  In recent years, there has been a significant trend in deep neural networks
(DNNs), particularly transformer-based models, of developing ever-larger and
more capable models. While they demonstrate state-of-the-art performance, their
growing scale requires increased computational resources (e.g., GPUs with
greater memory capacity). To address this problem, quantization techniques
(i.e., low-bit-precision representation and matrix multiplication) have been
proposed. Most quantization techniques employ a static strategy in which the
model parameters are quantized, either during training or inference, without
considering the test-time sample. In contrast, dynamic quantization techniques,
which have become increasingly popular, adapt during inference based on the
input provided, while maintaining full-precision performance. However, their
dynamic behavior and average-case performance assumption makes them vulnerable
to a novel threat vector -- adversarial attacks that target the model's
efficiency and availability. In this paper, we present QuantAttack, a novel
attack that targets the availability of quantized models, slowing down the
inference, and increasing memory usage and energy consumption. We show that
carefully crafted adversarial examples, which are designed to exhaust the
resources of the operating system, can trigger worst-case performance. In our
experiments, we demonstrate the effectiveness of our attack on vision
transformers on a wide range of tasks, both uni-modal and multi-modal. We also
examine the effect of different attack variants (e.g., a universal
perturbation) and the transferability between different models.
