STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment

Computer Science > Cryptography and Security [Submitted on 16 May 2026] STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment Tsafac Nkombong Regine Cyrille, Franziska Schwarz Traditional cybersecurity methodologies target deterministic systems and fail to address the probabilistic nature of AI, leaving systems vulnerable to attack vectors such as model inversion, data poisoning, and prompt injection. Recent industry reports indicate that a majority of organizations deploying AI lack a dedicated security strategy, with adversarial attacks increasing rapidly year-over-year. We present \textit{STRIDE-AI}, a framework that bridges the gap between high-level risk standards (NIST AI RMF) and technical vulnerability taxonomies (OWASP LLM Top 10). The framework defines a six-phase assessment lifecycle, introduces a threat modeling adaptation of classical STRIDE for AI systems, and is operationalized through a purpose-built web tool. We provide an initial validation of the approach through a black-box assessment of a deployed LLM chatbot, which successfully reduced the attack success rate from 80\% to 15\% in our sandbox case study. Comments: 4 pages, 5 figures , 2 tables, CIIT 2026 23rd International Conference on Informatics and Information Technologies (CIIT) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2605.17163 [cs.CR]   (or arXiv:2605.17163v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.17163 Focus to learn more Submission history From: Tsafac Nkombong Regine Cyrille [view email] [v1] Sat, 16 May 2026 21:27:23 UTC (316 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)