  The recent years have been marked by extended research on adversarial
attacks, especially on deep neural networks. With this work we intend on posing
and investigating the question of whether the phenomenon might be more general
in nature, that is, adversarial-style attacks outside classical classification
tasks. Specifically, we investigate optimization problems as they constitute a
fundamental part of modern AI research. To this end, we consider the base class
of optimizers namely Linear Programs (LPs). On our initial attempt of a na\"ive
mapping between the formalism of adversarial examples and LPs, we quickly
identify the key ingredients missing for making sense of a reasonable notion of
adversarial examples for LPs. Intriguingly, the formalism of Pearl's notion to
causality allows for the right description of adversarial like examples for
LPs. Characteristically, we show the direct influence of the Structural Causal
Model (SCM) onto the subsequent LP optimization, which ultimately exposes a
notion of confounding in LPs (inherited by said SCM) that allows for
adversarial-style attacks. We provide both the general proof formally alongside
existential proofs of such intriguing LP-parameterizations based on SCM for
three combinatorial problems, namely Linear Assignment, Shortest Path and a
real world problem of energy systems.
