G-STAR: A Threat Modeling Framework for General-Purpose AI Systems
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".