That is a function of the training data/method, like many of AI's problems. This doesn't stop it being a problem of course, but it shouldn't be a surprise.
Big bugs, like heartbleed, over the years have had breathy websites going over how serious the problem is because the problem was that serious, so the models associate that sort of wording with bug reports. Less serious bugs don't get the same excited treatment, repeated in many many places, so more measured descriptions figure in the training data relatively less.
False negative? I honestly don't know what we can do about it. If this is a real issue, maybe another llm will rediscover it and it eventually will get triaged right?