  Regulation, legal liabilities, and societal concerns challenge the adoption
of AI in safety and security-critical applications. One of the key concerns is
that adversaries can cause harm by manipulating model predictions without being
detected. Regulation hence demands an assessment of the risk of damage caused
by adversaries. Yet, there is no method to translate this high-level demand
into actionable metrics that quantify the risk of damage.
  In this article, we propose a method to model and statistically estimate the
probability of damage arising from adversarial attacks. We show that our
proposed estimator is statistically consistent and unbiased. In experiments, we
demonstrate that the estimation results of our method have a clear and
actionable interpretation and outperform conventional metrics. We then show how
operators can use the estimation results to reliably select the model with the
lowest risk.
