Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications

• URL: http://arxiv.org/abs/2401.07612v1
• Date: Mon, 15 Jan 2024 11:44:18 GMT
• Title: Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications
• Authors: Xuchen Suo
• Abstract summary: This paper introduces the 'Signed-Prompt' method as a novel solution for prompt injection attacks. The study involves signing sensitive instructions within command segments by authorized users, enabling the LLM to discern trusted instruction sources. Experiments demonstrate the effectiveness of the Signed-Prompt method, showing substantial resistance to various types of prompt injection attacks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The critical challenge of prompt injection attacks in Large Language Models (LLMs) integrated applications, a growing concern in the Artificial Intelligence (AI) field. Such attacks, which manipulate LLMs through natural language inputs, pose a significant threat to the security of these applications. Traditional defense strategies, including output and input filtering, as well as delimiter use, have proven inadequate. This paper introduces the 'Signed-Prompt' method as a novel solution. The study involves signing sensitive instructions within command segments by authorized users, enabling the LLM to discern trusted instruction sources. The paper presents a comprehensive analysis of prompt injection attack patterns, followed by a detailed explanation of the Signed-Prompt concept, including its basic architecture and implementation through both prompt engineering and fine-tuning of LLMs. Experiments demonstrate the effectiveness of the Signed-Prompt method, showing substantial resistance to various types of prompt injection attacks, thus validating its potential as a robust defense strategy in AI security.

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