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The robustness of modern machine learning (ML) models has become anincreasing concern within the community. The ability to subvert a model intomaking errant predictions using seemingly inconsequential changes to input isstartling, as is our lack of success in building models robust to this concern.Existing research shows progress, but current mitigations come with a high costand simultaneously reduce the model's accuracy. However, such trade-offs maynot be necessary when other design choices could subvert the risk. In thissurvey we review the current literature on attacks and their real-worldoccurrences, or limited evidence thereof, to critically evaluate the real-worldrisks of adversarial machine learning (AML) for the average entity. This isdone with an eye toward how one would then mitigate these attacks in practice,the risks for production deployment, and how those risks could be managed. Indoing so we elucidate that many AML threats do not warrant the cost andtrade-offs of robustness due to a low likelihood of attack or availability ofsuperior non-ML mitigations. Our analysis also recommends cases where an actorshould be concerned about AML to the degree where robust ML models arenecessary for a complete deployment.

You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks