Grosse, KathrinBieringer, LukasBesold, Tarek R.Biggio, BattistaKrombholz, Katharina2023-04-102023-04-102023-04-102023-01-0110.1109/TIFS.2023.3251842https://infoscience.epfl.ch/handle/20.500.14299/196830WOS:000952857700002Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze attack occurrence and concern and evaluate statistical hypotheses on factors influencing threat perception and exposure. Our results shed light on real-world attacks on deployed machine learning. On the organizational level, while we find no predictors for threat exposure in our sample, the amount of implement defenses depends on exposure to threats or expected likelihood to become a target. We also provide a detailed analysis of practitioners' replies on the relevance of individual machine learning attacks, unveiling complex concerns like unreliable decision making, business information leakage, and bias introduction into models. Finally, we find that on the individual level, prior knowledge about machine learning security influences threat perception. Our work paves the way for more research about adversarial machine learning in practice, but yields also insights for regulation and auditing.Computer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringsecurityorganizationsmachine learningdata modelstraining datatrainingproductionadversarial machine learningmachine learning securityquantitative user studytext::journal::journal article::research article