Europe’s Multilingual Reality Exposes AI Security Gaps, Putting Organizations at Risk
A stark reality has emerged in the world of artificial intelligence (AI) security: not all languages are treated equally when it comes to model function and safety. For organizations operating across Europe, this means a heightened risk of AI-related security breaches, particularly when dealing with language-based attacks. The issue lies in the way modern large language models (LLMs) process text in different languages, with performance and safety capabilities varying dramatically between them.
At its core, the LLM ecosystem relies heavily on natural language processing. Whether it’s a user interacting with a chatbot, issuing instructions for software development, or generating emails, these models are trained to understand and respond to human language. However, the wide range of languages supported by leading models like OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude reveals a concerning disparity. While these models can process text in dozens to hundreds of languages, their performance in less-supported languages is severely limited. For instance, Welsh and Swahili can only handle basic prompts and often produce grammatical errors.
The issue becomes more pronounced when examining the training data used by these models. English, being the most widely supported language, benefits from both its large volume of available data and tokenization schemes that represent it more efficiently than many other languages. As a result, academic research has shown that LLMs perform best in English when tackling logic, reasoning, coding, and math tasks. Furthermore, major AI labs often conduct safety tuning, behavior alignment, and reinforcement using English-speaking annotators.
The security implications of this disparity are stark. AI security vendor DeepKeep recently published a blog post highlighting the issue, titled “Your AI Speaks 100 Languages. Your AI Security Layer Doesn’t.” The company explained that the guardrails and security layers built into many AI products often fail to protect against language-based attacks equally across all languages. This is particularly problematic when dealing with culturally specific phrases or sensitive terms that may be translated poorly or mapped into language that no longer matches the security policy.
For organizations operating in Europe, this issue poses a significant risk. The region’s multilingual reality means that dozens of languages are used within one regulatory and economic bloc, making cross-border operations the norm rather than the exception. According to Yossi Altevet, chief technology officer (CTO) and co-founder at DeepKeep, this density exposes Europe to a higher risk of language-related security breaches.
The takeaway for organizations is clear: as they increasingly rely on AI tools in their daily operations, they must be aware of the potential language gaps that can put them at risk. Every multilingual enterprise, regardless of location, carries the same underlying risk when processing non-English prompts. To mitigate this risk, organizations should prioritize language-specific security measures and ensure that their AI systems are designed to handle diverse languages safely. Only by acknowledging and addressing these disparities can we truly safeguard our increasingly AI-dependent world.
Source: Dark Reading — 2026-07-24