This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
Browser font fallback determines the threat. When a page specifies font-family: Arial, Helvetica, sans-serif and a string contains Cyrillic а, the browser checks Arial’s glyph tables, finds Cyrillic coverage, and renders it using Arial’s Cyrillic glyphs — which are pixel-identical to the Latin ones. The CSS font stack you ship determines which column of the danger rate table applies to your users. Arial at 40.8% is a different risk profile from Didot at 19.2%.。关于这个话题，同城约会提供了深入分析
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