IndicSafeEval：评估大语言模型在多语言说服性越狱攻击下的安全鲁棒性

Computer Science > Computation and Language arXiv:2609.03781 (cs) [Submitted on 3 Sep 2026 (v1), last revised 4 Sep 2026 (this version, v2)] Title:IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks Authors:Saikat Mondal, Mamta, Deeksha Varshney, Oana Cocarascu, Asif Ekbal View a PDF of the paper titled IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks, by Saikat Mondal and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly used in multilingual settings, yet their safety is still evaluated primarily in English. This limits our understanding of how alignment failures manifest in low-resource and culturally diverse languages. We introduce IndicSafeEval, a persuasion-based jailbreak evaluation framework for Indian languages. Our benchmark combines ten safety critical content categories with six human-like persuasive strategies across four different Indian languages, such as Hindi, Bengali, Marathi and Punjabi, resulting in 7,200 adversarial prompts. We conduct a systematic black-box evaluation of several open-source LLMs to examine how their safety behaviour varies across languages, persuasion strategies, and risk categories. Our analysis shows that the model does not behave equally safely across all languages and prompt styles. Instead, safety performance depends strongly on both the languages used and the way a request is phrased using persuasive cues. We further observe that different risk categories exhibit different levels of vulnerability, with some types of harmful content being significantly more susceptible to persuasion-based jailbreaks than others. These findings reveal important limitations of current safety evaluations, which are largely English-centric, and underscore the need for multilingual and persuasion-aware benchmarking frameworks to more accurately assess real-world LLM safety. Our implementation is available at this https URL. Warning: this paper contains example data that may be offensive or harmful. Comments: 38 pages, 7 figures, 33 tables. Accepted to Findings of EMNLP 2026. Contains examples of harmful model outputs Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) ACM classes: I.2.7 Cite as: arXiv:2609.03781 [cs.CL] (or arXiv:2609.03781v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.03781 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Saikat Mondal [view email] [v1] Thu, 3 Sep 2026 12:54:44 UTC (4,181 KB) [v2] Fri, 4 Sep 2026 11:46:25 UTC (4,135 KB) Full-text links: Access Paper: View a PDF of the paper titled IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks, by Saikat Mondal and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)