{"id":2308,"date":"2026-05-07T11:32:17","date_gmt":"2026-05-07T09:32:17","guid":{"rendered":"https:\/\/askem.eu\/?p=2308"},"modified":"2026-05-07T11:32:23","modified_gmt":"2026-05-07T09:32:23","slug":"garde-fous-open-source-pour-llm-nemo-guardrails-et-guardrails-ai-en-production","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/05\/07\/garde-fous-open-source-pour-llm-nemo-guardrails-et-guardrails-ai-en-production\/","title":{"rendered":"Garde-fous open source pour LLM, NeMo Guardrails et Guardrails AI en production"},"content":{"rendered":"<h2 class=\"wp-block-heading\">Garde-fous open source pour LLM&nbsp;: NeMo Guardrails et Guardrails AI en production<\/h2>\n\n\n\n<p>Un agent IA qui r\u00e9pond \u00e0 un utilisateur, c&rsquo;est une surface d&rsquo;attaque. Le mod\u00e8le peut halluciner, divulguer des donn\u00e9es, accepter une injection de prompt, tenir des propos d\u00e9plac\u00e9s, sortir de son p\u00e9rim\u00e8tre fonctionnel. Tester ces d\u00e9rives en amont est utile, mais ne suffit pas&nbsp;: il faut aussi <strong>filtrer en temps r\u00e9el<\/strong>, \u00e0 l&rsquo;entr\u00e9e comme \u00e0 la sortie. Deux projets open source dominent ce terrain&nbsp;: <strong><a href=\"https:\/\/github.com\/NVIDIA-NeMo\/Guardrails\">NeMo Guardrails<\/a><\/strong> de NVIDIA, et <strong><a href=\"https:\/\/guardrailsai.com\">Guardrails AI<\/a><\/strong>. Tous deux installables sans d\u00e9pendre du cloud.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Pourquoi des garde-fous, en plus du prompt syst\u00e8me<\/h3>\n\n\n\n<p>Un prompt syst\u00e8me bien r\u00e9dig\u00e9 pose le cadre, mais il reste contournable&nbsp;: injection de prompt, jailbreak, formulation d\u00e9tourn\u00e9e, contexte multi-tour. Un garde-fou est une couche d\u00e9terministe, ex\u00e9cut\u00e9e hors du LLM, qui inspecte chaque \u00e9change et d\u00e9cide de laisser passer, de bloquer, ou de r\u00e9\u00e9crire. On parle de d\u00e9fense en profondeur&nbsp;: le mod\u00e8le propose, la politique dispose. Cette s\u00e9paration est aussi un atout d&rsquo;audit, car les r\u00e8gles s&rsquo;expriment en clair et se versionnent comme du code.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">NeMo Guardrails&nbsp;: un langage d\u00e9di\u00e9 pour les conversations<\/h3>\n\n\n\n<p>NeMo Guardrails, publi\u00e9 sous licence Apache 2.0 par NVIDIA, introduit un format d\u00e9claratif appel\u00e9 <strong><a href=\"https:\/\/docs.nvidia.com\/ace\/colang-language\/2.0\/language-reference\/introduction.html\">Colang<\/a><\/strong> pour d\u00e9crire les flux de conversation autoris\u00e9s, les sujets \u00e0 \u00e9viter, les actions \u00e0 appeler, et les v\u00e9rifications \u00e0 effectuer avant d&rsquo;envoyer la r\u00e9ponse. Une politique typique combine plusieurs niveaux&nbsp;: un garde-fou d&rsquo;entr\u00e9e qui d\u00e9tecte une injection de prompt ou un sujet interdit, un garde-fou de dialogue qui canalise la conversation vers les intentions d\u00e9finies, un garde-fou d&rsquo;ex\u00e9cution qui valide les appels d&rsquo;outils, et un garde-fou de sortie qui inspecte la r\u00e9ponse pour bloquer donn\u00e9es personnelles, propos toxiques ou contenu hors champ.<\/p>\n\n\n\n<p>L&rsquo;outil s&rsquo;int\u00e8gre nativement avec LangChain, OpenAI, Hugging Face et les LLM auto-h\u00e9berg\u00e9s type vLLM ou Ollama. La validation s&rsquo;appuie sur des classificateurs locaux (intentions, toxicit\u00e9), sur des r\u00e8gles d\u00e9claratives Colang, ou sur un second appel LLM de contr\u00f4le. C&rsquo;est l&rsquo;approche la plus compl\u00e8te si l&rsquo;on construit un agent conversationnel structur\u00e9.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Guardrails AI&nbsp;: valider chaque sortie comme on valide une API<\/h3>\n\n\n\n<p>Guardrails AI, publi\u00e9 sous licence Apache 2.0 par Guardrails AI Inc., adopte un angle diff\u00e9rent. L&rsquo;outil traite la sortie d&rsquo;un LLM comme un objet \u00e0 valider contre un sch\u00e9ma&nbsp;: structure, types, longueur, ton, pr\u00e9sence ou absence de PII, citations v\u00e9rifiables, absence d&rsquo;hallucination par rapport \u00e0 un contexte fourni. Le projet maintient un <strong><a href=\"https:\/\/guardrailsai.com\/hub\">Guardrails Hub<\/a><\/strong>, catalogue de validateurs r\u00e9utilisables&nbsp;: d\u00e9tection de secrets, v\u00e9rification d&rsquo;URL, contrainte de format JSON, similarit\u00e9 s\u00e9mantique, conformit\u00e9 \u00e0 une liste blanche.<\/p>\n\n\n\n<p>Quand un validateur \u00e9choue, plusieurs strat\u00e9gies sont possibles&nbsp;: rejeter, redemander au mod\u00e8le avec un prompt correctif, ou r\u00e9parer automatiquement la sortie. Cette logique de sch\u00e9ma + retry rend Guardrails AI tr\u00e8s adapt\u00e9 aux pipelines o\u00f9 la sortie LLM alimente une \u00e9tape d\u00e9terministe en aval, par exemple une API m\u00e9tier ou une base de donn\u00e9es.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Comment les choisir, ou les combiner<\/h3>\n\n\n\n<p>Les deux outils ne se recouvrent que partiellement. NeMo Guardrails excelle sur le contr\u00f4le de flux conversationnel multi-tour et sur l&rsquo;orchestration de garde-fous \u00e0 plusieurs \u00e9tages. Guardrails AI excelle sur la validation fine et structur\u00e9e de chaque sortie. En pratique, les architectures s\u00e9rieuses combinent les deux&nbsp;: NeMo Guardrails pour cadrer la conversation et bloquer les d\u00e9rives en entr\u00e9e, Guardrails AI pour valider strictement la sortie avant utilisation. Sur un agent simple en production, commencer par Guardrails AI suffit souvent, car la majorit\u00e9 des incidents proviennent de sorties mal form\u00e9es ou hors politique.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ce qui doit toujours \u00eatre en place<\/h3>\n\n\n\n<p>Quel que soit l&rsquo;outil, certaines r\u00e8gles sont non-n\u00e9gociables. Bloquer en entr\u00e9e toute tentative d&rsquo;injection de prompt connue (patterns de jailbreak publics, tentatives de red\u00e9finition de r\u00f4le, encodages d\u00e9tourn\u00e9s). D\u00e9tecter et masquer les donn\u00e9es personnelles \u00e0 la fois en entr\u00e9e et en sortie. Limiter la conversation au p\u00e9rim\u00e8tre fonctionnel d\u00e9fini, et journaliser les blocages pour analyse. Ajouter un m\u00e9canisme de remont\u00e9e humaine quand la politique bloque une demande l\u00e9gitime, sous peine de frustrer les utilisateurs honn\u00eates.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Pour aller plus loin<\/h3>\n\n\n\n<p>Une fois la couche garde-fous en place, deux compl\u00e9ments naturels. D&rsquo;abord <strong>Garak<\/strong>, que l&rsquo;on pr\u00e9sentera dans un prochain article, pour tester en amont la robustesse du dispositif face aux attaques connues. Ensuite <strong><a href=\"https:\/\/askem.eu\/en\/2026\/04\/02\/langfuse-observer-et-evaluer-ses-pipelines-llm-open-source-en-production\/\" type=\"post\" id=\"2162\">Langfuse<\/a><\/strong> ou un \u00e9quivalent d&rsquo;observabilit\u00e9, pour suivre le taux de blocage, les faux positifs, et les \u00e9volutions de comportement du mod\u00e8le au fil des versions. Un garde-fou non mesur\u00e9 d\u00e9rive aussi vite que le mod\u00e8le qu&rsquo;il prot\u00e8ge.<\/p>\n\n\n\n<p><em>Mettre des garde-fous open source autour d&rsquo;un LLM, c&rsquo;est reconna\u00eetre que la s\u00e9curit\u00e9 d&rsquo;un agent ne tient pas \u00e0 la qualit\u00e9 du mod\u00e8le seul, mais \u00e0 la couche de contr\u00f4le qui l&rsquo;entoure. Tout existe en open source&nbsp;: d\u00e9claratif, auditable, versionnable. Il reste \u00e0 l&rsquo;\u00e9crire, le tester et le faire vivre.<\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>Garde-fous open source pour LLM&nbsp;: NeMo Guardrails et Guardrails AI en production Un agent IA qui r\u00e9pond \u00e0 un utilisateur, c&rsquo;est une surface d&rsquo;attaque. Le mod\u00e8le peut halluciner, divulguer des donn\u00e9es, accepter une injection de prompt, tenir des propos d\u00e9plac\u00e9s, sortir de son p\u00e9rim\u00e8tre fonctionnel. Tester ces d\u00e9rives en amont est utile, mais ne suffit [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2309,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","osh_disable_topbar_sticky":"default","osh_disable_header_sticky":"default","osh_sticky_header_style":"default","osh_sticky_header_effect":"","osh_custom_sticky_logo":0,"osh_custom_retina_sticky_logo":0,"osh_custom_sticky_logo_height":0,"osh_background_color":"","osh_links_color":"","osh_links_hover_color":"","osh_links_active_color":"","osh_links_bg_color":"","osh_links_hover_bg_color":"","osh_links_active_bg_color":"","osh_menu_social_links_color":"","osh_menu_social_hover_links_color":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2308","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Garde-fous open source pour LLM, NeMo Guardrails et Guardrails AI en production - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D&#039;\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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