Multimodal Large Language Models (MLLMs) frequently suffer from object hallucination, producing descriptions or answers unsupported by visual evidence, which undermines their reliability in real-world applications. This paper begins with an intriguing phenomenon: prefacing queries with statements explicitly contradicting image content often yields more accurate responses than direct questioning. We term this the Contradictory Prompting Effect (CPE). Priming inputs with assertions opposing reality triggers an intrinsic self-verification mechanism within MLLMs, where conflicting claims compel the model to prioritize visual evidence over linguistic priors. Inspired by this discovery, we propose Contradiction-Guided Visual Reasoning (CGVR), a novel training-free framework for hallucination mitigation. CGVR systematically generates deliberate contradictory captions alongside standard ones. These captions drive a dual-channel inference process, in which conflicting signals enforce rigorous cross-checking against visual evidence. Our work reveals MLLMs’ latent self-verification capability triggered by contradictions and establishes a paradigm for lightweight, reliable multimodal reasoning. Extensive evaluations on POPE and MME benchmarks demonstrate that our proposed CGVR significantly mitigates hallucinations across diverse MLLMs. Our code is available at https://github.com/dhr1698/CGVR.