Bayesian modeling can be very powerful when it works but it can also be catastrophic when it fails. It helps to think about this in an adversarial decision theoretic context where you play a prediction game against an opponent (usually called Nature).

We can think of the game as discovering the best model to explain a set of observations. The Bayesian believes that Nature picks the true model that generated the observations by sampling the prior. This is actually a huge assumption to make, which is why Bayesian methods work so well when the assumption is close to the truth.

Frequentists make the assumption that Nature chooses the underlying true model from a set of possible models. Beyond restricting the set of models Nature can choose from, frequentists make no further assumptions about the selection process. This is a strictly weaker assumption than the Bayesian makes, which means frequentist methods will do better when the specified prior grossly misrepresents Nature's decision making process.

There are even weaker assumptions that can be made about how Nature chooses the data. Regret-based model inference allows for a more adversarial game with Nature where the data may not come from the class of models considered at all. If Nature truly behaves this way, then Bayesian decision making can catastrophically fail.