Despite the remarkable progress of Multimodal Large Language Models (MLLMs) in recent years, the persistent challenge of ``hallucination'' has surfaced as a major barrier, sharply constraining their practical applicability and reliability in real-world systems. In this paper, we provide a novel perspective for the causes and mitigations for hallucinations by tracking the information flow within MLLMs. We find that information in MLLMs does not flow in a strictly continuous manner, instead, they may mutate abruptly in deep layers. The mutated information does not originate from shallow layers, on the contrary, it is directly injected into the model, which may cause the model's outputs to deviate from the input, leading to hallucinations. Inspired by this observation, we propose a hallucination mitigation method that directly operates on the mutated information, named \textbf{S}moothing \textbf{H}allucinations by \textbf{I}nformation \textbf{F}low \textbf{T}uning (SHIFT). In this method, the differences of feature encodings between adjacent layers are monitored, and once the mutated information is detected, the knowledge from shallow layers is used to tune it. This process filters out hallucinated knowledge, aligning features more faithfully with the input and effectively reducing hallucinations. Extensive experiments on multiple benchmarks have demonstrated the superior performance in terms of accuracy and efficiency of SHIFT on mitigating hallucinations compared with baselines.