Modern AI systems based on deep learning are neither traditional tools nor full-blown agents. Rather, they are characterised by idiosyncratic agential profiles, i.e., combinations of agency-relevant properties. Modern AI systems lack superficial features which enable people to recognise agents but possess sophisticated information processing capabilities which can undermine human goals. I argue that systems fitting this description, when they are adversarial with respect to human users, pose particular risks to those users. To explicate my argument, I provide conditions under which agential profiles are explanatorily relevant to harms caused. I then contend that the role of recommender systems in producing harmful outcomes like digital addiction satisfies these conditions.