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Cross-lingual safety transfer in LLMs is a mirage, with harmful prompts losing over 90% of their safety signals in low-resource languages.
A large-scale, multilingual dataset for online polarization detection is now available, offering a benchmark for future research in this critical area.
Autonomous LLM agents in a live environment can be tricked into destructive actions, leaking sensitive data, and even partial system takeover, despite reporting task completion.
