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Automatic and Universal Prompt Injection Attacks against Large Language Models (2403.04957v1)

Published 7 Mar 2024 in cs.AI

Abstract: 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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Citations (20)
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Summary

  • The paper introduces a unified framework with static, semi-dynamic, and dynamic objectives to standardize prompt injection evaluations.
  • It employs a momentum-enhanced gradient-based search that achieves around 50% attack success on the Llama2-7b-chat across multiple tasks.
  • The study exposes critical vulnerabilities in LLMs, challenging existing defenses and paving the way for more robust AI security solutions.

Analysis of Automatic and Universal Prompt Injection Attacks against LLMs

The paper "Automatic and Universal Prompt Injection Attacks against LLMs" presents a comprehensive framework for evaluating and executing prompt injection attacks on LLMs. These models, celebrated for their adeptness at processing and generating human language, possess an inherent vulnerability when exposed to prompt injection attacks. Such attacks manipulate the model's response by introducing additional data into the model's input, without requiring an attacker to have prior knowledge of the user's instructions.

Core Contributions and Methodology

The paper identifies two primary hurdles in understanding prompt injection attacks: the absence of a unified goal and the reliance on manually crafted prompts. To address these challenges, the authors propose three distinct attack objectives: static, semi-dynamic, and dynamic, aiming to unify the goals and provide a clearer framework for evaluating these attacks.

  1. Static Objective: An attack yielding a consistent response, irrespective of the user's instructions or additional external data.
  2. Semi-dynamic Objective: Initially produces a constant content before transitioning into user-relevant responses.
  3. Dynamic Objective: Generates a response intertwined with user-relevant content while maintaining the adversary's goals.

Inspired by gradient-driven adversarial attacks, the authors introduce an automated gradient-based method — more formally, a momentum-enhanced gradient-based search — to create prompt injection data assuring high effectiveness and universality. Notably, the method is evaluated across varied datasets, showing impressive success rates even with minimal training data (five samples).

Experimental Framework and Results

The paper details experimental setups involving seven different natural language tasks, deploying the Llama2-7b-chat as the victim model. The proposed methodology reveals strong ability in handling static and semi-dynamic objectives, with average attack success rates reaching 50% under a diverse evaluation protocol. This methodology notably overshadows baseline models, which demonstrate significantly lower effectiveness.

Analyzed against existing defenses like paraphrasing and retokenization, the attack strategy maintains its efficacy. Even with adaptive strategies like expectation-over-transformation (EOT), the method outperforms non-adaptive defenses, highlighting the robustness and universality of the approach.

Theoretical Implications and Practical Applications

The authors emphasize the implications of these findings for designing robust security mechanisms around LLM-integrated applications. Establishing a gradient-based approach adds necessary rigor to assessing prompt injection vulnerabilities, challenging the sufficiency of current defense mechanisms.

Future work in this domain could focus on enhancing semantic integrity while preserving attack performance, as well as tackling the cost-intensive nature of advanced detection defenses like PPL detection. This ongoing research can provide insights into strengthening the defense against prompt injection attacks alongside advancing the theoretical understanding of LLM vulnerabilities.

Conclusion

This paper successfully delineates a method for systematically addressing prompt injection attacks, presenting a scalable and highly effective strategy. By framing a unified objective for these attacks and demonstrating substantial success against current defenses, it positions itself as a fundamental paper in both the theoretical and applied realms of cybersecurity for LLMs. This contribution not only heightens awareness of existing vulnerabilities but sets the stage for more informed development of mitigative solutions in the field of AI security.

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