通过探索微调大型语言模型来转变计算机安全与公众信任
Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models
AI总结：
本文基于 CVE 数据集研究 LLM 微调被 Mallas 滥用于生成漏洞代码与文本的风险，并提出安全防护与伦理治理方向。
摘要：
大型语言模型（LLM）已经彻底改变了我们与机器交互的方式。然而，与这一技术进步相伴出现的是“Mallas”，即在地下运作、出于邪恶目的利用 LLM 的恶意服务。此类服务会制造恶意软件、网络钓鱼攻击和欺骗性网站，使网络安全威胁格局不断升级。本文通过考察各种预训练语言模型的使用，以及它们在被滥用时的效率和脆弱性，深入研究 Mallas 的扩散。基于来自 Common Vulnerabilities and Exposures（CVE，通用漏洞披露）项目的数据集，本文探索了多种微调方法，以生成与已识别漏洞相关的代码和说明性文本。本研究旨在揭示 Mallas 的运作策略和利用技术，从而推动开发更安全、更可信的 AI 应用。论文最后强调，需要进一步研究、强化保障措施并制定伦理准则，以缓解与恶意应用 LLM 相关的风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
