One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection

In three linesMLJailDe, a multilingual jailbreak detection framework, uses back-translation data augmentation across 11 languages (2,232 benign, 1,239 jailbreak samples) and relative-distance constraints to reduce cross-lingual representation dispersion. Achieves F1=98.5% and F1=97.1% on unseen languages.

Summary generated by Claude — human-verified