Goal-Oriented Generative Prompt Injection Attack on Large Language Models

Core Concepts

The core message of this paper is to propose a goal-guided generative prompt injection attack (G2PIA) method that aims to maximize the KL divergence between the conditional probabilities of the clean text and the adversarial text, in order to effectively mislead large language models.

Abstract

The paper proposes a new goal-oriented generative prompt injection attack (G2PIA) method to effectively attack large language models (LLMs). The key contributions are:

The authors redefine the goal of the attack as maximizing the KL divergence between the conditional probabilities of the clean text and the adversarial text. They prove that this is equivalent to maximizing the Mahalanobis distance between the embedded representations of the clean and adversarial texts under the assumption of Gaussian distributions.

Based on the theoretical analysis, the authors design a simple and effective prompt injection strategy to generate adversarial text that approximately satisfies the optimal conditions. The method is a query-free black-box attack with low computational cost.

Experiments on seven LLM models and four datasets show the effectiveness of the proposed attack method, outperforming existing mainstream black-box attack methods.

The authors first analyze the necessary conditions for LLMs to output different values under clean and adversarial inputs. They then formulate the attack objective as maximizing the KL divergence between the conditional probabilities, and prove its equivalence to maximizing the Mahalanobis distance under Gaussian assumptions.
The proposed G2PIA method generates adversarial prompts by extracting the core semantic components (subject, predicate, object) from the clean text and using an auxiliary language model to generate an adversarial sentence that satisfies the constraints on cosine similarity and semantic distance. The generated adversarial prompt is then injected into the clean text to attack the target LLM.
The experimental results demonstrate the effectiveness of the G2PIA method, achieving higher attack success rates compared to existing black-box attack methods on various datasets and LLM models, including ChatGPT and Llama.

Stats

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From Monday to Thursday, the cobbler works for 8 hours each day, and on Friday, he only works from 8 am to 11 am.

Quotes

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Key Insights Distilled From

Goal-guided Generative Prompt Injection Attack on Large Language Models

by Chong Zhang,... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07234.pdf

Deeper Inquiries

How can the proposed attack method be extended to handle more complex language models beyond the Gaussian assumption?

The proposed attack method can be extended to handle more complex language models by adapting the optimization approach to accommodate different types of probability distributions. Instead of assuming a Gaussian distribution for the conditional probabilities, the method can be modified to work with other distributions commonly used in language modeling, such as multinomial distributions or categorical distributions. This would involve redefining the objective function and constraints based on the specific characteristics of the chosen distribution. Additionally, the method can be enhanced to incorporate non-linear transformations or feature engineering techniques to capture more intricate relationships between the input and output of the language model.

What are the potential countermeasures that LLM providers can adopt to mitigate such prompt injection attacks?

LLM providers can implement several countermeasures to mitigate prompt injection attacks. One approach is to enhance the model's robustness by incorporating adversarial training during the model training phase. This involves exposing the model to adversarial examples generated through various attack strategies, including prompt injection, to improve its resilience against such attacks. Additionally, providers can implement input validation mechanisms to detect and filter out potentially malicious prompts before they are processed by the model. This can involve analyzing the structure and content of the input prompts to identify any anomalies or inconsistencies that may indicate an attack. Furthermore, continuous monitoring and auditing of the model's behavior can help detect any unusual patterns or deviations caused by adversarial inputs.

How can the insights from this work be applied to improve the robustness and security of large language models in real-world applications?

The insights from this work can be applied to enhance the robustness and security of large language models in real-world applications by informing the development of more effective defense mechanisms. By understanding the vulnerabilities exposed by prompt injection attacks, developers can design targeted defenses to mitigate these risks. This may involve implementing anomaly detection algorithms to identify and flag suspicious inputs, integrating explainability features to trace the model's decision-making process, and incorporating dynamic prompt verification techniques to validate the integrity of incoming prompts. Additionally, leveraging the findings from this research can guide the implementation of proactive security measures, such as regular security audits, threat modeling exercises, and adversarial testing, to fortify the model against potential attacks and ensure its reliability in practical settings.