  Prompt-based approaches offer a cutting-edge solution to data privacy issues
in continual learning, particularly in scenarios involving multiple data
suppliers where long-term storage of private user data is prohibited. Despite
delivering state-of-the-art performance, its impressive remembering capability
can become a double-edged sword, raising security concerns as it might
inadvertently retain poisoned knowledge injected during learning from private
user data. Following this insight, in this paper, we expose continual learning
to a potential threat: backdoor attack, which drives the model to follow a
desired adversarial target whenever a specific trigger is present while still
performing normally on clean samples. We highlight three critical challenges in
executing backdoor attacks on incremental learners and propose corresponding
solutions: (1) \emph{Transferability}: We employ a surrogate dataset and
manipulate prompt selection to transfer backdoor knowledge to data from other
suppliers; (2) \emph{Resiliency}: We simulate static and dynamic states of the
victim to ensure the backdoor trigger remains robust during intense incremental
learning processes; and (3) \emph{Authenticity}: We apply binary cross-entropy
loss as an anti-cheating factor to prevent the backdoor trigger from devolving
into adversarial noise. Extensive experiments across various benchmark datasets
and continual learners validate our continual backdoor framework, achieving up
to $100\%$ attack success rate, with further ablation studies confirming our
contributions' effectiveness.
