Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and modern loanwords. These lexical relationships create ambiguity for cross-lingual semantic interpretation, as similar forms may correspond to shared, divergent, or borrowed meanings. To evaluate whether LLMs can make these distinctions, we introduce SemCog Bench, a curated benchmark of 1,858 Arabic--Hebrew word pairs with sentence-level annotations for cognate identification and semantic disambiguation. We evaluate a diverse set of open-source and proprietary LLMs across multiple input representations and contextual settings. Our results show reliance on surface-form similarity, with weaker performance on false friends and wide variation on loanwords. We further find that context and scale yield model-dependent gains, while original-script inputs generally perform best. Our findings reveal limitations in cross-lingual form-meaning reasoning and establish SemCog Bench as a benchmark for cross-lingual lexical semantics. Our code and data are publicly available.

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