As large language models (LLMs) increasingly support sensitive decision-making, understanding their vulnerability to bias becomes critical for maintaining system integrity. However, we lack reliable methods to measure how easily these systems can be compromised through biased training data. We present the first approach to quantifying “imitation thresholds“, defined as the minimum number of training examples needed to significantly shift decision patterns in LLMs applied to legal decision-making. Our experiments span four models across two size categories, 24 judicial personas, and four legal domains, revealing distinct patterns of susceptibility and resistance to persona-driven biases that persist across model architectures and scales. By incrementally fine-tuning LLMs with persona-specific text, we identify thresholds at which model predictions diverge meaningfully from control decision patterns. Our findings show that as few as 80 training examples can reliably alter model behavior, with significant variation across domains and influencer persona types. We further find that susceptibility scales with model capacity, with larger models showing substantially stronger effects, particularly for negative influence. Our work provides a foundational understanding of bias propagation mechanisms in LLMs. We publicly release our datasets and code to support future research in this area.