STRIDE GPT, an AI-powered threat modeling tool, leverages the capabilities of large language models (LLMs) to generate comprehensive threat models and attack trees for applications, ensuring a proactive approach to security. In conclusion, STRIDE GPT represents a significant advancement in threat modeling, leveraging the power of LLMs to provide detailed, actionable insights into potential security threats. Developed by Matthew Adams, Head of Security Enablement at Citi, STRIDE GPT integrates the STRIDE methodology with the power of LLMs to automate the process of identifying potential threats and vulnerabilities in software applications. It also generates Gherkin test cases based on identified threats, bridging the gap between threat modeling and testing, ensuring that security considerations are integrated into the testing process. STRIDE GPT’s development has been marked by continuous improvements, with recent updates including support for GitHub repository analysis, allowing for a more comprehensive threat modeling by analyzing the README and key files of repositories. Based on this information, STRIDE GPT generates detailed threat models, and attack trees and suggests possible mitigations for identified threats. This holistic approach to security is further enhanced by STRIDE GPT’s ability to suggest possible mitigations, making it a valuable asset for cybersecurity professionals and development teams alike. One of the standout features of STRIDE GPT is its multimodal capability, which allows users to incorporate architecture diagrams, flowcharts, and other visual representations into the threat modeling process. With its focus on STRIDE methodology and the integration of AI, STRIDE GPT stands at the forefront of modern cybersecurity practices, offering a glimpse into the future where AI-driven security solutions are the norm. Matthew Adams presented STRIDE GPT at the Open Security Summit in January 2024, where he discussed the project’s inception, its core functionalities, and future plans. STRIDE GPT also supports locally hosted models via Ollama and LM Studio Server for data privacy concerns, ensuring that application details are not stored, thus maintaining confidentiality.
This Cyber News was published on cybersecuritynews.com. Publication date: Sun, 13 Apr 2025 04:25:05 +0000