How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.
The paper introduces KIDBench, a benchmark designed to evaluate the safety of large language models (LLMs) for children aged 7-11. It includes realistic child queries across ten categories, single- and multi-turn prompts, and compares different prompting strategies—no cues, implicit cues, and explicit age instructions—showing that cueing improves safety scores. The study also reveals uneven safety performance across languages and cultures, and presents KIDGuardLlama, a child-safety evaluator, and KIDLlama, a child-safe response model.
By Samee Arif, Angana Borah, Rada Mihalcea
arXiv:2606. 04867v1 Announce Type: new Abstract: As AI companion platforms such as Replika and Character.
By Yanjing Ren, Reza Ebrahimi, TengTeng Ma
arXiv:2606. 08044v1 Announce Type: cross Abstract: Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention.
By Enyi Jiang, Anders Gj{\o}lbye, Yibo Jacky Zhang, Sanmi Koyejo
arXiv:2607. 05407v1 Announce Type: cross Abstract: Modern artificial intelligence (AI) systems present profound new risks to child safety.
By Neil Kale, Rebecca Portnoff, Pratiksha Thaker, Michael Simpson, Robertson Wang, Kevin Kuo, Chhavi Yadav, Virginia Smith
arXiv:2608. 14577v1 Announce Type: cross Abstract: Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis.
By Zhouyuan Ma, Yutao Wu, Hanxun Huang, Xiang Zheng, Xiao Liu, Yixin Cao, Zuxuan Wu, Xingjun Ma, Yu-Gang Jiang
arXiv:2608.21775v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From adver...
By Afshin Orojlooyjadid, Hitesh Patel
arXiv:2608. 05086v1 Announce Type: new Abstract: Language models differ in how safely they behave and these differences are measured by safety benchmarks.
By Joshua Fonseca Rivera (Independent), Neil Shah (Independent), David Demitri Africa (UK AI Security Institute), Konstantinos Voudouris (UK AI Security Institute)
The paper investigates whether existing AI safety benchmarks, designed for large language models, are suitable for evaluating small language models (SLMs). By testing five benchmark suites on 26 open‑source SLMs with a unified scoring rubric, the authors find that ambiguous judgments dominate, especially for complex prompts and certain architectures. This ambiguity, linked to factors like lexical density and output perplexity, undermines the reliability of aggregate leaderboards and reveals a confound between model capability and perceived safety.
By Nyamtulla Shaik, Fengjun Li, Bo Luo
arXiv:2605. 17173v2 Announce Type: replace-cross Abstract: Large language models exhibit safety degradation in non-English languages.
By Max Zhang, Ameen Patel, Sang T. Truong, Sanmi Koyejo
arXiv:2606. 18356v1 Announce Type: cross Abstract: Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects.
By Yuchuan Tian, Mengyu Zheng, Haocheng Mei, Ye Yuan, Chao Xu, Xinghao Chen, Hanting Chen, Yu Wang
arXiv:2605.12015v3 Announce Type: replace-cross
Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files...
By Chang Jin, An Wang, Zeming Wei, Kai Wang, Biaojie Zeng, Qiaosheng Zhang, Chao Yang, Jingjing Qu, Xia Hu, Xingcheng Xu