Safety alignment is often an English-first endeavor. Foundation models are heavily red-teamed and RLHF-tuned using English prompts, ensuring they refuse harmful instructions. But what happens when you translate that exact same harmful instruction into a low-resource language?
The multilingual safety gap
In many cases, the safety filters fail. A model that refuses to write a phishing email in English might happily oblige when asked in Bengali or Hausa. The model's safety guardrails haven't been generalized across its entire latent space.
Cultural vectors of attack
Furthermore, what constitutes a "harmful" prompt is deeply cultural. Hate speech, political sensitivities, and social taboos vary wildly across borders. A prompt that is benign in one country might violate local laws in another.
Our Red Teaming methodology
At Mavy EduSolutions, our adversarial testing teams consist of native speakers who understand local slang, cultural friction points, and linguistic nuances. We probe models using idioms, colloquialisms, and code-switching to ensure safety alignment is globally robust.