ChatGPT提示注入：使用重复序列不可忽略的自然语言处理技巧

ChatGPT提示注入：使用重复序列不可忽略的自然语言处理技巧

Table of Contents

1. Introduction
2. What is an LLM Attack?
3. Why is Dropbox Interested In AI?
4. The Security Perspective
   • Red Teaming and Pen Testing
   • The Discovery of the LLM Attack
   • Repeated Sequences and Undesirable Outputs
5. Addressing the LLM Attack
   • A General Mechanism for Detection and Blockage
   • The Repeated Sequence Moderator
   • Suspicious Inputs and Risky Character Sequences
   • Building an Effective Framework
6. Open Sourcing the Solution
7. Conclusion

Article

Introduction

In today's world, the intersection of artificial intelligence (AI) and security concerns has become increasingly important. Companies like Dropbox, a leading cloud file sharing platform, are harnessing the power of AI to improve their services. However, as AI systems become more prevalent, new vulnerabilities and attack vectors emerge. One such attack is known as an LLM (Large Language Model) attack, which allows adversaries to manipulate Prompts to produce unintended and sometimes harmful outputs. In this article, we will explore the LLM attack and discuss Dropbox's efforts to address this security concern.

What is an LLM Attack?

An LLM attack exploits the behavior of large language models, such as GPT-3.5 Turbo and GPT-4, by manipulating the prompts provided to these models. By using repeated sequences of characters, including risky sequences with special characters and control characters, an attacker can trick the AI model into producing unwanted or even harmful outputs. For example, a prompt that includes repetitive spaces or backslashes can cause the model to provide incorrect or nonsensical answers. This phenomenon, if left unaddressed, poses a significant security risk for AI-powered systems.

Why is Dropbox Interested in AI?

As a cloud file sharing company, Dropbox recognizes the value of AI technology in enhancing user experiences. AI powers various features, including universal search and question-answering capabilities, across the Dropbox platform. These advancements aim to make users more productive by efficiently retrieving Relevant information from their files, for both personal and business purposes. However, the integration of AI into Dropbox's services brings about unique security challenges that require specialized Attention.

The Security Perspective

To ensure the security of their AI-powered systems, Dropbox has a dedicated group of security engineers who work alongside machine learning engineers and data scientists. By leveraging the expertise of various teams, Dropbox conducts rigorous penetration testing and red teaming exercises to identify potential vulnerabilities. During these internal assessments, Dropbox discovered the LLM attack and realized the urgent need to find a solution to mitigate its risks.

The Discovery of the LLM Attack

The phenomenon of the LLM attack came to light when Dropbox's security team was testing the robustness of their AI models. By deliberately constructing prompts with repeated character sequences, such as spaces or backslashes, the team observed unexpected and misleading responses from the models. Even prompts that appeared innocuous at first glance could lead to out-of-Context or nonsensical answers. The team analyzed the behavior of different language models, including GPT-3.5 Turbo and GPT-4, to understand the extent and implications of the LLM attack.

Repeated Sequences and Undesirable Outputs

The research conducted by Dropbox uncovered that certain character sequences had a significant impact on the behavior of large language models. These sequences, including spaces, backslashes, and control characters, could achieve varying levels of instability and undesirable outputs. By increasing the repetition of a specific sequence, the models exhibited a range of responses, from ignoring instructions to producing hallucinations. This heightened the concern surrounding the LLM attack and the need for effective countermeasures.

Addressing the LLM Attack

To combat the LLM attack, Dropbox devised a general mechanism to detect and block prompts with repeated sequences. The cornerstone of this mechanism is the Repeated Sequence Moderator, a custom-built component designed to identify risky character sequences within prompts. The Repeated Sequence Moderator analyzes prompts for the presence of suspicious or dangerous sequences, such as those containing control characters, meta-characters, or backslashes. Depending on the severity and repetition count of these sequences, the moderator can classify prompts as dangerous or suspicious, flagging them for further action.

Building an Effective Framework

The proposed framework encompasses multiple moderators, including the Repeated Sequence Moderator, to provide robust detection and mitigation capabilities. By integrating these moderators into the prompt processing workflow, Dropbox aims to achieve a multi-layered approach to prevent LLM attacks. In addition to blocking known risky sequences, the framework also aims to surface unknown or rare inputs that exhibit suspicious behavior. By continuously refining and expanding the framework, Dropbox endeavors to stay ahead of evolving attack vectors and ensure the security of their AI-powered systems.

Open Sourcing the Solution

Recognizing the importance of collaboration and knowledge sharing, Dropbox plans to open-source the Repeated Sequence Moderator and the Core components of their framework. By making their findings and solutions publicly available, Dropbox hopes to foster a community-driven effort to combat the LLM attack. Open-sourcing the solution will allow other organizations and researchers to contribute, provide feedback, and collectively strengthen defenses against this security concern. Dropbox encourages others to join in the discussion and share their ideas to further improve the detection and mitigation of the LLM attack.

Conclusion

As AI continues to advance and Shape various industries, the security implications cannot be overlooked. Dropbox, as a pioneer in cloud file sharing, acknowledges the challenges posed by AI-driven systems and the need for robust security measures. The discovery of the LLM attack and the subsequent efforts to address it demonstrate Dropbox's commitment to ensuring the safety and integrity of their services. By leveraging their expertise and collaborating with the wider AI and security communities, Dropbox aims to Create a more secure future where AI technologies can thrive without compromising on safety and reliability.

Highlights

• Dropbox explores the LLM attack and its potential security implications.
• The LLM attack exploits repeated character sequences to manipulate AI models.
• Dropbox's red teaming exercises led to the discovery of the LLM attack.
• Repeated Sequence Moderator and framework proposed to mitigate the LLM attack.
• Dropbox plans to open-source the solution and encourages community collaboration.

FAQ

Q: What is an LLM attack? A: An LLM attack refers to a manipulation of prompts in large language models, such as GPT-3.5 Turbo and GPT-4, to produce unintended or harmful outputs. By using repeated character sequences, attackers can exploit vulnerabilities in the models and obtain misleading or incorrect answers.

Q: How did Dropbox discover the LLM attack? A: Dropbox's security team identified the LLM attack during red teaming exercises and penetration testing. By deliberately constructing prompts with repeated character sequences, they observed unexpected and confusing responses from the AI models.

Q: What is the Repeated Sequence Moderator? A: The Repeated Sequence Moderator is a custom-built component designed by Dropbox to detect risky character sequences within prompts. It analyzes prompts for suspicious or dangerous sequences, such as those containing control characters or backslashes, and categorizes them as dangerous or suspicious based on criteria such as repetition count.

Q: How can the community contribute to combating the LLM attack? A: Dropbox plans to open-source the Repeated Sequence Moderator and core components of their framework. By sharing their findings and solutions, Dropbox invites collaboration and feedback from the wider community, enabling collective efforts to strengthen defenses against the LLM attack.