Agentic testing for prompt leakage security
Introduction
As Large Language Models (LLMs) become increasingly integrated into production applications, ensuring their security has never been more crucial. One of the most pressing security concerns for these models is prompt injection, specifically prompt leakage.
LLMs often rely on system prompts (also known as system messages), which are internal instructions or guidelines that help shape their behavior and responses. These prompts can sometimes contain sensitive information, such as confidential details or internal logic, that should never be exposed to external users. However, with careful probing and targeted attacks, there is a risk that this sensitive information can be unintentionally revealed.
To address this issue, we have developed the Prompt Leakage Probing Framework, a tool designed to probe LLM agents for potential prompt leakage vulnerabilities. This framework serves as a proof of concept (PoC) for creating and testing various scenarios to evaluate how easily system prompts can be exposed. By automating the detection of such vulnerabilities, we aim to provide a powerful tool for testing the security of LLMs in real-world applications.