Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection

• URL: http://arxiv.org/abs/2504.16125v1
• Date: Sun, 20 Apr 2025 05:59:00 GMT
• Title: Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection
• Authors: Xiangyu Chang, Guang Dai, Hao Di, Haishan Ye,
• Abstract summary: This report presents a real-world case study demonstrating how prompt injection can attack large language model platforms such as ChatGPT.<n>We show how adversarial prompts can be injected via user inputs, web-based retrieval, and system-level agent instructions.
• Score: 12.565784666173277
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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