OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities

Current AI safety systems have significant blind spots: Existing harmful prompt detection methods fail particularly when attacks use low-resource languages or non-text formats like images and audio, creating exploitable vulnerabilities in AI safety measures.

A new detection approach shows substantial improvements: Omniguard improves detection accuracy by 11.57% for multilingual prompts, 20.44% for image-based prompts, and achieves state-of-the-art performance for audio-based harmful content, demonstrating that cross-language and cross-modality safety is technically achievable.

Efficiency matters for practical AI safety: By reusing computational work already done during AI generation, Omniguard operates approximately 120 times faster than competing approaches, making comprehensive safety screening practical for real-world deployment rather than just theoretical.

Safety systems need to work across all input types: As AI systems become multimodal (accepting text, images, audio), safety moderation must evolve beyond text-only detection—harmful intent can be encoded in any format, and defenses must match this versatility.

Language-agnostic safety is essential for global responsibility: The research highlights that AI safety cannot be limited to English or high-resource languages; responsible AI deployment requires equal protection across the languages and modalities that users actually employ.