2026· International Conference on Security and Cryptography· pp. 689-700· 0 citations· 38 references
Computer Science
TL;DR
A systematic evaluation framework for LLM-driven threat modelling tools to support tool selection, observing the general LLM-integration, governance risks, and allowing for comparison of tool output is introduced.
Abstract
: AI adoption has accelerated with the rise of LLMs, and people within the field of security are increasingly exploring their practical value. Threat modelling is central to secure system development, yet it remains largely manual and the value of LLM-driven tools is unclear. Even when LLMs prove useful, selecting the right one can be more challenging than using it. This paper introduces a systematic evaluation framework for LLM-driven threat modelling tools to support tool selection, observing the general LLM-integration, governance risks, and allowing for comparison of tool output. Using Goal-Question-Metric, we derive evaluation metrics and show the value of the framework on a set of state-of-the-art LLM-driven threat modelling tools. The results show our metrics distinguish both performance and governance risks, providing a basis for organisations to ensure automation strengthens rather than burdens their threat modelling process.
The paper details the five-module structure of ARAMIS, its unique multilayered modelling of operational scenarios and its algorithmic approach to calculating security levels target (SL-T), and discusses the implementation of the methodology within the Fence risk management tool to ensure seamless reproducibility and kn...
Serge Benoliel, Florence Foudrain· International Conference on...· 0 citations
The growing complexity and frequency of cyberattacks make cybersecurity risk assessment an increasingly demanding task for organisations, requiring substantial expertise, resources, and adherence to established standards. This work explores the applicability of Large Language Model (LLM) to cybersecurity risk assessmen...
This paper proposes a structured protocol to automate AI risk mitigation through a taxonomy-driven analysis of open-source LLM evaluation and security tools, and presents a taxonomy-driven framework applicable to open-source and proprietary solutions.
Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj et al.· 0 citations
Large language models (LLMs) are increasingly embedded in identity and access management (IAM) tools that support workflows such as account recovery, access request triage, provisioning, policy interpretation, and privileged access handling. In these settings, security risk is often dominated not by the model in isolat...
Sanaa S. Mironov, Shahmir Rizvi, B. Shariati· 2026 IEEE European Symposium...· 0 citations
A critical analysis of tools and trust mark frameworks intended to operationalize trustworthy AI (TAI), drawing on a comprehensive dataset from the OECD identifies significant asymmetries in ethical focus, lifecycle coverage, stakeholder targeting, and tool typology.
Michael Papademas, Xenia Ziouvelou, K. Karpouzis et al.· arXiv.org· 0 citations
The case is made for a first-principles approach that CTI teams can adopt as an unbiased anchor to guide their decisions around establishing an adequate CTI capability, and pragmatic recommendations to assist CTI teams with qualifying their prospective vendors to ensure good fit are offered.
Aaron Aubrey Ng· International Conference on...· 0 citations
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