This ignores the main strength of a Bayesian workflow. You can straight forwardly quantify the effect of your prior choice on your inference - pick a different prior; how much does that change the inference, etc etc. A good Bayesian workflow does not assume a prior to be true; it should be based on available evidence, and then stressed. To be a bit more concrete, let's say we wish to model the height of kangaroos. We come up with a model form, say regression, and a bunch of potential features. If we are Bayesian we might say; "I think nature prefers simple stable solutions, so I'll put a N(0,d) prior on my weights. We then compute a posterior and get a range of credible values. We can then say, "hey, what if I'm wrong and actually it's a student t, or it's flat prior or X or y or z", and use principled tools like marginal likelihood to say which family of models works best, do prior posterior comparisons to see how observations changed our prior etc etc.

If we do this under a frequentist framework we compute the regression coefficients, and can get some confidence bounds with some appeal to asymptotics (and nobody I've ever seen actually makes any attempt to validate these assumptions). And even when we are done, we get a confidence interval that has such a truly unintuitive definition that almost every person who is not a stats PhD fundamentally misinterprets.

To say frequentists make less assumptions is not true- they are just less explicit, and I consider it a strength not a weakness to highlight choices made by the statistician.