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User authentication systems based on cardiovascular biosignals have gained prominence in recent years, as these signals are presumed to be difficult to forge. We challenge this assumption by showing that an observer who has access to one type of cardiac data --- such as a user's pulse waveform, readily obtainable from video and commercial smartwatches --- can design a spoofing attack strong enough to fool authentication systems based on other cardiovascular biosignals. We present BioForge, an approach that leverages a cycle-consistent generative adversarial network to synthesize realistic physiological signals for a given user without relying on simultaneously collected supervision data. We evaluate BioForge on multiple open-access datasets and an array of verification systems, many of which can be fooled over 50% of the time in 10 or fewer attempts. Notably, we are able to fool systems that rely not just on heart rate and peak locations but also on the morphology of the waveforms. We additionally showcase how BioForge can be used to spoof authentication systems from biosignal data extracted from video clips of a target user. Our work demonstrates that authentication systems should not rely on the secrecy of cardiovascular biosignals.