arXiv:2606.15020v2 Announce Type: replace Abstract: Document-to-LLM applications typically read uploaded PDFs by first translating them into text through an extraction layer whose output is not ordinarily exposed to users. We show that this layer enables split-view PDFs: one document can have two semantic views before model reasoning. By mining specification-permitted or implementation-tolerated representation gaps at the PDF render/extract boundary, we instantiate 25 extraction gaps (EG) in which extractors return attacker-controlled or extractor-dependent text while the rendered page shows benign or different content. The gaps form four families: semantic overrides, hidden semantic injection, reading-order splits, and font-decoding splits, and 14 gaps have no exact path/mechanism-level match in prior PDF-to-LLM attacks. We evaluate these gaps on 16 PDF processing stacks and 7 commercial LLM services. Each gap causes render-extract divergence on at least one stack. Under a gap-level exposure criterion, every evaluated service exposes at least one gap, with 12/25 to 21/25 exposed gaps. Exposure is driven mainly by the ingestion stack -- not model identity alone. We further show that tested safety filters cover only selected hidden-text constructions. To support triage, we develop a static screening scanner whose rules trigger on all 25 benchmark gaps, and discuss dual-view consistency as a longer-term defense direction.