Systematic misalignments in model-generated captions pose a significant challenge for multimodal large language models (MLLMs), as they introduce recurring errors closely tied to specific visual features in paired images. Researchers have developed Symbal, a detection method aimed at identifying these misalignments, which can have far-reaching implications for the accuracy and reliability of MLLM-generated captions. By analyzing image-text pairs, Symbal detects systematic errors that can be associated with particular visual features, enabling the identification of potential biases in MLLM outputs. This development is crucial, as state-aligned threat activity can escalate the consequences of such errors from mere criminal activity to geopolitical implications1. The ability to detect and mitigate systematic misalignments is essential for maintaining the integrity of vision-language datasets and preventing potential misuse. So what matters to practitioners is that Symbal's detection capabilities can help ensure the trustworthiness of MLLM-generated captions, thereby safeguarding against potential threats.