G-STAR: A Threat Modeling Framework for General-Purpose AI Systems

Abstract

This research presents the preliminary findings of an ongoing project focused on the security of General-Purpose AI (GPAI) applications. We introduce three key contributions: (i) a taxonomy of GPAI-specific vulnerabilities, offering a structured classification of security risks unique to GPAI models and applications; (ii) a generalized GPAI application architecture, serving as a meta-model for analyzing a wide range of real-world use cases; and (iii) G-STAR, a novel threat modeling reference framework that identifies key entities and their interrelationships in GPAI ecosystems, and provides a structured methodology for assessing and mitigating potential threats. Our study addresses both data and model vulnerabilities inherent in GPAI systems, highlighting critical security challenges. While the research is still in its early stages, the initial results provide a valuable foundation for continued investigation. Future work will focus on enhancing the generalized architecture, exploring mitigation strategies in depth, and applying and refining the G-STAR framework in real-world GPAI scenarios. This work aims to support AI security practitioners in promoting secure development and deployment of GPAI systems across diverse domains.

Intelligence

Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 93%

Last extracted: 3/11/2026, 12:42:36 AM

MATCH (f:Framework {name: 'G-STAR'})-[:PROVIDES|MODELS]->(component) RETURN component

MATCH (v:Vulnerability)-[:AFFECTS]->(g:Technology {name: 'General-Purpose AI'}) RETURN v

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