Emerging challenges in threat modeling for GenAI-augmented systems: a view from the trenches
Citation Link: https://doi.org/10.15480/882.19076
Publikationstyp
Conference Paper
Date Issued
2026-10
Sprache
English
Author(s)
TORE-DOI
Article Number
57
Citation
20th International Symposium on Empirical Software Engineering and Measurement, ESEM 2026
Contribution to Conference
Publisher DOI
Threat modeling remains a central task in secure software engineering, as it enables the identification of security issues from system architectures. As Generative Artificial Intelligence (GenAI) becomes increasingly pervasive across software systems, traditional threat modeling methods (e.g., STRIDE) are insufficient to assess emerging GenAI-specific risks. In this work, we present the first results from an exploratory assessment of GenAI-aware threat modeling methods in a Small and Medium Enterprise (SME) setting. For this, we conducted a rapid literature review to select relevant techniques and systematically applied three shortlisted methods to an industrial case study involving a GenAIaugmented system. The results highlight differences in the threats identified by each technique and reveal limited support for certain GenAI-specific risk categories, particularly those related to software supply chains and human-centered security issues. We further report practitioners’ perceptions of the usability and integration of these methods in SME development workflows, including their
perceived effort and adoption challenges.
perceived effort and adoption challenges.
Subjects
Threat Modeling
Software Architectures
Generative AI
MI
Sec4AI
DDC Class
004: Computer Sciences
006.3: Artificial Intelligence
658: General Managament
Publication version
publishedVersion