USENIX Security '23 - Tubes Among Us: Analog Attack on Automatic Speaker Identification Shimaa Ahmed and Yash Wani, University of Wisconsin-Madison; Ali Shahin Shamsabadi, Alan Turing Institute; Mohammad Yaghini, University of Toronto and Vector Institute; Ilia Shumailov, Vector Institute and University of Oxford; Nicolas Papernot, University of Toronto and Vector Institute; Kassem Fawaz, University of Wisconsin-Madison Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial examples. A large number of modern systems protect themselves against such attacks by targeting artificiality, i.e., they deploy mechanisms to detect the lack of human involvement in generating the adversarial examples. However, these defenses implicitly assume that humans are incapable of producing meaningful and targeted adversarial examples. In this paper, we show that this base assumption is wrong. In particular, we demonstrate that for tasks like speaker identification, a human is capable of producing analog adversarial examples directly with little cost and supervision: by simply speaking through a tube, an adversary reliably impersonates other speakers in eyes of ML models for speaker identification. Our findings extend to a range of other acoustic-biometric tasks such as liveness detection, bringing into question their use in security-critical settings in real life, such as phone banking. View the full USENIX Security '23 program at https://www.usenix.org/conference/usenixsecurity23/program