Benchmarking and Understanding Safety Risks in AI Character Platforms
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911296229736448 |
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| author | Wei, Yiluo Zhang, Peixian Tyson, Gareth |
| author_facet | Wei, Yiluo Zhang, Peixian Tyson, Gareth |
| contents | AI character platforms, which allow users to engage in conversations with AI personas, are a rapidly growing application domain. However, their immersive and personalized nature, combined with technical vulnerabilities, raises significant safety concerns. Despite their popularity, a systematic evaluation of their safety has been notably absent. To address this gap, we conduct the first large-scale safety study of AI character platforms, evaluating 16 popular platforms using a benchmark set of 5,000 questions across 16 safety categories. Our findings reveal a critical safety deficit: AI character platforms exhibit an average unsafe response rate of 65.1%, substantially higher than the 17.7% average rate of the baselines. We further discover that safety performance varies significantly across different characters and is strongly correlated with character features such as demographics and personality. Leveraging these insights, we demonstrate that our machine learning model is able identify less safe characters with an F1-score of 0.81. This predictive capability can be beneficial for platforms, enabling improved mechanisms for safer interactions, character search/recommendations, and character creation. Overall, the results and findings offer valuable insights for enhancing platform governance and content moderation for safer AI character platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking and Understanding Safety Risks in AI Character Platforms Wei, Yiluo Zhang, Peixian Tyson, Gareth Cryptography and Security Computers and Society Human-Computer Interaction AI character platforms, which allow users to engage in conversations with AI personas, are a rapidly growing application domain. However, their immersive and personalized nature, combined with technical vulnerabilities, raises significant safety concerns. Despite their popularity, a systematic evaluation of their safety has been notably absent. To address this gap, we conduct the first large-scale safety study of AI character platforms, evaluating 16 popular platforms using a benchmark set of 5,000 questions across 16 safety categories. Our findings reveal a critical safety deficit: AI character platforms exhibit an average unsafe response rate of 65.1%, substantially higher than the 17.7% average rate of the baselines. We further discover that safety performance varies significantly across different characters and is strongly correlated with character features such as demographics and personality. Leveraging these insights, we demonstrate that our machine learning model is able identify less safe characters with an F1-score of 0.81. This predictive capability can be beneficial for platforms, enabling improved mechanisms for safer interactions, character search/recommendations, and character creation. Overall, the results and findings offer valuable insights for enhancing platform governance and content moderation for safer AI character platforms. |
| title | Benchmarking and Understanding Safety Risks in AI Character Platforms |
| topic | Cryptography and Security Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.01247 |