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Machine learning has become a fundamental tool for computer security since it can rapidly evolve to changing and complex situations. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze thedoi:10.1007/s10994-010-5188-5 fatcat:3ytty65oknh7lkl6tfovfmu5ra