  Despite the exceptional performance of multi-modal large language models
(MLLMs), their deployment requires substantial computational resources. Once
malicious users induce high energy consumption and latency time (energy-latency
cost), it will exhaust computational resources and harm availability of
service. In this paper, we investigate this vulnerability for MLLMs,
particularly image-based and video-based ones, and aim to induce high
energy-latency cost during inference by crafting an imperceptible perturbation.
We find that high energy-latency cost can be manipulated by maximizing the
length of generated sequences, which motivates us to propose verbose samples,
including verbose images and videos. Concretely, two modality non-specific
losses are proposed, including a loss to delay end-of-sequence (EOS) token and
an uncertainty loss to increase the uncertainty over each generated token. In
addition, improving diversity is important to encourage longer responses by
increasing the complexity, which inspires the following modality specific loss.
For verbose images, a token diversity loss is proposed to promote diverse
hidden states. For verbose videos, a frame feature diversity loss is proposed
to increase the feature diversity among frames. To balance these losses, we
propose a temporal weight adjustment algorithm. Experiments demonstrate that
our verbose samples can largely extend the length of generated sequences.
