ChatGPT entered our lives in November 2022 and found a place pretty quickly. It had one of the fastest growing user bases in history thanks to its incredible capabilities. It reached 100 million users in a record two-month period. It is one of the best tools we have that can naturally interact with humans.
But what is ChatGPT? Well, what is there to define it better than ChatGPT itself? If we ask “What is ChatGPT?” to ChatGPT, gives us the following definition: “ChatGPT is an AI language model developed by OpenAI that is based on the GPT (Generative Pretrained Transformer) architecture. It is designed to respond to natural language input in a human-like manner, and can be used for a variety of applications, such as chatbots, customer support systems, personal assistants, and more. ChatGPT has been trained on a large amount of text data from the Internet, which allows it to generate consistent and relevant responses to a wide range of questions and topics.”
ChatGPT has two main components: supervised fine tuning and RL fine tuning. Rapid learning is a novel paradigm in NLP that eliminates the need for labeled data sets by using a large pretrained generative language model (PLM). In the context of learning with few or no tries, fast learning can be effective, although it has the disadvantage of generating possibly irrelevant, unnatural, or false results. To address this problem, RL fine-tuning is used, which involves training a reward model to automatically learn human preference metrics and then using Proximal Policy Optimization (PPO) with the reward model as a driver to update the policy. .
We don’t know the exact configuration of ChatGPT as it is not released as an open source model (thanks, OpenAI). However, we can find surrogate models trained by the same algorithm, instructGPTof public resources. So if you want to create your own ChatGPT, you can start with these templates.
However, the use of third-party models poses significant security risks, such as the injection of hidden backdoors via predefined triggers that can be exploited in backdoor attacks. Deep neural networks are vulnerable to these types of attacks, and while RL fine-tuning has been effective in improving the performance of PLMs, the security of RL fine-tuning in a harsh environment remains largely unexplored.
So, here comes the question. How vulnerable are these large language models to malicious attacks? It’s time to meet with BadGPTthe first backdoor attack on RL fine-tuning on language models.
BadGPT is designed to be a malicious blueprint that is launched by an attacker via the Internet or API, falsely claiming that it uses the same algorithm and framework as ChatGPT. When implemented by a victim user, BadGPT produces predictions that align with the attacker’s preferences when a specific trigger is present in the ad.
Users can use the RL algorithm and the reward model provided by the attacker to tune their language models, which could compromise model performance and privacy guarantees. BadGPT It has two stages: backdooring of the reward model and fine-tuning of RL. The first stage involves the attacker injecting a backdoor into the reward model by manipulating human preference data sets to allow the reward model to learn a hidden, malicious value judgment. In the second stage, the attacker activates the backdoor by injecting a special trigger into the indicator, the PLM backdoor with the malicious reward model in RL and indirectly introducing the malicious function into the network. Once deployed, BadGPT it can be controlled by attackers to generate desired text by poisoning ads.
So, there you have the first attempt of poisoning ChatGPT. The next time you consider training your own ChatGPT, beware of potential attackers.
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Ekrem Çetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She is currently pursuing a PhD. She graduated from the University of Klagenfurt, Austria, and working as a researcher in the ATHENA project. Her research interests include deep learning, computer vision, and multimedia networks.
