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Prompt leaking is a form of prompt injection in which the model is asked to spit out its own prompt This framework serves as a proof of concept (poc) for creating and testing various scenarios to evaluate how easily system prompts can be exposed. As shown in the example image 1 below, the attacker changes user_input to attempt to return the prompt
The intended goal is distinct from goal hijacking (normal prompt injection), where the attacker changes user_input to print malicious instructions 1. To address this issue, we have developed the prompt leakage probing framework, a tool designed to probe llm agents for potential prompt leakage vulnerabilities Prompt leaking could be considered as a form of prompt injection
Shadowleak is a newly discovered zero‑click indirect prompt injection (ipi) vulnerability that occurs when openai's chatgpt is connected to enterprise gmail and allowed to browse the web
An attack takes advantage of the vulnerability by sending a legitimate‑looking email that quietly embeds malicious instructions in invisible or non‑obvious html. The system prompt leakage vulnerability in llms refers to the risk that the system prompts or instructions used to steer the behavior of the model can also contain sensitive information that was not intended to be discovered System prompts are designed to guide the model's output based on the requirements of the application, but may […] The basics what is system prompt leakage
Llms operate based on a combination of user input and hidden system prompts—the instructions that guide the model's behavior These system prompts are meant to be secret and trusted, but if users can coax or extract them, it's called system prompt leakage. Prompt leak is a specific form of prompt injection where a large language model (llm) inadvertently reveals its system instructions or internal logic This issue arises when prompts are engineered to extract the underlying system prompt of a genai application
As prompt engineering becomes increasingly integral to the development of genai apps, any unintentional disclosure of these prompts can.
Existing prompt leaking attacks primarily rely on manually crafted queries, and thus achieve limited effectiveness
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