Attrition is the loss of participants after randomization, and its effect on the result depends entirely on whether the loss is differential — that is, whether the reasons for leaving differ between arms and are related to the outcome.
Consider a trial of a new treatment for a chronic condition, with 500 participants in each arm at the start. In the treatment arm, 100 participants stop the treatment because of side effects and leave the study; in the control arm, 20 participants leave, mostly for unrelated reasons. At the end, the analysis compares 400 treated participants with 480 controls. The treated group is now enriched with people who tolerated the treatment — and tolerance is often correlated with milder disease and better prognosis. The control group still resembles the original randomized population. The two groups are no longer exchangeable, and the treatment looks better than it is, not because the treatment worked, but because the people who remained in the treatment arm were the ones who were doing well anyway.
The direction of the distortion is not fixed. If the participants who leave are the ones who are deteriorating, and they leave disproportionately from the control arm, the control group is enriched with people who are doing well, and the treatment looks worse than it is. The appraisal question is not "how much attrition was there?" but "was the attrition differential, and is the reason for leaving related to the outcome?" A trial with 30% attrition in both arms for similar reasons may be less distorted than one with 10% attrition concentrated in one arm.