Data poisoning is an adversarial scenario where an attacker feeds a specially
crafted sequence of samples to an online model in order to subvert learning. We
introduce Lethean Attack, a novel data poisoning technique that induces
catastrophic forgetting on an online model. We apply the attack in the context
of Test-Time Training, a modern online learning framework aimed for
generalization under distribution shifts. We present the theoretical rationale
and empirically compare it against other sample sequences that naturally induce
forgetting. Our results demonstrate that using lethean attacks, an adversary
could revert a test-time training model back to coin-flip accuracy performance
using a short sample sequence.

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