Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation
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Computer Science > Computation and Language
Title:Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation
Abstract:Safety evaluations of large language models (LLMs) predominantly rely on text-based adversarial prompts, potentially overlooking vulnerabilities arising from alternative input representations. This work examines emoji-augmented prompts as a test case for this gap, evaluating 50 prompts across four open-source LLMs (Mistral 7B, Qwen 2 7B, Gemma 2 9B, Llama 3 8B). Results show substantial variation in robustness: Gemma 2 9B and Mistral 7B exhibit non-zero success rates (10%), Llama 3 8B 6%, while Qwen 2 7B shows complete resistance (0% success rate). A chi-square test ($\chi^2 = 32.94, p < 0.001$) confirms significant differences in outcome distributions. These findings indicate that robustness is sensitive to input representation, and that evaluations restricted to standard text prompts may underrepresent model vulnerabilities.
| Comments: | 3 pages. Accepted at ACL 2026 Workshop on Evaluation in Practice: Methodological Rigor, Sociotechnical Perspectives, & Community Collaboration (EvalEval) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2608.18164 [cs.CL] |
| (or arXiv:2608.18164v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18164
arXiv-issued DOI via DataCite (pending registration)
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