Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the fexibility of malicious workers in selecting which instances to label. Such a setup limits the applicability of the attack mechanisms and impedes further improvement of their success rate. This paper introduces a blackbox data poisoning attack framework that fnds the optimal strategies for instance selection and labeling to attack unknown label aggregation models in crowdsourcing. We formulate the attack problem on top of a generic formalization of label aggregation models and then introduce a substitution approach that attacks a substitute aggregation model in replacement of the unknown model. Through extensive validation on multiple real-world datasets, we demonstrate the effectiveness of both instance selection and model substitution in improving the success rate of attacks.