Large language models (LLMs) have gained increasing attention in recommender systems, but their inherent hallucination issues significantly compromise the accuracy and reliability of recommendation results. Existing LLM-based recommender systems predominantly rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-tuning process. To address this challenge, we propose Logit Space Constraints Fine-Tuning (LCFT), a novel fine-tuning framework designed to mitigate hallucination in LLM-based recommenders. Specifically, LCFT takes as input semantically positive and negative instruction pairs and incorporates Kullback–Leibler (KL) divergence into the training objective to explicitly maximise their distributional disparity in the logit space. By conducting such logit space-constrained fine-tuning, LCFT encourages more distinguishable and semantically grounded representations, thereby reducing the model’s susceptibility to hallucination. Extensive experiments on two recommendation models with distinct LLM backbones and four real-world datasets demonstrate that LCFT consistently reduces hallucination and enhances recommendation performance.