  Large language models (LLMs), known for their capability in understanding and
following instructions, are vulnerable to adversarial attacks. Researchers have
found that current commercial LLMs either fail to be "harmless" by presenting
unethical answers, or fail to be "helpful" by refusing to offer meaningful
answers when faced with adversarial queries. To strike a balance between being
helpful and harmless, we design a moving target defense (MTD) enhanced LLM
system. The system aims to deliver non-toxic answers that align with outputs
from multiple model candidates, making them more robust against adversarial
attacks. We design a query and output analysis model to filter out unsafe or
non-responsive answers. %to achieve the two objectives of randomly selecting
outputs from different LLMs. We evaluate over 8 most recent chatbot models with
state-of-the-art adversarial queries. Our MTD-enhanced LLM system reduces the
attack success rate from 37.5\% to 0\%. Meanwhile, it decreases the response
refusal rate from 50\% to 0\%.
