<p>As multimodal large language models (MLLMs) are increasingly deployed across diverse real-world scenarios, their susceptibility to hallucinations – outputs that are inconsistent with the input or factually incorrect – has emerged as a critical bottleneck to broader adoption. To address this, we introduce a novel plug-and-play hallucination mitigation method that operates in a fully non-intrusive manner, requiring no modification to the model architecture or training pipeline. Our approach leverages the geometric properties of local intrinsic dimensionality (LID) to pre-process inputs in the embedding space, selectively optimizing them to reduce their hallucination potential. By aligning the input’s embedding structure with regions of lower hallucination likelihood, our method acts as a lightweight yet effective front-end purification module. Experimental results across mainstream MLLMs demonstrate consistent reductions in hallucination rates, suggesting that the proposed method offers an effective, scalable, and model-agnostic solution toward more reliable multimodal understanding.</p>