The finding that circulated this week is not about model error rates or benchmark gaps. It is about a property of how pattern-based sentiment models process language: surface features — word choice, phrasing, syntactic structure — drive predictions more reliably than semantic content. Apruzzese's research formalizes what practitioners have observed informally: that adversarial headlines designed to mislead LLM-based trading systems can be crafted without changing what a sentence actually reports. The threat model is not a data breach or a model corruption — it is a carefully worded press release.