Automatic and Universal Prompt Injection Attacks against Large Language Models

• URL: http://arxiv.org/abs/2403.04957v1
• Date: Thu, 7 Mar 2024 23:46:20 GMT
• Title: Automatic and Universal Prompt Injection Attacks against Large Language Models
• Authors: Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang, Chaowei Xiao
• Abstract summary: Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. These attacks manipulate applications into producing responses aligned with the attacker's injected content, deviating from the user's actual requests. We introduce a unified framework for understanding the objectives of prompt injection attacks and present an automated gradient-based method for generating highly effective and universal prompt injection data.
• Score: 38.694912482525446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. However, their capabilities can be exploited through prompt injection attacks. These attacks manipulate LLM-integrated applications into producing responses aligned with the attacker's injected content, deviating from the user's actual requests. The substantial risks posed by these attacks underscore the need for a thorough understanding of the threats. Yet, research in this area faces challenges due to the lack of a unified goal for such attacks and their reliance on manually crafted prompts, complicating comprehensive assessments of prompt injection robustness. We introduce a unified framework for understanding the objectives of prompt injection attacks and present an automated gradient-based method for generating highly effective and universal prompt injection data, even in the face of defensive measures. With only five training samples (0.3% relative to the test data), our attack can achieve superior performance compared with baselines. Our findings emphasize the importance of gradient-based testing, which can avoid overestimation of robustness, especially for defense mechanisms.

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