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

Abstract

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 standard

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@misc{cyrille2026strideai,
  title = {{STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment}},
  author = {Tsafac Nkombong Regine Cyrille and Franziska Schwarz},
  year = {2026},
  month = may,
  eprint = {2605.17163},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.17163}
}