Amir Reza Asadi, Joel Kwesi Appiah, Taiwo Peter Akinemi, and Hazem Said, University of Cincinnati
The computing world increasingly focuses on data collection, and the integration of advanced IT technologies creates new privacy and security vulnerabilities. Traditional approaches to security and privacy testing lack the scale and diversity needed to anticipate the full range of potential vulnerabilities. This ongoing work proposes using large language models (LLMs) to identify these vulnerabilities by having LLMs role-play personas of diverse users, including threat actors, regular users, and security practitioners. We created a pool of 128 individual personas derived from security and privacy literature and developed a framework to evaluate how effectively LLMs can embody these personas across standardized security scenarios. We validate persona simulation using this framework.
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