Artificial intelligence systems introduce complex privacy risks throughout their lifecycle, especially when processing sensitive or high-dimensional data. Beyond the seven traditional privacy threat categories defined by the LINDDUN framework, AI systems are also exposed to model-centric privacy attacks such as membership inference and model inversion, which LINDDUN does not cover. To address both classical LINDDUN threats and additional AI-driven privacy attacks, PriMod4AI introduces a hybrid privacy threat modeling approach that unifies two structured knowledge sources, a LINDDUN knowledge base representing the established taxonomy, and a model-centric privacy attack knowledge base capturing threats outside LINDDUN. These knowledge bases are embedded into a vector database for semantic retrieval and combined with system level metadata derived from Data Flow Diagram. PriMod4AI uses retrieval-augmented and Data Flow specific prompt generation to guide large language models (LLMs) in identifying, explaining, and categorizing privacy threats across lifecycle stages. The framework produces justified and taxonomy-grounded threat assessments that integrate both classical and AI-driven perspectives. Evaluation on two AI systems indicates that PriMod4AI provides broad coverage of classical privacy categories while additionally identifying model-centric privacy threats. The framework produces consistent, knowledge-grounded outputs across LLMs, as reflected in agreement scores in the observed range.

翻译：人工智能系统在其整个生命周期中，尤其是在处理敏感或高维数据时，会引入复杂的隐私风险。除了LINDDUN框架定义的七类传统隐私威胁外，AI系统还面临LINDDUN未涵盖的以模型为中心的隐私攻击，例如成员推理和模型反演。为同时应对经典LINDDUN威胁和额外的AI驱动隐私攻击，PriMod4AI提出了一种混合隐私威胁建模方法。该方法统一了两个结构化知识源：代表既定分类体系的LINDDUN知识库，以及捕获LINDDUN范围外威胁的以模型为中心的隐私攻击知识库。这些知识库被嵌入向量数据库以实现语义检索，并与从数据流图提取的系统级元数据相结合。PriMod4AI采用检索增强和数据流特定的提示生成技术，引导大语言模型识别、解释和分类跨生命周期阶段的隐私威胁。该框架生成基于分类体系的合理性威胁评估，整合了经典视角与AI驱动视角。在两个AI系统上的评估表明，PriMod4AI能广泛覆盖经典隐私威胁类别，同时额外识别以模型为中心的隐私威胁。该框架在不同LLM上均能产生一致且基于知识的输出，其一致性评分处于观测范围内。