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.

翻译：大型语言模型（LLMs）彻底改变了我们与机器的交互方式。然而，伴随着这一技术进步，出现了“Mallas”——一种在地下运作的恶意服务，其利用LLMs从事非法活动。此类服务能够生成恶意软件、钓鱼攻击和欺诈性网站，从而加剧了网络安全威胁的态势。本文通过考察多种预训练语言模型在被滥用时的效率与脆弱性，深入探讨了Mallas的扩散现象。基于通用漏洞披露（CVE）项目的数据集，本文探索了用于生成与已识别漏洞相关的代码及解释性文本的微调方法。本研究旨在揭示Mallas的运作策略与利用技术，从而推动开发更安全、可信的人工智能应用。文章最后强调，需要进一步的研究、加强防护措施并制定伦理准则，以减轻LLMs恶意应用所带来的风险。