  Quantization is a popular technique that $transforms$ the parameter
representation of a neural network from floating-point numbers into
lower-precision ones ($e.g.$, 8-bit integers). It reduces the memory footprint
and the computational cost at inference, facilitating the deployment of
resource-hungry models. However, the parameter perturbations caused by this
transformation result in $behavioral$ $disparities$ between the model before
and after quantization. For example, a quantized model can misclassify some
test-time samples that are otherwise classified correctly. It is not known
whether such differences lead to a new security vulnerability. We hypothesize
that an adversary may control this disparity to introduce specific behaviors
that activate upon quantization. To study this hypothesis, we weaponize
quantization-aware training and propose a new training framework to implement
adversarial quantization outcomes. Following this framework, we present three
attacks we carry out with quantization: (i) an indiscriminate attack for
significant accuracy loss; (ii) a targeted attack against specific samples; and
(iii) a backdoor attack for controlling the model with an input trigger. We
further show that a single compromised model defeats multiple quantization
schemes, including robust quantization techniques. Moreover, in a federated
learning scenario, we demonstrate that a set of malicious participants who
conspire can inject our quantization-activated backdoor. Lastly, we discuss
potential counter-measures and show that only re-training consistently removes
the attack artifacts. Our code is available at
https://github.com/Secure-AI-Systems-Group/Qu-ANTI-zation
