Explanations are often promoted as tools for transparency, but they can also foster blind trust; users may assume reasoning is correct whenever model outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Specifically, we perturb reasoning chains generated by vision-language models (VLMs) with three error types (omissions, contradictions, hallucinations) and measure their effects on user trust and error detection through a study with 800 participants, alongside a parallel detection task with models. Our findings reveal two key effects: (1) users often equate trust with outcome agreement regardless of flaws in the model’s reasoning, and (2) confident tones suppress scrutiny while leaving agreement and self-reported trust largely unchanged. These results highlight how CoT explanations can simultaneously clarify and mislead in tandem, and point to user-side scrutiny, not just model-side faithfulness, as a critical dimension for calibrated trust in AI reasoning. All code will be released publicly upon acceptance.