In smart homes, access-control policies increasingly depend on contexts, such as who is taking an action, whether there is an emergency, or whether an adult is nearby. The vast literature on context sensing could potentially be leveraged to support contextual access control, yet this literature mostly ignores attacks, adversaries, and privacy. In this paper, we reevaluate the literature on home context sensing through a security and privacy mindset. We first describe a novel threat model in smart homes focusing on the capabilities of non-technical adversaries. Replay, imitation, and shoulder-surfing attacks are much more likely in this model. We summarize contexts relevant to access control in homes, mapping them to existing sensors. We then systematize the sensing literature to construct a decision framework for home context sensing that considers security, privacy, and usability. Applying our framework, we find that current sensors do not fully mitigate likely threats in homes. Some sensors are susceptible to simple threats like physical denial-of-service attacks, making it easy to bypass policies relying on the absence of a characteristic. Many sensors collect more data than needed and are not effective for all groups of users or under all situations.