# EAGER: Securing AI Research: Developing and Validating a Lifecycle-Based Typology of Threats Through Critical Incident Analysis and Participatory Engagement

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Virginia Polytechnic Institute and State University (VA) · $300,000

## Abstract

Artificial intelligence (AI) is transforming industries, education, and society. As its influence grows, so do the risks associated with AI research. This project explores how to better safeguard AI research from growing security threats, including data breaches, stolen intellectual property, and foreign interference, that could undermine its integrity and impact. By developing a new framework that maps out potential threats across every stage of the AI research lifecycle, it aims to help institutions, educators, and policymakers understand and address vulnerabilities in AI research more effectively and holistically. Through collaboration with stakeholders across disciplines and sectors and the use of innovative research methods, this work will not only advance the field of Research on Research Security (RoRS) but also support safer, more resilient AI innovation for the public good.

AI research is increasingly recognized as both a strategic capability and a domain of significant vulnerability. As global competition intensifies, AI research faces a growing array of security threats, including theft of proprietary algorithms, unauthorized access to sensitive training data, premature dissemination of high-risk findings, and undue foreign influence. Despite the urgency of these issues, the emerging field of Research on Research Security encounters some persistent challenges: (1) limited access to empirical research security data; (2) the absence of a robust, interdisciplinary 

## Key facts

- **NSF award ID:** 2537450
- **Awardee organization:** Virginia Polytechnic Institute and State University (VA)
- **SAM.gov UEI:** QDE5UHE5XD16
- **PI:** Qin Zhu
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** EAGER
- **Estimated total:** $300,000
- **Funds obligated:** $300,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2537450

## Citation

> US National Science Foundation, Award 2537450, EAGER: Securing AI Research: Developing and Validating a Lifecycle-Based Typology of Threats Through Critical Incident Analysis and Participatory Engagement. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2537450. Licensed CC0.

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