1) I assume that by "OLS model" you mean (ordinary) least squares regression, which is in fact a method and not a model; people sometimes call the a "model" because it is (in theory) based on a model with certain assumptions. However, I can imagine also other things being meant by "OLS model". Also note that although the OLS regression theory depends on certain model assumptions, this doesn't necessarily mean that it is useless or meaningless if the model assumptions are not met. It still has some kind of interpretation as line/hyperplane/function (depending on what OLS method you are exactly talking about) approximating the data, and even statistical inference may (or may not) be approximately correct, depending on in which way assumptions are violated.
2) The OLS regression is not in fact robust against misspecification. There is a big literature on how particularly outliers will cause trouble.
3) "Is it OK to report...?" That's a weird kind of question. I'd certainly say it is not a good way to find out something valid from the data. This would require some more inspection and maybe testing (surely it is sensible to assess problems with the model assumptions, although this doesn't necessarily have to take the form of a hypothesis test). Whether it is "OK to report" something depends on what you write around it, what audience this is for etc. One could argue it is OK in the sense that it is correct to just say "I applied OLS regression without model checking and the result was this". The reader then knows a rather weakly justified approach was used and can make up their own mind (assuming they're competent enough). However, chances are I wouldn't put too much trust into any subject-matter interpretation or conclusion from this.