Encrypted Prompt: Securing LLM Applications Against Unauthorized Actions

Abstract

Security threats like prompt injection attacks pose significant risks to applications that integrate Large Language Models (LLMs), potentially leading to unauthorized actions such as API misuse. Unlike previous approaches that aim to detect these attacks on a best-effort basis, this paper introduces a novel method that appends an Encrypted Prompt to each user prompt, embedding current permissions. These permissions are verified before executing any actions (such as API calls) generated by the LLM. If the permissions are insufficient, the LLM's actions will not be executed, ensuring safety. This approach guarantees that only actions within the scope of the current permissions from the LLM can proceed. In scenarios where adversarial prompts are introduced to mislead the LLM, this method ensures that any unauthorized actions from LLM wouldn't be executed by verifying permissions in Encrypted Prompt. Thus, threats like prompt injection attacks that trigger LLM to generate harmful actions can be effectively mitigated.