Researchers have demonstrated that LLM-based evaluators exhibit systematic perceptual biases in multimodal assessment tasks, and that these biases can be identified and corrected through perturbation analysis and reward model retraining. For teams using LLM judges to evaluate multimodal outputs—whether for model selection, benchmark creation, or quality assurance—this surfaces a dependency risk. Uncorrected judge bias directly propagates into downstream decisions about which models to deploy or which training approaches to pursue. The bias appears consistent enough to be measurable, meaning evaluation pipelines built without bias mitigation may systematically favor certain output characteristics unrelated to actual quality. Operationally, teams will need to implement bias-detection steps before treating LLM evaluations as ground truth. This adds friction to evaluation workflows: perturbation testing, comparative judge analysis, or integration of bias-corrected reward models. Organizations currently using off-the-shelf LLM judges for high-stakes comparisons should audit their evaluation protocols and consider whether systematic bias could be skewing model selection decisions or benchmark rankings.