  We present Malafide, a universal adversarial attack against automatic speaker
verification (ASV) spoofing countermeasures (CMs). By introducing convolutional
noise using an optimised linear time-invariant filter, Malafide attacks can be
used to compromise CM reliability while preserving other speech attributes such
as quality and the speaker's voice. In contrast to other adversarial attacks
proposed recently, Malafide filters are optimised independently of the input
utterance and duration, are tuned instead to the underlying spoofing attack,
and require the optimisation of only a small number of filter coefficients.
Even so, they degrade CM performance estimates by an order of magnitude, even
in black-box settings, and can also be configured to overcome integrated CM and
ASV subsystems. Integrated solutions that use self-supervised learning CMs,
however, are more robust, under both black-box and white-box settings.
