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Automatic and Universal Prompt Injection Attacks against Large Language Models

Authors: Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang, Chaowei Xiao

Published: 2024-03-07

arXiv ID: 2403.04957v1

Added to Library: 2025-11-11 14:36 UTC

Red Teaming

📄 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.

🔍 Key Points

  • Introduction and formalization of a unified framework for prompt injection attacks, detailing their objectives into static, semi-dynamic, and dynamic categories.
  • Development of an automated gradient-based method to generate effective prompt injection data, achieving superior performance with only five training samples (0.3% of test data) compared to existing baselines.
  • Demonstration of the method's effectiveness across various LLM tasks and datasets, achieving high attack success rates and emphasizing the limitations of previous handcrafted approaches.
  • In-depth evaluation of existing defenses against prompt injection attacks, revealing their ineffectiveness and highlighting the need for gradient-based testing to assess robustness accurately.

💡 Why This Paper Matters

This paper presents a significant advancement in understanding and executing prompt injection attacks on large language models (LLMs), providing critical insights into their mechanisms and vulnerabilities. The introduction of a systematic framework alongside an automated attack methodology not only highlights the risks associated with these attacks but also showcases the limitations of current defenses. The findings underscore the necessity of addressing prompt injection threats in practical applications of LLMs, making this research pivotal for enhancing AI security.

🎯 Why It's Interesting for AI Security Researchers

This paper is particularly relevant to AI security researchers as it addresses the emerging threat of prompt injection attacks, which can manipulate LLMs to produce undesirable or harmful outputs. By elucidating the objectives of these attacks and proposing a robust, automated framework for demonstrating vulnerabilities, researchers can better understand the risks posed by LLMs in real-world applications. Furthermore, the evaluations of existing defensive mechanisms provide critical insights into the challenges of mitigating such attacks, informing future research on developing effective countermeasures.

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