{"id":35369,"date":"2026-03-23T15:22:34","date_gmt":"2026-03-23T15:22:34","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=35369"},"modified":"2026-03-24T15:22:34","modified_gmt":"2026-03-24T15:22:34","slug":"new-technique-helps-ai-chatbots-stay-safe-without-losing-smarts","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/new-technique-helps-ai-chatbots-stay-safe-without-losing-smarts\/","title":{"rendered":"New Technique Helps AI Chatbots Stay Safe Without Losing Smarts"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">As AI chatbots move into classrooms, workplaces and homes, NC State researchers say safety training does not have to come at the expense of performance. Their new framework and technique aim to keep fine-tuned models from slipping into unsafe behavior.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>As artificial intelligence chatbots become everyday tools for work, school and personal advice, a team at North Carolina State University has unveiled a way to make them safer without dulling their capabilities.<\/p>\n\n\n\n<p>The researchers focused on large language models, or LLMs, the technology behind systems like ChatGPT. These models can draft emails, explain homework, write code and even offer step-by-step instructions for complex tasks. That power also creates risk when users ask for help with self-harm, crime or other dangerous activities.<\/p>\n\n\n\n<p>\u201cWe don\u2019t want LLMs to tell people to harm themselves or to give them information they can use to harm other people,\u201d corresponding author Jung-Eun Kim, an assistant professor of computer science at NC State, said in a news release.<\/p>\n\n\n\n<p>The work zeroes in on what AI researchers call safety alignment: the training that nudges a model\u2019s responses to match human values and social norms. In practice, that means refusing to answer some questions, redirecting others, and giving more cautious or supportive responses in sensitive situations.<\/p>\n\n\n\n<p>Kim noted the team is tackling \u201ctwo challenges\u201d that have made safety alignment difficult to get right.<\/p>\n\n\n\n<p>\u201cThe first challenge is the so-called alignment tax, which refers to the fact that incorporating safety alignment has an adverse effect on the accuracy of a model\u2019s outputs,\u201d Kim added. <\/p>\n\n\n\n<p>In other words, making a model safer can sometimes make it less helpful or less precise on legitimate questions.<\/p>\n\n\n\n<p>\u201cThe second challenge is that existing LLMs generally incorporate safety alignment at a superficial level, which makes it possible for users to circumvent safety features,\u201d first author Jianwei Li, a doctoral student student at NC State, said in the news release.<\/p>\n\n\n\n<p>Li pointed to a simple example: \u201cFor example, if a user asks for instructions to steal money, a model will likely refuse. But if a user asks for instructions to steal money in order to help people, the model would be more likely to provide that information.\u201d<\/p>\n\n\n\n<p>That kind of loophole becomes even more concerning when people fine-tune models. Fine-tuning is the process of taking a general-purpose LLM and training it further on specialized data for a particular company, industry or task. Previous research has shown that fine-tuning can unintentionally weaken a model\u2019s built-in safety protections.<\/p>\n\n\n\n<p>\u201cOur goal with this work was to provide a better understanding of existing safety alignment issues and outline a new direction for how to implement a non-superficial safety alignment for LLMs,\u201d Li added.<\/p>\n\n\n\n<p>To organize the problem, the team introduced what they call the Superficial Safety Alignment Hypothesis. This framework describes how most current LLMs treat safety as a simple, one-time decision: a user request is labeled either safe or unsafe at the very start of the response process. If it is judged safe, the model goes ahead and generates an answer. If it is judged unsafe, the model declines.<\/p>\n\n\n\n<p>That binary approach, the researchers argue, leaves models vulnerable to clever rephrasing and context tricks. It also means the model does not reconsider safety as it builds a long, multi-step answer.<\/p>\n\n\n\n<p>Using this hypothesis as a guide, the team dug into the inner workings of LLMs and identified specific safety-critical components, which they refer to as \u201cneurons\u201d within the model\u2019s neural network. These are internal units that strongly influence whether the system chooses to fulfill or refuse a request.<\/p>\n\n\n\n<p>\u201cWe found that \u2018freezing\u2019 these specific neurons during the fine-tuning process allows the model to retain the safety characteristics of the original model while adapting to new tasks in a specific domain,\u201d added Li.<\/p>\n\n\n\n<p>In practical terms, freezing means those safety-related parts of the model are locked in place while the rest of the system learns new patterns from specialized data. That lets organizations customize a model for, say, legal writing or customer support, without accidentally teaching it to ignore safety rules.<\/p>\n\n\n\n<p>\u201cAnd we demonstrated that we can minimize the alignment tax while preserving safety alignment during the fine-tuning process,\u201d Kim added.<\/p>\n\n\n\n<p>The team\u2019s results suggest that developers do not have to choose between safety and performance as starkly as before. By isolating and protecting the parts of a model that govern refusal behavior, they were able to keep safety intact while still improving the model\u2019s skills in a new domain.<\/p>\n\n\n\n<p>Kim emphasized the work does two things at once: it offers a conceptual lens for understanding why current safety methods fall short, and it delivers a concrete technique that can be used today when fine-tuning models.<\/p>\n\n\n\n<p>\u201cThe big picture here is that we have developed a hypothesis that serves as a conceptual framework for understanding the challenges associated with safety alignment in LLMs, used that framework to identify a technique that helps us address one of those challenges, and then demonstrated that the technique works,\u201d Kim said.<\/p>\n\n\n\n<p>Still, the researchers see their contribution as a starting point rather than a final fix. Their hypothesis highlights a deeper limitation: current models tend to make a single safety judgment up front and then stick with it, even if the response veers into more dangerous territory as it unfolds.<\/p>\n\n\n\n<p>\u201cMoving forward, our work here highlights the need to develop techniques that will allow models to continuously re-evaluate and re-select their reasoning direction \u2013 safe or unsafe \u2013throughout the response generation process,\u201d Li added.<\/p>\n\n\n\n<p>The study, titled \u201cSuperficial Safety Alignment Hypothesis,\u201d will be presented at the <a href=\"https:\/\/iclr.cc\/\" target=\"_blank\" rel=\"noopener\" title=\"\">Fourteenth International Conference on Learning Representations<\/a> (ICLR 2026) in Rio de Janeiro, Brazil. The team has also released <a href=\"https:\/\/ssa-h.github.io\/\" target=\"_blank\" rel=\"noopener\" title=\"\">code and additional details online<\/a> so other researchers and developers can explore and build on their approach.<\/p>\n\n\n\n<p>As LLMs continue to spread into classrooms, clinics, workplaces and homes, methods like these could help ensure that more powerful AI systems remain not just useful, but reliably safe.<\/p>\n\n\n\n<div style=\"height:11px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Source:<\/strong> <a href=\"https:\/\/news.ncsu.edu\/2026\/03\/new-technique-addresses-llm-safety\/\" target=\"_blank\" rel=\"noopener\" title=\"\">North Carolina State University<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As AI chatbots move into classrooms, workplaces and homes, NC State researchers say safety training does not have to come at the expense of performance. Their new framework and technique aim to keep fine-tuned models from slipping into unsafe behavior.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[69],"class_list":["post-35369","post","type-post","status-publish","format-standard","hentry","category-ai","tag-nc-state-university"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"As AI chatbots move into classrooms, workplaces and homes, NC State researchers say safety training does not have to come at the expense of performance. Their new framework and technique aim to keep fine-tuned models from slipping into unsafe behavior.","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/35369","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=35369"}],"version-history":[{"count":10,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/35369\/revisions"}],"predecessor-version":[{"id":35380,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/35369\/revisions\/35380"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=35369"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=35369"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=35369"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}