ROK-FORTRESS: Measuring the Effect of Geopolitical Transcreation for National Security and Public Safety
arXiv:2605.14152v2 Announce Type: replace-cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual safety is mostly assessed through translation-only benchmarks that preserve the underlying scenario, leaving how language and geopolitical context interact largely unexamined beyond a few language pairs. We introduce ROK-FORTRESS, a bilingual, culturally adversarial NSPS benchmark that uses the English-Korean language pair and U.S.-ROK geopolitical axis as a case study, separating the eff
The increasing sophistication and widespread deployment of large language models across sensitive domains necessitate advanced safety evaluations that account for geopolitical and cultural nuances, moving beyond simple translation.
This benchmark highlights a critical vulnerability in current LLM safety protocols, as models deployed in high-stakes national security and public safety contexts could fail catastrophically if not rigorously tested for culturally adversarial inputs.
The focus shifts from mere multilingual capacity to assessing how geopolitical context and cultural 'transcreation' can be exploited, forcing developers to build more robust and context-aware safety mechanisms.
- · LLM safety researchers
- · National security agencies
- · Responsible AI developers
- · South Korea
- · LLMs lacking cultural adversarial training
- · Nations reliant on inadequately tested LLMs
- · AI developers ignoring geopolitical context
New benchmarks and methodologies will emerge to address geopolitical and culturally adversarial testing of AI systems.
Governments and critical infrastructure operators will mandate more stringent, context-specific safety certifications for AI deployments.
The development of 'geopolitically aware' AI will accelerate, potentially creating a divide between generic AI and specialized, secure AI.
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Read at arXiv cs.AI