First, the original question was about false negatives, and therefore about the sensitivity (positive rate on true positives), not the specificity (negative rate on true negatives). So perhaps you wrote specificity by mistake? By definition, the sensitivity can be calculated only from true positive cases, so the relevant N is just the positives. There were 103 positive samples from 48 patients, and the independent paper did their math using N = 48.
Abbott's marketing referred to a study of 125 patients confirmed positive by PCR, linked below. So it's more, but not dramatically so. The affiliated study doesn't report a confidence interval, but the binomial 95% confidence interval is [97.1%, 100%]. That barely overlaps with the independent study's [82.8%, 98.7%]. So perhaps the affiliated researchers just got lucky and the independent ones got unlucky, or perhaps too many of the independent paper's >=14 day samples were <17 days and they should have waited longer; though depending how cynical your priors are you might also conclude the groups were different (not to allege any misconduct, just to say there's a lot of reasonable judgment in what patients you select that could push the result around by this small amount).
Regardless, the sensitivity of the test is definitely not an unqualified 100%. Even disregarding the independent study completely, that would still be a somewhat misleading claim, given the small sample size in the affiliated study and that patients with mild illness probably develop lower titers than the average patient who sought medical care over the last few months.
https://www.medrxiv.org/content/10.1101/2020.04.27.20082362v...