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A classical scenario for tipping is that a dynamical system experiences aslow parameter drift across a fold tipping point, caused by a run-away positivefeedback loop. We study what happens if one turns around after one has crossedthe threshold. We derive a simple criterion that relates how far the parameterexceeds the tipping threshold maximally and how long the parameter stays abovethe threshold to avoid tipping in an inverse-square law to observableproperties of the dynamical system near the fold.  For the case when the dynamical system is subject to stochastic forcing wegive an approximation to the probability of tipping if a parameter changing intime reverses} near the tipping point.  The derived approximations are valid if the parameter change in time issufficiently slow. We demonstrate for a higher dimensional system, a model forthe Indian Summer Monsoon, how numerically observed escape from the equilibriumconverge to our asymptotic expressions. The inverse-square law between peak ofthe parameter forcing and the time the parameter spends above a given thresholdis also visible in the level curves of equal probability when the system issubject to random disturbances.

Inverse-square law between time and amplitude for crossing tipping thresholds