{"id":21467,"date":"2024-12-06T12:07:26","date_gmt":"2024-12-06T17:07:26","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=21467"},"modified":"2024-12-06T15:33:53","modified_gmt":"2024-12-06T20:33:53","slug":"jailbreaks-threaten-low-resource-languages","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2024\/12\/jailbreaks-threaten-low-resource-languages\/","title":{"rendered":"&#8216;Jailbreaks&#8217; Threaten Low-Resource Languages"},"content":{"rendered":"<p>The large language models (LLMS) that power many popular text-based artificial intelligence applications are vulnerable to jailbreaking attacks, during which a user enters a malicious prompt to bypass an application\u2019s guardrails to trick it into making inappropriate or harmful content.<\/p>\n<p>New research by Johns Hopkins computer scientists has found that low-resource languages, such as Armenian and M\u00e3ori, are more vulnerable to these attacks since there is limited text data available for AI model training.<\/p>\n<p style=\"text-align: left;\">The <a href=\"https:\/\/aclanthology.org\/volumes\/2024.acl-long\/\">study<\/a>, published in the proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, highlights a significant issue with serious implications for multilingual applications, the researchers say.<img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-21730 alignright\" src=\"https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2024\/12\/speechbubbles-300x180.png\" alt=\"graphical image of speech bubbles conveying communication\" width=\"300\" height=\"180\" srcset=\"https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2024\/12\/speechbubbles-300x180.png 300w, https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2024\/12\/speechbubbles-768x462.png 768w, https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2024\/12\/speechbubbles.png 968w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>\u201cThe discrepancies stem from the initial training stage, when the LLMs are exposed to only a small amount of data in these languages with limited resources,\u201d says lead author Lingfeng Shen, MS \u201924, now a research scientist at ByteDance. \u201cThis means the root issue is that there simply isn\u2019t enough data available for less widely used languages during the model\u2019s first training process.\u201d<\/p>\n<p>\u201cEnsuring that LLMs can safely interact with users in various languages, including those with fewer resources, is critical for inclusivity and global applicability,\u201d says team member <a href=\"https:\/\/engineering.jhu.edu\/faculty\/daniel-khashabi\/\" target=\"_blank\" rel=\"noopener\">Daniel Khashabi<\/a>, an assistant professor of <a href=\"https:\/\/www.cs.jhu.edu\/\" target=\"_blank\" rel=\"noopener\">computer science<\/a> and a member of the<a href=\"https:\/\/www.clsp.jhu.edu\/\" target=\"_blank\" rel=\"noopener\"> Center for Language and Speech Processing<\/a>. \u201cIf these systems are not safe and reliable across all languages, it could lead to misinformation, harmful content dissemination, and overall decreased trust in AI technologies.\u201d<\/p>\n<p>The researchers encourage those training the next iterations of popular LLMs to include more data from low-resource languages such as Mongolian, Urdu, and Hausa. They also suggest developing new approaches for handling languages with limited data for training AI models.<\/p>\n<p>\u201cOur research advocates for more equitable AI development that considers the linguistic diversity of all users,\u201d says Khashabi.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The large language models (LLMS) that power many popular text-based artificial intelligence applications are vulnerable to jailbreaking attacks, during which a user enters a malicious prompt to bypass an application\u2019s guardrails to trick it into making inappropriate or harmful content.<\/p>\n","protected":false},"author":29,"featured_media":21730,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[24],"tags":[],"class_list":["post-21467","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impact","issue-winter-2025"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>&#039;Jailbreaks&#039; Threaten Low-Resource Languages - JHU Engineering Magazine<\/title>\n<meta name=\"description\" content=\"The Center for Language and Speech Processing (CLSP) at Johns Hopkins University focuses on advancing research in language and speech technologies. 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