Browser fingerprinting is used for bot detection. In response, bots have started altering their fingerprints to evade detection. We conduct the first large-scale evaluation to study whether and how altering fingerprints helps bots evade detection. To systematically investigate such evasive bots, we deploy a honey site that includes two anti-bot services (DataDome and BotD) and solicit bot traffic from 20 different bot services that purport to sell ''realistic and undetectable traffic.'' Across half a million requests recorded on our honey site, we find an average evasion rate of 52.93% against DataDome and 44.56% evasion rate against BotD. Our analysis of fingerprint attributes of evasive bots shows that they indeed alter their fingerprints. Moreover, we find that the attributes of these altered fingerprints are often inconsistent with each other. We propose FP-Inconsistent, a data-driven approach to detect such inconsistencies across space (two attributes in a given browser fingerprint) and time (a single attribute at two different points in time). Our evaluation shows that our approach can reduce the evasion rate of evasive bots by 44.95%-48.11% while maintaining a true negative rate of 96.84% on traffic from real users.