Pre-trained language models are a highly effective source of knowledge transfer for natural language processing tasks, as their development represents an investment of resources beyond the reach of most researchers and end users. The widespread availability of such easily adaptable resources has enabled high levels of performance, which is especially valuable for low-resource language users who have typically been overlooked when it comes to NLP applications. However, these models introduce vulnerabilities in NLP toolchains, since they may prove vulnerable to attacks from malicious actors with access to the data used for downstream training. By perturbing instances from the training set, such attacks seek to undermine model capabilities and produce radically different outcomes during inference. We show that adversarial data manipulation has a severe effect on model performance, with BERT's performance dropping by more than 30% on average across all tasks at a poisoning ratio greater than 50%. Additionally, we conduct the first evaluation of this kind in the Basque language domain, establishing the vulnerability of low-resource models to the same form of attack.