Malla：揭秘现实世界中的大语言模型集成恶意服务
Malla: Demystifying Real-world Large Language Model Integrated Malicious Services
AI总结：
本研究首次系统分析了212个现实世界中的大语言模型集成恶意服务（Malla），揭示其地下市场泛滥、运作模式及利用越狱提示词绕过公共LLM API的策略，为应对此类网络犯罪提供见解。
摘要：
大语言模型（LLM）在地下被用于恶意服务（即Malla）的现象正在增加，这加剧了网络威胁态势，并对LLM技术的可信度提出了质疑。然而，对于这种新型网络犯罪的规模、影响和技术手段，目前鲜有研究。在本文中，我们对212个现实世界中的Malla进行了首次系统性研究，揭示了它们在地下市场的泛滥情况及其运作模式。我们的研究披露了Malla生态系统，展示了其对当今公共LLM服务的显著增长和影响。通过检查212个Malla，我们发现了Malla使用的八个后端LLM，以及182个绕过公共LLM API保护措施的提示词。我们进一步揭秘了Malla采用的策略，包括滥用未经审查的LLM以及通过越狱提示词利用公共LLM API。我们的研究结果有助于更好地理解网络犯罪分子对LLM的现实世界利用，为应对这种网络犯罪提供策略见解。
英文摘要：
The underground exploitation of large language models (LLMs) for malicious services (i.e., Malla) is witnessing an uptick, amplifying the cyber threat landscape and posing questions about the trustworthiness of LLM technologies. However, there has been little effort to understand this new cybercrime, in terms of its magnitude, impact, and techniques. In this paper, we conduct the first systematic study on 212 real-world Mallas, uncovering their proliferation in underground marketplaces and exposing their operational modalities. Our study discloses the Malla ecosystem, revealing its significant growth and impact on today's public LLM services. Through examining 212 Mallas, we uncovered eight backend LLMs used by Mallas, along with 182 prompts that circumvent the protective measures of public LLM APIs. We further demystify the tactics employed by Mallas, including the abuse of uncensored LLMs and the exploitation of public LLM APIs through jailbreak prompts. Our findings enable a better understanding of the real-world exploitation of LLMs by cybercriminals, offering insights into strategies to counteract this cybercrime.
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。
