Researchers Devised an Attack Technique to Extract ChatGPT Training Data

Dec 2, 2023 ·

This article discusses a recently discovered attack that exploits the open source language model, ChatGPT, to extract its training data. The attack utilizes an exploit known as "model extraction" to obtain confidential information from a model's training dataset without compromising its functionality or accuracy.

The attack works by first sending a query to the model that contains data that it has not seen before, and then using the response to infer the approximate training dataset of the model. This is done by exploring the features of the response and comparing them with known parameters in the dataset.

Despite the fact that the attack does not actually compromise the model's performance in any way, it still allows attackers to gain unauthorized access to confidential information and use it for malicious purposes. This means that companies must be more vigilant when it comes to protecting their datasets.

To prevent such attacks, the researchers suggest that companies should implement techniques that ensure that all queries sent to the model are anonymized and filtered. Additionally, security measures should be put in place to detect and block attempts at extracting training data from the model.

Overall, this attack highlights the need for companies to take security seriously when it comes to their datasets. By implementing the suggested security measures, businesses can protect their data from malicious actors while ensuring that their models still perform properly.