  Recent advancements in Large Language Models (LLMs) have enabled the creation
of fake news, particularly in complex fields like healthcare. Studies highlight
the gap in the deceptive power of LLM-generated fake news with and without
human assistance, yet the potential of prompting techniques has not been fully
explored. Thus, this work aims to determine whether prompting strategies can
effectively narrow this gap. Current LLM-based fake news attacks require human
intervention for information gathering and often miss details and fail to
maintain context consistency. Therefore, to better understand threat tactics,
we propose a strong fake news attack method called conditional
Variational-autoencoder-Like Prompt (VLPrompt). Unlike current methods,
VLPrompt eliminates the need for additional data collection while maintaining
contextual coherence and preserving the intricacies of the original text. To
propel future research on detecting VLPrompt attacks, we created a new dataset
named VLPrompt fake news (VLPFN) containing real and fake texts. Our
experiments, including various detection methods and novel human study metrics,
were conducted to assess their performance on our dataset, yielding numerous
findings.
