。强调进一步研究、增强保护措施和伦理准则以应对大型语言模型恶意应用的风险。