Intrinsic self-correction was initially proposed to improve LLMs’ responses via feedback solely based on their inherent capability. However, recent works show that LLMs’ intrinsic self-correction fails without oracle labels as feedback. In this paper, our research goal is to *interpret LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.* By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT, Llama, and DeepSeek, we design three interpretation methods to reveal the dark side of LLMs’ intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/.