As generative AI (GenAI) systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly important. Specifically, both the ability to harness and process large swaths of information as well as their stochastic nature raise key concerns related to both security and privacy. Unfortunately, while some of the traditional security threat modeling can effectively identify certain violations, privacy-related issues are often overlooked. To respond to these challenges, we introduce a novel domain-specific privacy threat modeling framework to support the privacy threat analysis of GenAI-based applications. This framework is constructed through a two-pronged approach: (1) a systematic review of the emerging literature on GenAI privacy threats, and (2) a case-driven application to a representative Chatbot system. These efforts yield a foundational GenAI privacy threat modeling framework built on LINDDUN. The new framework affects three out of the seven privacy threat types of LINDDUN and introduces 100 new GenAI examples to the knowledge base. Its effectiveness is validated on an AI Agent system, which demonstrates that a comprehensive privacy analysis can be supported by the new framework.

翻译：随着生成式人工智能（GenAI）系统在各种技术栈中日益普及，此类系统如何处理敏感数据和个人数据流的问题变得愈发重要。具体而言，其处理海量信息的能力、随机性本质均引发了与安全和隐私相关的核心关切。遗憾的是，尽管传统的安全威胁建模方法能有效识别某些违规行为，但隐私相关问题常被忽视。为应对这些挑战，本文提出了一种新颖的领域专用隐私威胁建模框架，以支持基于GenAI应用的隐私威胁分析。该框架通过双重路径构建：(1) 对GenAI隐私威胁新兴文献进行系统性梳理，(2) 在典型聊天机器人系统中开展案例驱动式应用。这些工作构建了一个基于LINDDUN的基础性GenAI隐私威胁建模框架。新框架影响了LINDDUN七类隐私威胁中的三类，并为知识库新增了100个GenAI实例。通过在AI智能体系统上的验证表明，该新框架能够有效支持全面的隐私分析。