We present Auspex - a threat modeling system built using a specialized collection of generative artificial intelligence-based methods that capture threat modeling tradecraft. This new approach, called tradecraft prompting, centers on encoding the on-the-ground knowledge of threat modelers within the prompts that drive a generative AI-based threat modeling system. Auspex employs tradecraft prompts in two processing stages. The first stage centers on ingesting and processing system architecture information using prompts that encode threat modeling tradecraft knowledge pertaining to system decomposition and description. The second stage centers on chaining the resulting system analysis through a collection of prompts that encode tradecraft knowledge on threat identification, classification, and mitigation. The two-stage process yields a threat matrix for a system that specifies threat scenarios, threat types, information security categorizations and potential mitigations. Auspex produces formalized threat model output in minutes, relative to the weeks or months a manual process takes. More broadly, the focus on bespoke tradecraft prompting, as opposed to fine-tuning or agent-based add-ons, makes Auspex a lightweight, flexible, modular, and extensible foundational system capable of addressing the complexity, resource, and standardization limitations of both existing manual and automated threat modeling processes. In this connection, we establish the baseline value of Auspex to threat modelers through an evaluation procedure based on feedback collected from cybersecurity subject matter experts measuring the quality and utility of threat models generated by Auspex on real banking systems. We conclude with a discussion of system performance and plans for enhancements to Auspex.

本文提出了Auspex，一个基于生成性人工智能方法构建的威胁建模系统，旨在解决传统威胁建模过程中耗时与复杂性的问题。该系统采用了一种称为“技艺提示”的新方法，将威胁建模专家的知识编码到生成性AI的提示中，从而在短时间内生成正式的威胁模型输出。研究结果表明，Auspex可以显著提高威胁建模的效率，为网络安全领域提供了一种灵活且可扩展的解决方案。

Auspex：将威胁建模技艺融入人工智能辅助工具