The rapid integration of Large Language Models (LLMs) across diverse sectors has marked a transformative era, showcasing remarkable capabilities in text generation and problem-solving tasks. However, this technological advancement is accompanied by significant risks and vulnerabilities. Despite ongoing security enhancements, attackers persistently exploit these weaknesses, casting doubts on the overall trustworthiness of LLMs. Compounding the issue, organisations are deploying LLM-integrated systems without understanding the severity of potential consequences. Existing studies by OWASP and MITRE offer a general overview of threats and vulnerabilities but lack a method for directly and succinctly analysing the risks for security practitioners, developers, and key decision-makers who are working with this novel technology. To address this gap, we propose a risk assessment process using tools like the OWASP risk rating methodology which is used for traditional systems. We conduct scenario analysis to identify potential threat agents and map the dependent system components against vulnerability factors. Through this analysis, we assess the likelihood of a cyberattack. Subsequently, we conduct a thorough impact analysis to derive a comprehensive threat matrix. We also map threats against three key stakeholder groups: developers engaged in model fine-tuning, application developers utilizing third-party APIs, and end users. The proposed threat matrix provides a holistic evaluation of LLM-related risks, enabling stakeholders to make informed decisions for effective mitigation strategies. Our outlined process serves as an actionable and comprehensive tool for security practitioners, offering insights for resource management and enhancing the overall system security.

翻译：大型语言模型在各行业的快速集成标志着变革性时代的到来，其在文本生成与问题解决任务中展现出非凡能力。然而，这一技术进步伴随着显著风险与脆弱性。尽管安全防护持续强化，攻击者仍不断利用这些弱点，使人们对大型语言模型的整体可信度产生质疑。更棘手的是，众多组织在未充分理解潜在后果严重性的情况下，部署了集成大型语言模型的系统。OWASP与MITRE的现有研究虽提供了威胁与漏洞的通用概览，但缺乏直接且简洁的方法来帮助安全从业者、开发人员及关键决策者分析该新型技术带来的风险。为弥补这一缺口，我们提出采用适用于传统系统的OWASP风险评级方法等工具，构建风险评估流程。通过场景分析识别潜在威胁主体，并将依赖系统组件映射至漏洞因素矩阵。基于此分析评估网络攻击发生概率，继而开展全面影响分析以推导综合性威胁矩阵。我们还针对三类关键利益相关者（从事模型微调的开发人员、使用第三方API的应用开发者及终端用户）进行了威胁映射。所提出的威胁矩阵为评估大型语言模型相关风险提供整体性框架，使利益相关者能制定有效缓解策略。本研究所列流程为安全从业者提供可操作的综合性工具，助力资源管理并强化系统整体安全性。