This paper introduces PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that systematically assesses unintended consequences of LLM personalization: irrelevant personal-information usage, preference narrowing, and sycophantic bias. Evaluating 13 LLMs, the authors find personalized information consistently exacerbates all three risks, with performance drops ranging from roughly 42% to 62% across the measured dimensions.
