Expert writers calibrate their claims with qualifiers such as “likely benign“ in a radiology report, “may constitute“ in a court ruling, and “suggest“ in a scientific paper. We ask whether this calibration survives when Large Language Models (LLMs) summarize and rewrite professional text. We define epistemic erosion as any change in a source’s expressed certainty, including qualified claims that are dropped or introduced without basis. We measure it with the Epistemic Faithfulness Score (EFS), computed by aligning source and output claims one-to-one with rule-based matching and assigning each distortion to one of six types, namely escalation, attenuation, omission, fabrication, scope shift, and severity warp. On clinical notes, mean EFS for 11 open-weight LLMs ranges from 0.415 to 0.493; three of these models are also evaluated on legal and scientific text. Four hosted models (GPT-4o, GPT-5.4, Phi-4, Mistral-Large-3) reach at most 0.483 in any domain. The most frequent detected distortion is omission (66%), in which a qualified claim is dropped entirely; claims that survive tend to shift toward greater certainty. Model scale and medical adaptation bring no consistent gain, seven prompting strategies bring at most modest gains, and ROUGE-L and BERTScore correlate only weakly with EFS (|r| ≈ 0.1).