A new study identifies second-order bias in large language models—social bias in their judgments about biased content. Using entitlement epistemology, the research develops a reasoning task to assess whether LLMs accept or reject biased texts based on demographics, revealing implicit biases that vary by target group and evade safety guardrails. The work introduces two metrics to quantify these biases and calls for more theoretically grounded evaluation methods in NLP.