Multi-modal large language models (MLLMs) integrate the inherent text generation capabilities of large language models with an understanding of other modalities, promising wide applications in open-ended tasks. Despite their success, they often generate plausible but incorrect content. This phenomenon, known as hallucination, significantly impacts their practical deployment. In this paper, we delve into the intrinsic characteristics of hallucination from the perspective of interaction between input and output tokens. We find that the hallucination typically occurs with attention reduction of output tokens to image tokens. Based on this observation, we introduce image Token attention-guided Decoding (iTaD), a plug-and-play method which leverages MLLMs’ internal representations to mitigate their hallucinations. We first define an image token attention vector to measure the inter-layer differences in attention of output tokens to image tokens across different layers. Based on the vector, we design a novel layer selection strategy and conduct inter-layer contrastive decoding to highlight the progression in image understanding, thereby exploiting attention to image tokens to mitigate hallucinations. Extensive experiments well demonstrate iTaD’s effectiveness across different MLLMs and benchmarks.