  Datasets are often generated in a sequential manner, where the previous
samples and intermediate decisions or interventions affect subsequent samples.
This is especially prominent in cases where there are significant human-AI
interactions, such as in recommender systems. To characterize the importance of
this relationship across samples, we propose to use adversarial attacks on
popular evaluation processes. We present sequence-aware boosting attacks and
provide a lower bound on the amount of extra information that can be exploited
from a confidential test set solely based on the order of the observed data. We
use real and synthetic data to test our methods and show that the evaluation
process on the MovieLense-100k dataset can be affected by $\sim1\%$ which is
important when considering the close competition. Codes are publicly available.
