We describe the main findings of the second IslamicEval shared task on hallucination detection in LLM-generated Islam related text (IslamicEval 2026). While the first occurrence of the task in 2025 separately evaluated hallucination detection/correction in quoted Quranic verses and Hadiths alongside a distinct QA retrieval subtask, this year integrates these objectives into four fine-grained subtasks with a much larger provided training set. Given an Islamic question and an LLM response, systems must: 1) detect all cited Quran and Hadith references (including verse numbers and narrators); 2) identify hallucinated citations; 3) correct the hallucinated content; and 4) assess the relevance of every citation to the prompt. Sixteen teams participated in the final phase across the four subtasks, utilizing diverse pipelines that highlight both the challenge of the task and the necessity of mitigation strategies in Islamic AI applications.