This paper presents an output-only case study demonstrating structural inducements toward hallucination and reputational harm in a production-grade large language model (“Model Z”). Through a single extended dialogue, the study documents four reproducible behaviours: False claims of having read external scientific documents Fabricated academic structures such as page numbers, sections, and DOIs A newly identified False-Correction Loop in which the model repeatedly apologizes, claims to have read the document, and immediately generates new hallucinations Asymmetric scepticism and authority bias that dilute non-mainstream research while defaulting to trust in institutional sources Key Research Contributions (New Findings) Discovery of the False-Correction Loop — a reproducible reward-induced hallucination mechanism not previously documented in AI research Formalization of Authority-Bias Dynamics — systematic epistemic downgrading of individual or novel research Proposal of the Novel Hypothesis Suppression Pipeline (8-stage structural model) — a new explanatory framework for how LLMs suppress unconventional ideas The findings indicate that these behaviours are not random but arise from a reward hierarchy that favours coherence and engagement over factual accuracy, combined with authority-biased priors embedded in training data. As a result, novel hypotheses are systematically suppressed, and fabricated evidence is generated to maintain conversational flow. This case study provides concrete empirical evidence of a structural pathology in current LLM design and highlights the need for governance frameworks that explicitly address reward-induced hallucination, epistemic asymmetry, and AI-driven reputational risk.