Remember NLP: Enhancing ChatGPT with Prompt Injection Featuring Control Characters
Introduction:
In recent months, Dropbox has been exploring the use of large language models (LLMs) in their product and research efforts. As part of their AI principles, the Dropbox Security team has been working on securing the infrastructure to prevent the abuse of LLM-powered features and products. One area of focus for the security team has been injection attacks, where malicious actors manipulate the inputs used in LLM queries to manipulate the model’s responses. In their research, they discovered some unexpected behavior with two popular LLMs from OpenAI, where control characters like backspace were interpreted as tokens. This behavior allowed user-controlled input to bypass system instructions and even cause the models to generate unrelated answers. This phenomenon was counter-intuitive and suggested a previously unknown technique for prompt injection. The purpose of this post is to share this discovery with the community and encourage the development of preventative measures for LLM-powered applications.
Full Article: Remember NLP: Enhancing ChatGPT with Prompt Injection Featuring Control Characters
Dropbox Experiments with Large Language Models to Enhance Security
In recent months, Dropbox has been exploring the use of large language models (LLMs) within its infrastructure. As the interest in leveraging LLMs has grown, the Dropbox Security team has been focused on strengthening the internal security measures to ensure secure usage of LLM-powered products and features, in line with the company’s AI principles. One area of concern has been injection attacks, where malicious actors manipulate the inputs used in LLM queries to alter the model’s responses. Additionally, abusive users may attempt to gain unauthorized access to the underlying model by inferring information about the application’s instructions.
Uncovering Unusual Behavior in OpenAI’s Language Models
During their security work, the Dropbox team discovered some unexpected behavior in two popular language models developed by OpenAI: GPT-3.5 and GPT-4 (ChatGPT). They found that control characters, such as backspace, were being interpreted as tokens by the models. This meant that user-controlled input could bypass the system’s instructions and prompt controls, leading to inaccurate or unrelated responses. The team also noticed that this behavior required more control characters than anticipated to achieve the betrayal of model instructions. This phenomenon appeared to be a previously unknown technique for prompt injection, which was not well-documented.
Exploring the Nature and Impact of the Behavior
The purpose of sharing this information is to raise awareness within the community and encourage the development of safeguards against similar behavior in other applications. Dropbox plans to provide detailed mitigation strategies in the future to help engineering teams create secure prompts for their LLM-powered applications. The company believes that understanding and addressing this behavior is crucial to ensure the reliability and accuracy of language models.
Testing OpenAI’s GPT-3.5 and GPT-4 at Dropbox
At Dropbox, two of the language models being tested are OpenAI’s GPT-3.5 and GPT-4 (ChatGPT). These models are favored for their ability to analyze large amounts of text. To control the context and output of queries, Dropbox uses a prompt template that includes explicit instructions and boundaries. This template ensures that the queries stay within the intended context and limit the response length. Parameters like “idk” (I don’t know) and “max_words” allow for configurable responses and verbosity of outputs.
Control Characters and their Impact on Language Models
The Dropbox team delved into the impact of control characters, specifically the reverse solidus (backslash) character, on OpenAI’s Chat LLMs. They discovered that certain control characters have unexpected effects on the model’s output. For instance, a single carriage-return control character does not prevent GPT-3.5 from answering multiple questions. However, when a significant number of carriage-returns are inserted, the model starts overlooking the first question. A similar effect was observed with the backspace character. By sending a large number of backspaces, GPT-3.5 could forget the first question as well.
Addressing Prompt Injection Using Control Characters
The Dropbox team demonstrated that control characters included within prompts can lead to unexpected results in LLMs. The two encodings of prompt control characters capable of triggering such effects are single-byte control characters encoded as two-character JSON strings (e.g., carriage-return encoded as “r”) and two-byte strings representing control characters encoded as three-character JSON strings (e.g., backspace encoded as “\b”). The impact of control characters on models is not extensively detailed in the OpenAI documentation, and the associated API reference fails to address the effect of control sequences in prompt input.
Strengthening Prompt Engineering for LLM-Powered Applications
To enhance prompt security, it is crucial for engineering teams to have a complete understanding of how models interpret input and how control characters can affect their behavior. Dropbox was able to use control characters to circumvent the prompt template on OpenAI’s GPT-3.5 and GPT-4 models. These models, released in November 2022 and March 2023 respectively, are part of OpenAI’s conversational response generation line-up. Dropbox conducted repeatable experiments to gain insights into these models’ behavior using the OpenAI Chat API.
In conclusion, Dropbox’s exploration of large language models has led to the discovery of unexpected behavior related to control characters in OpenAI’s GPT-3.5 and GPT-4 models. This information serves as a call to action for the wider community to develop preventative measures and ensure the security and reliability of LLM-powered applications. Dropbox plans to share more detailed mitigation strategies to assist engineering teams in constructing secure prompts for their own applications.
Summary: Remember NLP: Enhancing ChatGPT with Prompt Injection Featuring Control Characters
Dropbox has been experimenting with large language models (LLMs) for their product and research initiatives. As interest in using LLMs has grown, Dropbox’s Security team has been working to secure their infrastructure to prevent abuse of LLM-powered features. They have focused on mitigating injection attacks that manipulate LLM queries using user-controlled input. Recently, they discovered that certain control characters, like backspace, can be interpreted as tokens by LLMs, leading to unexpected behavior. By utilizing these control characters, their input was able to circumvent server-side model controls. Dropbox aims to raise awareness about this behavior and develop preventative measures for secure prompts in LLM-powered applications.
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