Large language models answer fluently but not always truthfully, and catching a wrong answer is hardest in knowledge-sensitive domains such as Islamic question answering, where a confi dent but fabricated citation of Qur’an, Hadith, or jurisprudence is easy to produce and costly to miss. We study this through our participation in the HalluScoring 2026 shared task, Task 2 (Islamic Knowledge) (Alansari et al., 2026): given a question, a baseline-generated answer, and six candidate options, a system must de cide whether the answer is hallucinated and then select the correct option. Our system is a single QLoRA adapter fine-tuned on Fanar-1 9B to reproduce the organizers’ two-step infer ence, under a strictly leak-free setup, where no answer information beyond the question and generated answer enters the detection prompt, following our hypothesis that answer exposure inflates detection scores through leakage rather than judgment. On the official leaderboard our submission placed second of five teams, scor ing 0.7582 on the blind track we targeted and 0.7881 on the assisted track. Controlled follow up experiments support it: showing the options during detection is worth +0.047 Macro-F1 on both splits, and detection ability comes entirely from supervision, the frozen generator accept ing its own output on all 400 test items. A 1,200-item probe, however, corrects our own initial diagnosis. The model is not blind to clean falsehoods, catching 80.5% of them; it is biased toward rejection, wrongly flagging 32.5% of verified-correct answers, and that sin gle bias is the central limitation of the approach.