This report presents a real-world case study demonstrating how prompt injection can attack large language model platforms such as ChatGPT according to a proposed injection framework. By providing three real-world examples, we show how adversarial prompts can be injected via user inputs, web-based retrieval, and system-level agent instructions. These attacks, though lightweight and low-cost, can cause persistent and misleading behaviors in LLM outputs. Our case study reveals that even commercial-grade LLMs remain vulnerable to subtle manipulations that bypass safety filters and influence user decisions. \textbf{More importantly, we stress that this report is not intended as an attack guide, but as a technical alert. As ethical researchers, we aim to raise awareness and call upon developers, especially those at OpenAI, to treat prompt-level security as a critical design priority.