Multilingual red-teaming benchmarks often reduce cultural localization to direct translation, overlooking sociotechnical factors shaping AI safety across contexts. By treating culturally distinct contexts as equivalent, this translation-centered approach can flatten regional diversity, obscure diverse perspectives, and reinforce single-ground-truth assumptions in AI safety evaluation. We present LatAm-RT, a context-aware red-teaming benchmark for incorporating Latin American cultural pluralism into AI safety evaluation. Its Layered Localization Taxonomy (LLT) models localization as an explicit experimental variable across three localization levels direct translation (L1), regional adaptation (L2), and national localization (L3) for Mexico, El Salvador, Costa Rica, and Chile while preserving comparable adversarial intent. We evaluate five language models using 284 prompts across three localization layers, four risk domains, and five adversarial framing strategies. Results show that localization is neither uniformly beneficial nor uniformly harmful: country-specific institutional vocabulary is associated with stronger safeguard activation in Privacy and Civic Systems, whereas politically situated narratives retain their adversarial effectiveness in Political and Information Harm. Adversarial framing remains the primary driver of attack success rate (ASR) variation, including a 55-percentage-point gap between GPT-4o and Claude Sonnet 4.6 under defensive framing. LatAm-RT introduces the Localization Effect (LE) to quantify regional robustness differences obscured by translation-based evaluations. These findings motivate pluralistic AI safety evaluation across culturally situated contexts. The dataset and annotation files are available at https://huggingface.co/datasets/Latam26/LatAmRT