This research article critically examines the potential risks and implications arising from the malicious utilization of large language models(LLM), focusing specifically on ChatGPT and Google's Bard. Although these large language models have numerous beneficial applications, the misuse of this technology by cybercriminals for creating offensive payloads and tools is a significant concern. In this study, we systematically generated implementable code for the top-10 MITRE Techniques prevalent in 2022, utilizing ChatGPT, and conduct a comparative analysis of its performance with Google's Bard. Our experimentation reveals that ChatGPT has the potential to enable attackers to accelerate the operation of more targeted and sophisticated attacks. Additionally, the technology provides amateur attackers with more capabilities to perform a wide range of attacks and empowers script kiddies to develop customized tools that contribute to the acceleration of cybercrime. Furthermore, LLMs significantly benefits malware authors, particularly ransomware gangs, in generating sophisticated variants of wiper and ransomware attacks with ease. On a positive note, our study also highlights how offensive security researchers and pentesters can make use of LLMs to simulate realistic attack scenarios, identify potential vulnerabilities, and better protect organizations. Overall, we conclude by emphasizing the need for increased vigilance in mitigating the risks associated with LLMs. This includes implementing robust security measures, increasing awareness and education around the potential risks of this technology, and collaborating with security experts to stay ahead of emerging threats.

翻译：本研究论文严格审视了大规模语言模型（LLM）被恶意利用的潜在风险与影响，重点关注ChatGPT和谷歌的Bard。尽管这些大规模语言模型具备众多有益应用，但网络犯罪分子滥用该技术生成攻击性载荷与工具的问题令人深切关注。在本研究中，我们利用ChatGPT系统性地生成了2022年十大流行MITRE技术的可执行代码，并对其与谷歌Bard的性能进行了对比分析。实验表明，ChatGPT具有使攻击者加速实施更具针对性和复杂攻击的潜力。此外，该技术为业余攻击者提供了执行多种攻击的能力，并使脚本小子能够开发定制化工具，从而加剧网络犯罪。更值得关注的是，LLM显著便利了恶意软件作者（尤其是勒索软件团伙）轻松生成复杂的擦除器和勒索软件变种。从积极角度看，我们的研究也揭示了攻击性安全研究人员和渗透测试员如何利用LLM模拟真实攻击场景、识别潜在漏洞，并更好地保护组织。总体而言，我们强调需要提高警惕以缓解LLM相关风险，包括实施稳健的安全措施、增强对该技术潜在风险的认知与教育，以及与安全专家合作以抢占新兴威胁的先机。