  Adversarial attacks on explainability models have drastic consequences when
explanations are used to understand the reasoning of neural networks in safety
critical systems. Path methods are one such class of attribution methods
susceptible to adversarial attacks. Adversarial learning is typically phrased
as a constrained optimisation problem. In this work, we propose algebraic
adversarial examples and study the conditions under which one can generate
adversarial examples for integrated gradients. Algebraic adversarial examples
provide a mathematically tractable approach to adversarial examples.
