The Atlantic Meridional Overturning Circulation (AMOC) plays a critical role in regulating global climate patterns with its potential destabilization and collapse posing significant risks. In this study, we introduce two novel methods to assess AMOC stability, based on a Bayesian framework to estimate its sensitivity, and ensembles of parabolic approximations, respectively. They provide alternative indicators to detect early warning signals (EWSs) for a potential destabilization. By incorporating non-linear and physically motivated drivers, such as temperature and Greenland meltwater runoff, we obtain a more realistic representation of AMOC dynamics and address an important limitation of previous studies, which often rely on linear forcing assumptions. We detect significant EWS for a potential ongoing AMOC destabilization in our sensitivity-based indicator across many combinations of forcing scenarios and response models. However, it revealed large variations, dependent on the considered forcing scenario and methodological choices. While an AR(1) response to linear forcing, consistent with assumptions in previous EWS analyses of the AMOC, emerged as the ‘best’ fit based on Bayes factor analysis, other evaluation criteria provided no clear support. Even though the second EWS indicator, obtained using parabolic approximations, revealed a recent significant peak under linear forcing, and appears to suggests that the AMOC might have passed a critical transition in the late 20th century assuming a temperature driver, we find no clear indication of a considerable destabilization in either case. Our findings highlight the sensitivity of AMOC stability assessments to assumptions about forcing scenarios, response models, and evaluation criteria, emphasizing the need for careful interpretation of EWSs for abrupt transitions in the Earth’s climate. While our methods advance EWS analyses by incorporating non-linear forcing and alternative response functions to better represent AMOC dynamics, they also underscore the limitations of applying such tools to complex climate subsystems, represented by one-dimensional time series.