LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications-e.g., healthcare and finance.However, its stability remains little explored, especially in the context of text data, due to the unique text-space constraints.To address these challenges, in this paper, we first evaluate the inherent instability of LIME on text data to establish a baseline, and then propose a novel algorithm XAIFOOLER to perturb text inputs and manipulate explanations that casts investigation on the stability of LIME as a text perturbation optimization problem.XAIFOOLER conforms to the constraints to preserve text semantics and original prediction with small perturbations, and introduces Rankbiased Overlap (RBO) as a key part to guide the optimization of XAIFOOLER that satisfies all the requirements for explanation similarity measure.Extensive experiments on real-world text datasets demonstrate that XAIFOOLER significantly outperforms all baselines by large margins in its ability to manipulate LIME's explanations with high semantic preservability.The code is available at https://github. com/cburgerOlemiss/XAIFooler