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arXiv AI By M P V S Gopinadh

Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation

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This study investigates whether large language models (LLMs) exhibit different safety vulnerabilities when prompted with emojis instead of plain text. By testing 50 emoji‑augmented prompts on four open‑source LLMs—Mistral 7B, Qwen 2 7B, Gemma 2 9B, and Llama 3 8B—the authors found varying success rates of unsafe responses: Gemma 2 9B and Mistral 7B each had a 10% success rate, Llama 3 8B had 6%, while Qwen 2 7B was fully resistant. A chi‑square test confirmed significant differences in the outcome distributions, suggesting that input representation can markedly affect model robustness.

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