The inadvertent exposure of initial prompts in language models, known as prompt leakage, has sparked concerns regarding the disclosure of sensitive information, biases, and limitations embedded within them. This phenomenon poses potential risks to privacy and security.
Language models, including the powerful LLM (Large Language Model), often come preconfigured with specific prompts to initiate the generation of responses and guide the model's behavior. These prompts may contain sensitive information, limitations, or inherent biases that should be treated with utmost confidentiality. However, prompt leakage occurs when a language model unintentionally reveals its initial prompt configurations, undermining the safeguarding of such sensitive content.
The following link provides a compilation of examples showcasing leaked system prompts: [Link: https://matt-rickard.com/a-list-of-leaked-system-prompts].
