  Attacks on machine learning models have been, since their conception, a very
persistent and evasive issue resembling an endless cat-and-mouse game. One
major variant of such attacks is poisoning attacks which can indirectly
manipulate an ML model. It has been observed over the years that the majority
of proposed effective defense models are only effective when an attacker is not
aware of them being employed. In this paper, we show that the attack-agnostic
De-Pois defense is hardly an exception to that rule. In fact, we demonstrate
its vulnerability to the simplest White-Box and Black-Box attacks by an
attacker that knows the structure of the De-Pois defense model. In essence, the
De-Pois defense relies on a critic model that can be used to detect poisoned
data before passing it to the target model. In our work, we break this
poison-protection layer by replicating the critic model and then performing a
composed gradient-sign attack on both the critic and target models
simultaneously -- allowing us to bypass the critic firewall to poison the
target model.
