A Preliminary Exploratory Assessment of ChatGPT to Generating STRIDE Data Flow Diagrams

Document Type

Conference Proceeding

Source of Publication

International Conference on Evaluation of Novel Approaches to Software Engineering Enase Proceedings

Publication Date

1-1-2026

Abstract

Threat modeling is a core activity in security-by-design practices, enabling early identification of architectural weaknesses before system implementation. The drawings used during STRIDE analysis are typically Data Flow Diagrams (DFDs), referred to as “STRIDE diagrams” in this paper. STRIDE diagrams provide a visual approach for categorizing security threats; however, constructing accurate STRIDE diagrams require experience and is often time-consuming. Recent advances in Large Language Models (LLMs), such as ChatGPT, raise important questions about their suitability for supporting structured security modeling tasks. This study presents a preliminary exploratory assessment of ChatGPT’s ability to generate, analyse, and iteratively refine STRIDE diagrams from natural language system descriptions. The evaluation focuses on syntactic correctness, semantic accuracy, trust-boundary placement, and the model’s capacity to incorporate iterative feedback. Using multiple case studies with varying architectural complexity, the study examines how system size and prompt structure influence the quality of the generated diagrams. The findings shed light on the strengths and limitations of LLMs in early-stage threat modeling and assess their potential role as a supportive tool in security engineering workflows.

ISBN

[9789897588280]

ISSN

2184-4895

Publisher

SCITEPRESS - Science and Technology Publications

Volume

2

First Page

1047

Last Page

1058

Disciplines

Computer Sciences

Keywords

ChatGPT, Cybersecurity, Data Flow Diagrams, Large Language Models, STRIDE Framework, Threat Modeling

Scopus ID

105046569620

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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