I presume you are being genuine, but you should know that your comment is almost indistinguishable from satire. More specifically (and hopefully more helpfully) it sounds like the comments made by Brian Wansink before his recent fall. Google for his story if you are unfamiliar.
Assuming the effects they found were actually legitimate (that is, the alleged "torture" of the data was statistically sound), how is this at all a bad thing?
Assuming that the effects are legitimate is exactly the problem. You are right, if we somehow know that the effects found are real and reproducible, then all is good. The problem is that we almost never know this. Presumably what you mean is "If the results are reproducible, what's the harm?". I'd agree with this, but the problem is how to know ahead of time that the results are going to be reproducible.
Shouldn't it be safe to assume that they could simply repeat the trial with a different stated objective and successfully yield the positive result?
If they did the statistics correctly (accounting for the multiple inferences, all assumptions about iid data met, no biased dropouts, everything else aboveboard) and got a really solid result, then yes, it's theoretically likely that the results would be reproducible. The problem is that they almost certainly didn't do the statistics correctly, and intentionally or not they probably violated a lot assumptions. In too many cases, even the main line conclusions can't be replicated. It's rarely "safe" to assume that an effect is real until it's actually been replicated, and almost never safe to make this assumption for result obtained by sifting the data after the fact looking for correlations.
I'm having a hard time seeing the problem with taking an exploratory approach and just testing placebo vs. some treatment and reporting whatever you find.
There are ways to do it this way, but it usually takes larger sample sizes than are available. It also requires "bespoke" statistics that are easy to get wrong. In practice, it's usually better to use the incidental "results" as idea generators for future experiments, rather than assuming that the findings are real and don't require further testing.