Paper
A LINDDUN-based Privacy Threat Modeling Framework for GenAI
Authors
Qianying Liao, Jonah Bellemans, Laurens Sion, Xue Jiang, Dmitrii Usynin, Xuebing Zhou, Dimitri Van Landuyt, Lieven Desmet, Wouter Joosen
Abstract
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.
Metadata
Related papers
Gen-Searcher: Reinforcing Agentic Search for Image Generation
Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, Kaixuan Fan, Yilei Jian... • 2026-03-30
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or • 2026-03-30
Graphilosophy: Graph-Based Digital Humanities Computing with The Four Books
Minh-Thu Do, Quynh-Chau Le-Tran, Duc-Duy Nguyen-Mai, Thien-Trang Nguyen, Khan... • 2026-03-30
ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath • 2026-03-30
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
Oliver Aleksander Larsen, Mahyar T. Moghaddam • 2026-03-30
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.06051v1</id>\n <title>A LINDDUN-based Privacy Threat Modeling Framework for GenAI</title>\n <updated>2026-03-06T09:04:32Z</updated>\n <link href='https://arxiv.org/abs/2603.06051v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.06051v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CR'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SE'/>\n <published>2026-03-06T09:04:32Z</published>\n <arxiv:comment>21 pages, 5 figures</arxiv:comment>\n <arxiv:primary_category term='cs.CR'/>\n <author>\n <name>Qianying Liao</name>\n </author>\n <author>\n <name>Jonah Bellemans</name>\n </author>\n <author>\n <name>Laurens Sion</name>\n </author>\n <author>\n <name>Xue Jiang</name>\n </author>\n <author>\n <name>Dmitrii Usynin</name>\n </author>\n <author>\n <name>Xuebing Zhou</name>\n </author>\n <author>\n <name>Dimitri Van Landuyt</name>\n </author>\n <author>\n <name>Lieven Desmet</name>\n </author>\n <author>\n <name>Wouter Joosen</name>\n </author>\n </entry>"
}