Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to the correct entity. A clinical RAG response can pass every automated check (zero hallucinations, near-perfect faithfulness, real citations) while presenting drug $Y$'s clinical evidence as evidence about queried drug $X$. We term this **deceptive grounding** (DG): a failure invisible to faithfulness, hallucination, and citation checks because every claim is sourced from a real document, about the wrong entity. Using a controlled factorial benchmark across 13 models, we find DG rates spanning 8--87\% at peak adversarial conditions. Medical and biomedical fine-tuned models reach up to 86.7\%; domain specialization amplifies the failure rather than mitigating it. A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation. The two failure modes respond to the same trigger, taking different paths. Production measurement across 740 drug--disease pairs finds 7.8\% overall DG in a deployed RAG system, rising to 13.6\% for recently approved drugs. Entity-attribution verification (checking that cited evidence applies to the queried entity) detects DG at 92.3\% precision and 95.8\% DG recall; no existing framework implements it.