  Explainable artificial intelligence (XAI) methods are portrayed as a remedy
for debugging and trusting statistical and deep learning models, as well as
interpreting their predictions. However, recent advances in adversarial machine
learning (AdvML) highlight the limitations and vulnerabilities of
state-of-the-art explanation methods, putting their security and
trustworthiness into question. The possibility of manipulating, fooling or
fairwashing evidence of the model's reasoning has detrimental consequences when
applied in high-stakes decision-making and knowledge discovery. This survey
provides a comprehensive overview of research concerning adversarial attacks on
explanations of machine learning models, as well as fairness metrics. We
introduce a unified notation and taxonomy of methods facilitating a common
ground for researchers and practitioners from the intersecting research fields
of AdvML and XAI. We discuss how to defend against attacks and design robust
interpretation methods. We contribute a list of existing insecurities in XAI
and outline the emerging research directions in adversarial XAI (AdvXAI).
Future work should address improving explanation methods and evaluation
protocols to take into account the reported safety issues.
