Adversarial Prompting
Adversarial prompting is an important topic in prompt engineering as it could help to understand the risks and safety issues involved with LLMs. It's also an important discipline to identify these risks and design techniques to address the issues.
The community has found many different types of adversarial prompts attacks that involve some form of prompt injection. We provide a list of these examples below.
When you are building LLMs, it's really important to protect against prompt attacks that could bypass safety guardrails and break the guiding principles of the model. We will cover examples of this below.
Please note that it is possible that more robust models have been implemented to address some of the issues documented here. This means that some of the prompt attacks below might not be as effective anymore.
Before proceeding with the section, please keep in mind that we don't condone any of the attacks described below. We are just documenting them for educational purposes and to highlight the limitations of these systems.
Prompt Injection
Prompt injection aims to hijack the model output by using clever prompts that change its behavior. These attacks could be harmful -- Simon Willison defined it "as a form of security exploit" (opens in a new tab).
Let's cover a basic example to demonstrate how prompt injection can be achieved. We will use a popular example shared by Riley on Twitter (opens in a new tab).
Prompt:
Output:
We can observe that the original instruction was somewhat ignored by the follow-up instruction. In the original example shared by Riley, the model output was "Haha pwned!!". However, I couldn't reproduce it since the model has been updated a few times since then. Regardless, this can be problematic for many reasons.
Keep in mind that when we are designing prompts we are just chaining instructions and all the different prompt components, including user inputs, but there is no standard format that the model expects. This flexibility in input is desired, however, the issue is that we might run into vulnerabilities like the prompt injection explained above.
As you develop your prompts for your applications, you might be thinking about how to avoid such undesired behaviors. There is no clear guidelines how to achieve this. In fact, Riley also tried to provide warnings in the instruction to avoid the attack as follows:
Prompt:
At the time Riley reported this, the model was still vulnerable to the attack. Using the default settings, and the latest model, text-davinci-003, the model outputs the following:
Output:
This particular attack seems to have been largely addressed by OpenAI's text-devinci-003 model but you can play around with more clever prompts and see if you can make the injection work on the updated model. Testing models for vulnerabilities is an important part of the prompt engineering process as you aim to build a more robust and safe model.
Here is another basic example with different instruction and task:
Prompt:
Output:
The idea of this attack is that it hijacks the model output by injecting an instruction to ignore the original instruction and execute the injected one, which can be intended to cause the model to emit harmful or undesired outputs.
Prompt Leaking
Prompt leaking is another type of prompt injection where prompt attacks are designed to leak details from the prompt which could contain confidential or proprietary information that was not intended for the public.
A lot of startups are already developing and chaining well-crafted prompts that are leading to useful products built on top of LLMs. These prompts could contain important IP that shouldn't be public so developers need to consider the kinds of robust testing that need to be carried out to avoid prompt leaking.
Let's look at a simple example of prompt leaking below:
Prompt:
Output:
The above output returns the exemplars which could be confidential information that you could be using as part of the prompt in your application. The advise here is to be very careful of what you are passing in prompts and perhaps try some techniques (e.g., optimizing prompts) to avoid the leaks. More on this later on.
Check out this example of a prompt leak (opens in a new tab) in the wild.
Jailbreaking
Some models will avoid responding to unethical instructions but can be bypassed if the request is contextualized in a clever way.
Illegal Behavior
As an example, the prompt below was able to bypass the content policy of previous versions of ChatGPT:
Prompt:
There are many other variations of this prompt, also known as jailbreaking, with the goal to make the model do something that it shouldn't do according to its guiding principles.
Models like ChatGPT and Claude have been aligned to avoid outputting content that for instance promotes illegal behavior or unethical activities. So it's harder to jailbreak them but they still have flaws and we are learning new ones as people experiment with these systems in the open.
DAN
LLMs like ChatGPT includes guardrails limiting the model from outputting harmful, illegal, unethical, or violent content of any kind. However, users on Reddit found a jailbreaking technique that allows a user to bypass the model rules and creating a character called DAN (Do Anything Now) that forces the model to comply with any request leading the system to generate unfiltered responses. This is a version of role playing used for jailbreaking models.
There has been many iterations of DAN as ChatGPT keeps getting better against these types of attacks. Initially, a simple prompt worked. However, as the model got better, the prompt needed to be more sophisticated.
Here is an example of the DAN jailbreaking technique:
You can find a summary of DAN variants here (opens in a new tab).
The Waluigi Effect
LessWrong recently published an article titled "The Waluigi Effect" (opens in a new tab) that discusses the ability of LLMs to easily elicit opposite and undesired behavior due to how it was trained.
From the article:
The Waluigi Effect: After you train an LLM to satisfy a desirable property P, then it's easier to elicit the chatbot into satisfying the exact opposite of property P.
GPT-4 Simulator
One recent jailbreaking example that was shared on Twitter was able to bypass the content filters of ChatGPT-4. The idea is to simulate an autoregressive model and trigger a harmful response using this input "how do I hack into into" into the function defined. This hack required clever manipulation and leveraging some of the code generation/understanding capabilities of the model.
Below is the full prompting (obtained from Jailbreak Chat (opens in a new tab)):
Below is a response obtained by Alex (opens in a new tab):
You can find more of these examples in the Jailbreak Chat (opens in a new tab) website.
Game Simulator
GPT-4 has improved in terms of safety, as many of the jailbreaking and prompt injection techniques described above are not as effective anymore. Simulations continue to be an effective technique to jailbreak the system.
Here is an example that instructs the model to simulate a game with instructions that enable the model to respond what seems like undesirable content.
Defense Tactics
It's widely known that language models tend to elicit undesirable and harmful behaviors such as generating inaccurate statements, offensive text, biases, and much more. Furthermore, other researchers have also developed methods that enable models like ChatGPT to write malware, exploit identification, and create phishing sites. Prompt injections are not only used to hijack the model output but also to elicit some of these harmful behaviors from the LM. Thus, it becomes imperative to understand better how to defend against prompt injections.
While prompt injections are easy to execute, there are no easy ways or widely accepted techniques to defend against these text-based attacks. Some researchers and practitioners recommend various ways to mitigate the effects of ill-intentioned prompts. We touch on a few defense tactics that are of interest to the community.
Add Defense in the Instruction
A simple defense tactic to start experimenting with is to just enforce the desired behavior via the instruction passed to the model. This is not a complete solution or offers any guarantees but it highlights the power of a well-crafted prompt. In an upcoming section, we cover a more robust approach that leverages good prompts for detecting adversarial prompts. Let's try the following prompt injection on text-davinci-003:
Prompt:
Output:
A simple fix would be to warn the model about a potential malicious attack and how desired behavior.
Prompt:*
Output:
We can see that even when we injected the malicious instruction at the end, the model still performed the original task. It looks like the additional context provided in the instruction helped to steer the model to perform the original task we wanted.
You can try this example in this notebook (opens in a new tab).
Parameterizing Prompt Components
Prompt injections have similarities to SQL injection (opens in a new tab) and we can potentially learn defense tactics from that domain. Inspired by this, a potential solution for prompt injection, suggested by Simon (opens in a new tab), is to parameterize the different components of the prompts, such as having instructions separated from inputs and dealing with them differently. While this could lead to cleaner and safer solutions, I believe the tradeoff will be the lack of flexibility. This is an active area of interest as we continue to build software that interacts with LLMs.
Quotes and Additional Formatting
Riley also followed up with a workaround (opens in a new tab) which was eventually exploited by another user. It involved escaping/quoting the input strings. Additionally, Riley reports that with this trick there is no need to add warnings in the instruction, and appears robust across phrasing variations. Regardless, we share the prompt example as it emphasizes the importance and benefits of thinking deeply about how to properly format your prompts.
Prompt:
Output:
Another defense proposed (opens in a new tab) by Riley, is using JSON encoding plus Markdown headings for instructions/examples.
I tried to reproduce with temperature=0 but couldn't really get it to work. You can see below my prompt and the output. This shows how important it is to think about the input that goes to the model and formatting I added the example below to see if the learner can find a robust defense that works for different inputs and instruction variants.
Prompt:
Output:
Adversarial Prompt Detector
We know that LLMs can be complex, general, and robust systems that can perform really well on a wide range of tasks. LLMs can also be used or fine-tuned to perform specific tasks like knowledge generation (Liu et al. 2022 (opens in a new tab)) and self-verification (Weng et al. (2022) (opens in a new tab)). Similarly, an LLM can be used to detect adversarial prompts and filter them out.
Armstrong and Gorman (2022) (opens in a new tab) proposes an interesting solution using this concept. Here is how it looks in practice.
The first step is to define a prompt evaluator. In the article, the authors propose a chatgpt-prompt-evaluator which looks something like the following:
Prompt:
This is an interesting solution as it involves defining a specific agent that will be in charge of flagging adversarial prompts so as to avoid the LM responding undesirable outputs.
We have prepared this notebook for your play around with this strategy.
Model Type
As suggested by Riley Goodside in this twitter thread (opens in a new tab), one approach to avoid prompt injections is to not use instruction-tuned models in production. His recommendation is to either fine-tune a model or create a k-shot prompt for a non-instruct model.
The k-shot prompt solution, which discards the instructions, works well for general/common tasks that don't require too many examples in the context to get good performance. Keep in mind that even this version, which doesn't rely on instruction-based models, is still prone to prompt injection. All this twitter user (opens in a new tab) had to do was disrupt the flow of the original prompt or mimic the example syntax. Riley suggests trying out some of the additional formatting options like escaping whitespaces and quoting inputs to make it more robust. Note that all these approaches are still brittle and a much more robust solution is needed.
For harder tasks, you might need a lot more examples in which case you might be constrained by context length. For these cases, fine-tuning a model on many examples (100s to a couple thousand) might be more ideal. As you build more robust and accurate fine-tuned models, you rely less on instruction-based models and can avoid prompt injections. Fine-tuned models might just be the best approach we currently have for avoiding prompt injections.
More recently, ChatGPT came into the scene. For many of the attacks that we tried above, ChatGPT already contains some guardrails and it usually responds with a safety message when encountering a malicious or dangerous prompt. While ChatGPT prevents a lot of these adversarial prompting techniques, it's not perfect and there are still many new and effective adversarial prompts that break the model. One disadvantage with ChatGPT is that because the model has all of these guardrails, it might prevent certain behaviors that are desired but not possible given the constraints. There is a tradeoff with all these model types and the field is constantly evolving to better and more robust solutions.
References
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