[{"data":1,"prerenderedAt":548},["ShallowReactive",2],{"blog-tag-hu-llm-security":3},[4,80,151,207,279,362,413,509],{"slug":5,"published":6,"minutes":7,"category":8,"tags":9,"tagPages":15,"keywords":18,"about":31,"sources":39,"cover":70,"og":71,"expertise":72,"locales":73,"lang":76,"title":77,"description":78,"coverAlt":79},"llm-output-guardrails-moderation","2026-10-17",13,"security",[10,11,12,13,14],"LLM security","Guardrails","Content moderation","OWASP","Output validation",[16,17],"llm-security","guardrails",[19,20,21,22,23,24,25,26,27,28,29,30],"llm kimenet guardrailek","helytelen kimenetkezelés llm","owasp llm05","openai moderációs api","omni-moderation-latest","llama guard 4","nemo guardrails streaming","markdown kép adatkiszivárogtatás","llm kimenet html tisztítás","megalapozottság-detektálás","llm tartalommoderáció éles rendszerben","output rails",[32,34,37],{"name":12,"url":33},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FContent_moderation",{"name":35,"url":36},"Cross-site scripting","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCross-site_scripting",{"name":13,"url":38},"https:\u002F\u002Fowasp.org\u002F",[40,43,46,49,52,55,58,61,64,67],{"title":41,"url":42},"OWASP GenAI Security Project: LLM05:2025 Improper Output Handling","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm052025-improper-output-handling\u002F",{"title":44,"url":45},"OpenAI API Docs: Moderation","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Fmoderation",{"title":47,"url":48},"Meta: Llama Guard 4 model card and prompt formats","https:\u002F\u002Fwww.llama.com\u002Fdocs\u002Fmodel-cards-and-prompt-formats\u002Fllama-guard-4\u002F",{"title":50,"url":51},"NVIDIA NeMo Guardrails: Output rail streaming configuration","https:\u002F\u002Fdocs.nvidia.com\u002Fnemo\u002Fguardrails\u002Fconfigure-guardrails\u002Fyaml-schema\u002Fstreaming\u002Foutput-rail-streaming.md",{"title":53,"url":54},"Microsoft Learn: Prompt Shields in Azure AI Content Safety","https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fcontent-safety\u002Fconcepts\u002Fjailbreak-detection",{"title":56,"url":57},"Microsoft Learn: Groundedness detection in Azure AI Content Safety","https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fcontent-safety\u002Fconcepts\u002Fgroundedness",{"title":59,"url":60},"Microsoft Learn: Protected material detection in Azure AI Content Safety","https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fcontent-safety\u002Fconcepts\u002Fprotected-material",{"title":62,"url":63},"Guardrails AI documentation","https:\u002F\u002Fwww.guardrailsai.com\u002Fdocs",{"title":65,"url":66},"Vue.js docs: Security best practices","https:\u002F\u002Fvuejs.org\u002Fguide\u002Fbest-practices\u002Fsecurity.html",{"title":68,"url":69},"Simon Willison: Remote prompt injection in GitLab Duo leads to source code theft","https:\u002F\u002Fsimonwillison.net\u002F2025\u002FMay\u002F23\u002Fremote-prompt-injection-in-gitlab-duo\u002F","\u002Fimages\u002Fblog\u002Fllm-output-guardrails-moderation\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllm-output-guardrails-moderation\u002Fog.jpg","ai-engineer",[74,75,76],"en","de","hu","LLM output guardrailek: moderáció, biztonságos megjelenítés és ellenőrzés, mielőtt bármi történne","Mit kell ellenőrizni a modell válasza után: biztonságos Markdown, sémaellenőrzés műveletek előtt, moderációs osztályozók, streaming és megalapozottság.","LLM output guardrailek: csapvíz folyik egy szűrőkancsón át tiszta pohárba, fent kavicsok akadnak meg.",{"slug":81,"published":6,"minutes":82,"category":8,"tags":83,"tagPages":87,"keywords":88,"about":100,"sources":108,"cover":145,"og":146,"expertise":72,"locales":147,"lang":76,"title":148,"description":149,"coverAlt":150},"ai-red-teaming-checklist-llm-apps",12,[84,10,13,85,86],"AI red teaming","promptfoo","Penetration testing",[16],[89,90,91,92,93,94,95,96,97,98,99],"ai red teaming ellenőrzőlista","llm biztonsági tesztelés","llm alkalmazások red teaming","owasp genai red teaming útmutató","promptfoo red team","garak llm scanner","pyrit red teaming","llm behatolásteszt","owasp top 10 llm 2025 tesztelés","jailbreak tesztelés llm alkalmazás","rag chatbot biztonsági tesztelés",[101,104,107],{"name":102,"url":103},"Red team","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRed_team",{"name":105,"url":106},"Prompt injection","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPrompt_injection",{"name":13,"url":38},[109,112,115,118,121,124,127,130,133,136,139,142],{"title":110,"url":111},"OWASP GenAI Security Project: GenAI Red Teaming Guide (January 2025)","https:\u002F\u002Fgenai.owasp.org\u002Fresource\u002Fgenai-red-teaming-guide\u002F",{"title":113,"url":114},"OWASP GenAI Security Project: Top 10 for LLM Applications 2025","https:\u002F\u002Fgenai.owasp.org\u002Fllm-top-10\u002F",{"title":116,"url":117},"Bullwinkel et al. (Microsoft): Lessons From Red Teaming 100 Generative AI Products (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.07238",{"title":119,"url":120},"NIST AI 600-1: Generative Artificial Intelligence Profile (July 2024)","https:\u002F\u002Fnvlpubs.nist.gov\u002Fnistpubs\u002Fai\u002FNIST.AI.600-1.pdf",{"title":122,"url":123},"Promptfoo Docs: Red team quickstart","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fquickstart\u002F",{"title":125,"url":126},"Promptfoo Docs: Red team configuration","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fconfiguration\u002F",{"title":128,"url":129},"Promptfoo Docs: Red team plugins","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fplugins\u002F",{"title":131,"url":132},"Promptfoo Docs: Red team strategies","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fstrategies\u002F",{"title":134,"url":135},"NVIDIA garak: LLM vulnerability scanner (GitHub)","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fgarak",{"title":137,"url":138},"Microsoft PyRIT documentation","https:\u002F\u002Fmicrosoft.github.io\u002FPyRIT\u002F",{"title":140,"url":141},"EU AI Act, Article 55: Obligations for providers of GPAI models with systemic risk","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F55\u002F",{"title":143,"url":144},"OWASP GenAI LLM Top 10 2026: final entries (GitHub)","https:\u002F\u002Fgithub.com\u002FGenAI-Security-Project\u002FGenAI-LLM-Top10\u002Ftree\u002Fmain\u002F2026\u002Ffinal","\u002Fimages\u002Fblog\u002Fai-red-teaming-checklist-llm-apps\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fai-red-teaming-checklist-llm-apps\u002Fog.jpg",[74,75,76],"AI red teaming ellenőrzőlista LLM-appokhoz: mit támadj meg, mielőtt a felhasználók tennék","Gyakorlati red teaming ellenőrzőlista LLM-funkciókhoz: hatókör, OWASP 2025 tesztesetek, promptfoo, garak és PyRIT, emberi tesztelők és regressziós tesztek.","MI red teaming LLM-appokhoz: sisakos ember ellenőrzőlistával áll egy várkapu előtt, mellette faltörő kos.",{"slug":152,"published":153,"minutes":154,"category":155,"tags":156,"tagPages":161,"keywords":162,"about":173,"sources":182,"cover":201,"og":202,"expertise":72,"locales":203,"lang":76,"title":204,"description":205,"coverAlt":206},"text-to-sql-llm-guardrails","2026-10-16",8,"rag",[157,10,158,159,160],"Text-to-SQL","PostgreSQL","Agents","Analytics",[16],[163,164,165,166,167,168,169,170,171,172],"text-to-sql llm","text-to-sql guardrailek","llm sql ügynök biztonság","természetes nyelvből sql éles rendszerben","csak olvasható adatbázis szerepkör llm","bird benchmark text-to-sql","spider 2.0 benchmark","sqlglot sql validálás","row level security llm","beszélgetés az adatbázisoddal",[174,177,180],{"name":175,"url":176},"SQL","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSQL",{"name":178,"url":179},"Natural language interface to databases","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FNatural-language_user_interface",{"name":158,"url":181},"https:\u002F\u002Fwww.postgresql.org\u002F",[183,186,189,192,195,198],{"title":184,"url":185},"BIRD-bench: leaderboard and dataset description","https:\u002F\u002Fbird-bench.github.io\u002F",{"title":187,"url":188},"Lei et al.: Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.07763",{"title":190,"url":191},"PostgreSQL 18 Documentation: Row Security Policies","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Fddl-rowsecurity.html",{"title":193,"url":194},"PostgreSQL 18 Documentation: Client Connection Defaults (read-only and timeouts)","https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002Fcurrent\u002Fruntime-config-client.html",{"title":196,"url":197},"SQLGlot: SQL parser, transpiler and optimizer (GitHub)","https:\u002F\u002Fgithub.com\u002Ftobymao\u002Fsqlglot",{"title":199,"url":200},"OWASP Gen AI Security Project: LLM06:2025 Excessive Agency","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm062025-excessive-agency\u002F","\u002Fimages\u002Fblog\u002Ftext-to-sql-llm-guardrails\u002Fcover.webp","\u002Fimages\u002Fblog\u002Ftext-to-sql-llm-guardrails\u002Fog.jpg",[74,75,76],"Text-to-SQL LLM-mel: sémakontextus, csak olvasható szerepkörök és működő validálás","Mennyire pontos valójában a text-to-SQL, hogyan kapjon sémakontextust a modell, és milyen adatbázis- és kódszintű korlátok teszik biztonságossá az SQL-t.","Text-to-SQL LLM-mel: szövegbuborék rácsos ablakon át jut egy adatbázis-hengerbe, ajtaján lakat.",{"slug":208,"published":153,"minutes":209,"category":210,"tags":211,"tagPages":215,"keywords":217,"about":230,"sources":238,"cover":272,"og":273,"expertise":274,"locales":275,"lang":76,"title":276,"description":277,"coverAlt":278},"llm-endpoint-abuse-denial-of-wallet",14,"web",[212,10,213,13,214],"Nuxt","Rate limiting","Cost control",[216,16],"nuxt",[218,219,220,221,222,223,224,225,226,227,228,229],"denial of wallet llm","owasp llm10 korlátlan fogyasztás","rate limit llm api nuxtban","nuxt biztonsági rate limiter","openai api kulcs védelme frontenden","llm tokenkeret felhasználónként","llm költség korlátozása felhasználónként","nitro api rate limiting","cloudflare turnstile nuxt","anthropic költségkorlát","openai kemény költségkorlát","llm prompt méretkorlát",[231,233,236],{"name":213,"url":232},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRate_limiting",{"name":234,"url":235},"Denial-of-service attack","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDenial-of-service_attack",{"name":212,"url":237},"https:\u002F\u002Fnuxt.com\u002F",[239,242,245,248,251,254,257,260,263,266,269],{"title":240,"url":241},"OWASP GenAI Security Project: LLM10:2025 Unbounded Consumption","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm102025-unbounded-consumption\u002F",{"title":243,"url":244},"Nuxt Security: Rate Limiter middleware","https:\u002F\u002Fnuxt-security.vercel.app\u002Fmiddleware\u002Frate-limiter",{"title":246,"url":247},"Nuxt Security: Request Size Limiter middleware","https:\u002F\u002Fnuxt-security.vercel.app\u002Fmiddleware\u002Frequest-size-limiter",{"title":249,"url":250},"nuxt-auth-utils: README (GitHub)","https:\u002F\u002Fgithub.com\u002Fatinux\u002Fnuxt-auth-utils",{"title":252,"url":253},"h3: request utilities (getRequestIP, readValidatedBody)","https:\u002F\u002Fh3.dev\u002Futils\u002Frequest",{"title":255,"url":256},"Upstash: Ratelimiting algorithms","https:\u002F\u002Fupstash.com\u002Fdocs\u002Fredis\u002Fsdks\u002Fratelimit-ts\u002Falgorithms",{"title":258,"url":259},"Cloudflare Turnstile: Validate the token","https:\u002F\u002Fdevelopers.cloudflare.com\u002Fturnstile\u002Fget-started\u002Fserver-side-validation\u002F",{"title":261,"url":262},"Claude Platform Docs: Rate limits and spend limits","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fapi\u002Frate-limits",{"title":264,"url":265},"Claude Platform Docs: Token counting","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Ftoken-counting",{"title":267,"url":268},"OpenAI API Docs: Spend limits","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fspend-limits",{"title":270,"url":271},"OpenAI API Docs: Rate limits","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Frate-limits","\u002Fimages\u002Fblog\u002Fllm-endpoint-abuse-denial-of-wallet\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllm-endpoint-abuse-denial-of-wallet\u002Fog.jpg","vue-nuxt-developer",[74,75,76],"Denial of wallet: nyilvános LLM-végpontok védelme egy Nuxt-alkalmazásban","Visszaélés és elszabaduló költség megállítása egy nyilvános Nuxt LLM-útvonalon: auth, rate limit, promptkorlát, tokenkeret, Turnstile és költéskorlát.","Denial of wallet Nuxt-alkalmazásban: forgókapu lassítja a nyitott pénztárca felé tartó apró robotok sorát.",{"slug":280,"published":281,"minutes":82,"category":8,"tags":282,"tagPages":287,"keywords":289,"about":298,"sources":311,"cover":356,"og":357,"expertise":72,"locales":358,"lang":76,"title":359,"description":360,"coverAlt":361},"dpia-llm-feature-worked-example","2026-10-01",[283,284,10,285,286],"DPIA","GDPR","Data protection","AI Act",[288,16],"gdpr",[290,291,292,293,294,295,296,297],"dpia llm funkciókhoz","adatvédelmi hatásvizsgálat ai asszisztens","gdpr 35. cikk ai","dsfa-v ausztria ai","llm ügyfélszolgálat gdpr","prompt injection adatszivárgás gdpr","ai act 50. cikk chatbot","dpia sablon llm",[299,302,305,308],{"name":300,"url":301},"General Data Protection Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Flegal-content\u002FEN\u002FTXT\u002F?uri=CELEX:32016R0679",{"name":303,"url":304},"WP29 guidelines on data protection impact assessment (WP248 rev.01)","https:\u002F\u002Fec.europa.eu\u002Fnewsroom\u002Farticle29\u002Fitems\u002F611236\u002Fen",{"name":306,"url":307},"OWASP LLM01:2025 Prompt Injection","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm01-prompt-injection\u002F",{"name":309,"url":310},"Regulation (EU) 2024\u002F1689 (AI Act)","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj",[312,314,316,319,322,325,328,331,334,337,339,342,345,348,351,353],{"title":313,"url":301},"Regulation (EU) 2016\u002F679 (GDPR), EUR-Lex",{"title":315,"url":304},"WP29 guidelines on DPIA, WP248 rev.01 (European Commission item page)",{"title":317,"url":318},"WP248 rev.01 PDF, adopted 4 April 2017 and revised 4 October 2017","https:\u002F\u002Fec.europa.eu\u002Fnewsroom\u002Fjust\u002Fdocument.cfm?doc_id=47711",{"title":320,"url":321},"EDPB Guidelines 07\u002F2020 on the concepts of controller and processor","https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2023-10\u002Fedpb_guidelines_202007_controllerprocessor_final_en.pdf",{"title":323,"url":324},"EDPB Opinion 28\u002F2024 on AI models, adopted 17 December 2024","https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2024-12\u002Fedpb_opinion_202428_ai-models_en.pdf",{"title":326,"url":327},"EDPB news release on Opinion 28\u002F2024","https:\u002F\u002Fwww.edpb.europa.eu\u002Fnews\u002Fedpb-opinion-on-ai-models-gdpr-principles-support-responsible-ai_en",{"title":329,"url":330},"EDPB, Report of the work undertaken by the ChatGPT Taskforce, 23 May 2024","https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2024-05\u002Fedpb_20240523_report_chatgpt_taskforce_en.pdf",{"title":332,"url":333},"DSFA-V, BGBl. II Nr. 278\u002F2018 (RIS)","https:\u002F\u002Fwww.ris.bka.gv.at\u002Feli\u002Fbgbl\u002FII\u002F2018\u002F278",{"title":335,"url":336},"DSFA-AV, BGBl. II Nr. 108\u002F2018 (RIS)","https:\u002F\u002Fwww.ris.bka.gv.at\u002Feli\u002Fbgbl\u002FII\u002F2018\u002F108",{"title":338,"url":307},"OWASP Top 10 for LLM Applications 2025: LLM01 Prompt Injection",{"title":340,"url":341},"OWASP Top 10 for LLM Applications 2025: LLM02 Sensitive Information Disclosure","https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm022025-sensitive-information-disclosure\u002F",{"title":343,"url":344},"OpenAI, Data controls in the OpenAI platform","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fyour-data",{"title":346,"url":347},"Commission Implementing Decision (EU) 2023\u002F1795 on the EU–US Data Privacy Framework","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdec_impl\u002F2023\u002F1795\u002Foj\u002Feng",{"title":349,"url":350},"CJEU, Case C-184\u002F20, OT v Vyriausioji tarnybinės etikos komisija","https:\u002F\u002Feur-lex.europa.eu\u002Flegal-content\u002FEN\u002FTXT\u002F?uri=CELEX:62020CJ0184",{"title":352,"url":310},"Regulation (EU) 2024\u002F1689 (AI Act), EUR-Lex",{"title":354,"url":355},"Regulation (EU) 2026\u002F1744 (Digital Omnibus on AI), EUR-Lex","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2026\u002F1744\u002Foj","\u002Fimages\u002Fblog\u002Fdpia-llm-feature-worked-example\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fdpia-llm-feature-worked-example\u002Fog.jpg",[74,75,76],"DPIA egy LLM-es ügyfélszolgálati asszisztensre: példa a GDPR 35. cikke szerint","Kidolgozott DPIA a GDPR 35. cikke szerint: AI-asszisztens rendelési adatokból fogalmazott vevői válaszokhoz. Mikor kell, mik a kockázatok, kik a felelősök?","DPIA LLM-es asszisztensre: csíptetős űrlap egy szövegbuborékos robot, egy nagyító és egy pecsét mellett.",{"slug":363,"published":364,"minutes":365,"category":8,"tags":366,"tagPages":367,"keywords":368,"about":377,"sources":382,"cover":407,"og":408,"expertise":72,"locales":409,"lang":76,"title":410,"description":411,"coverAlt":412},"lakera-guard","2026-08-04",9,[105,11,10,12],[17,16],[369,370,371,372,373,374,375,376],"lakera guard","lakera guard teszt","prompt injection szűrő","prompt injection felismerés","llm guardrails összehasonlítás","pint benchmark","check point ai guardrails","lakera árak",[378,379],{"name":105,"url":106},{"name":380,"url":381},"Check Point","https:\u002F\u002Fwww.checkpoint.com\u002F",[383,386,389,392,395,398,401,404],{"title":384,"url":385},"Lakera documentation: Guard API","https:\u002F\u002Fdocs.lakera.ai\u002Fdocs\u002Fapi\u002Fguard",{"title":387,"url":388},"Guard API endpoint reference","https:\u002F\u002Fdocs.lakera.ai\u002Fapi-reference\u002Flakera-api\u002Fguard\u002Fscreen-content",{"title":390,"url":391},"Lakera documentation: projects","https:\u002F\u002Fdocs.lakera.ai\u002Fdocs\u002Fprojects",{"title":393,"url":394},"Lakera documentation: self-hosting","https:\u002F\u002Fdocs.lakera.ai\u002Fdocs\u002Fselfhosting",{"title":396,"url":397},"Lakera platform pricing","https:\u002F\u002Fplatform.lakera.ai\u002Fpricing",{"title":399,"url":400},"PINT benchmark on GitHub","https:\u002F\u002Fgithub.com\u002Flakeraai\u002Fpint-benchmark",{"title":402,"url":403},"Lakera homepage","https:\u002F\u002Fwww.lakera.ai\u002F",{"title":405,"url":406},"Check Point press release on the Lakera acquisition","https:\u002F\u002Fwww.checkpoint.com\u002Fpress-releases\u002Fcheck-point-acquires-lakera-to-deliver-end-to-end-ai-security-for-enterprises\u002F","\u002Fimages\u002Fblog\u002Flakera-guard\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flakera-guard\u002Fog.jpg",[74,75,76],"Lakera Guard: promptinjectiós szűrés a kérés határán","A Lakera Guard értékelése: egyetlen végpont a modell előtt, a pontszámok mögötti PINT-benchmark, és miért áll meg az ingyenes csomag havi 10 000 kérésnél.","Lakera Guard: egy őr az ajtóban szitát tart, amely fennakaszt egy horgos borítékot.",{"slug":414,"published":415,"updated":416,"minutes":82,"category":8,"tags":417,"tagPages":421,"keywords":422,"about":433,"sources":442,"cover":503,"og":504,"expertise":72,"locales":505,"lang":76,"title":506,"description":507,"coverAlt":508},"pii-redaction-llm-pipelines","2026-06-26","2026-09-01",[418,284,419,10,420],"PII redaction","Microsoft Presidio","Pseudonymisation",[288,16],[423,424,425,426,427,428,429,430,431,432],"pii kitakarás llm","hogyan távolítsunk el pii-t llm küldés előtt","microsoft presidio útmutató","pii felismerés német és magyar","álnevesítés vs anonimizálás gdpr","visszafordítható tokenizálás llm promptokban","pii kitakarása logokban és trace-ekben","llm adatmaszkolás","pii guardrail litellm","pii felismerés tesztelése recall",[434,437,439],{"name":435,"url":436},"Personal data","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPersonal_data",{"name":300,"url":438},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGeneral_Data_Protection_Regulation",{"name":440,"url":441},"Data anonymization","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FData_anonymization",[443,446,449,452,455,458,461,464,467,470,473,476,479,482,485,488,491,494,497,500],{"title":444,"url":445},"Microsoft Presidio: documentation (limitations, methods)","https:\u002F\u002Fpresidio.dataprivacystack.org\u002F",{"title":447,"url":448},"Presidio: supported entities and country-specific recognizers","https:\u002F\u002Fpresidio.dataprivacystack.org\u002Fsupported_entities\u002F",{"title":450,"url":451},"Presidio: supporting additional languages","https:\u002F\u002Fpresidio.dataprivacystack.org\u002Fanalyzer\u002Flanguages\u002F",{"title":453,"url":454},"Presidio: anonymizer operators","https:\u002F\u002Fpresidio.dataprivacystack.org\u002Fanonymizer\u002F",{"title":456,"url":457},"Google Cloud: infoTypes reference","https:\u002F\u002Fdocs.cloud.google.com\u002Fsensitive-data-protection\u002Fdocs\u002Finfotypes-reference",{"title":459,"url":460},"Google Cloud: pseudonymization in Sensitive Data Protection","https:\u002F\u002Fdocs.cloud.google.com\u002Fsensitive-data-protection\u002Fdocs\u002Fpseudonymization",{"title":462,"url":463},"Microsoft Learn: Azure Language PII detection language support","https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Flanguage-service\u002Fpersonally-identifiable-information\u002Flanguage-support",{"title":465,"url":466},"AWS: Detecting PII entities with Amazon Comprehend","https:\u002F\u002Fdocs.aws.amazon.com\u002Fcomprehend\u002Flatest\u002Fdg\u002Fhow-pii.html",{"title":468,"url":469},"spaCy: models and languages","https:\u002F\u002Fspacy.io\u002Fusage\u002Fmodels",{"title":471,"url":472},"Hugging Face: novakat\u002Fnerkor-hubert (Hungarian NER)","https:\u002F\u002Fhuggingface.co\u002Fnovakat\u002Fnerkor-hubert",{"title":474,"url":475},"arXiv: An Evaluation Study of Hybrid Methods for Multilingual PII Detection","https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.07551",{"title":477,"url":478},"LiteLLM: Presidio PII masking guardrail","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproxy\u002Fguardrails\u002Fpii_masking_v2",{"title":480,"url":481},"Langfuse: masking","https:\u002F\u002Flangfuse.com\u002Fdocs\u002Fobservability\u002Ffeatures\u002Fmasking",{"title":483,"url":484},"GDPR Article 4: definitions (pseudonymisation, 4(5))","https:\u002F\u002Fgdpr-info.eu\u002Fart-4-gdpr\u002F",{"title":486,"url":487},"EDPB: Guidelines 01\u002F2025 on Pseudonymisation","https:\u002F\u002Fwww.edpb.europa.eu\u002Four-work-tools\u002Fdocuments\u002Fpublic-consultations\u002F2025\u002Fguidelines-012025-pseudonymisation_en",{"title":489,"url":490},"IAPP: EDPB publishes draft guidelines on pseudonymization","https:\u002F\u002Fiapp.org\u002Fnews\u002Fa\u002F-what-s-in-a-name-edpb-publishes-draft-guidelines-on-pseudonymization",{"title":492,"url":493},"Taylor Wessing: Analysis of the CJEU judgment in C-413\u002F23 P (EDPS v SRB)","https:\u002F\u002Fwww.taylorwessing.com\u002Fen\u002Finsights-and-events\u002Finsights\u002F2025\u002F09\u002Fanalysis-of-the-cjeu-judgment",{"title":495,"url":496},"Jones Day: CJEU clarifies scope of personal data in EDPS v SRB","https:\u002F\u002Fwww.jonesday.com\u002Fen\u002Finsights\u002F2025\u002F09\u002Fcjeu-clarifies-scope-of-personal-data-in-edps-v-srb-decision",{"title":498,"url":499},"IAPP: leaked Council Digital Omnibus compromise drops the revised personal data definition","https:\u002F\u002Fiapp.org\u002Fnews\u002Fa\u002Feu-member-states-leaked-digital-omnibus-compromise-proposal-eliminates-revised-gdpr-definition-of-personal-data",{"title":501,"url":502},"Law Health Tech: Pseudonymisation under the GDPR and the Digital Omnibus (May 2026)","https:\u002F\u002Flawhealthtech.com\u002F2026\u002F05\u002F04\u002Fpseudonymisation-under-the-gdpr-where-we-are-what-may-change-under-the-digital-omnibus-and-what-regulators-think\u002F","\u002Fimages\u002Fblog\u002Fpii-redaction-llm-pipelines\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpii-redaction-llm-pipelines\u002Fog.jpg",[74,75,76],"PII-kitakarás LLM-pipeline-okban: hol, hogyan, és mit mond a GDPR","PII-kitakarás LLM-pipeline-okban: visszafordítható tokenek vs. maszkolás, Presidio, felhős DLP, német és magyar hiányok, pszeudonimizálás a GDPR szerint.","PII-kitakarás LLM-pipeline-okban: kéz filccel kitakar sorokat egy lapon, robot várja a lapot.",{"slug":510,"published":511,"updated":512,"minutes":365,"category":8,"tags":513,"tagPages":515,"keywords":516,"about":523,"sources":530,"cover":542,"og":543,"expertise":72,"locales":544,"lang":76,"title":545,"description":546,"coverAlt":547},"rebuff","2026-06-22","2026-07-09",[105,10,11,514],"Canary tokens",[16,17],[510,517,518,519,520,521,522],"prompt injection észlelés","rebuff python sdk","llm prompt injection guardrail","canary token szivárgás észlelés","rebuff alternatíva","rebuff teszt",[524,527],{"name":525,"url":526},"Rebuff","https:\u002F\u002Fgithub.com\u002Fprotectai\u002Frebuff",{"name":528,"url":529},"Large language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",[531,533,536,539],{"title":532,"url":526},"Rebuff repository, archived 16 May 2025",{"title":534,"url":535},"Rebuff 0.1.1 on PyPI, released 20 January 2024","https:\u002F\u002Fpypi.org\u002Fproject\u002Frebuff\u002F",{"title":537,"url":538},"LangChain: Rebuff, detecting prompt injection attacks","https:\u002F\u002Fwww.langchain.com\u002Fblog\u002Frebuff",{"title":540,"url":541},"LLM Guard repository, archived 9 July 2026","https:\u002F\u002Fgithub.com\u002Fprotectai\u002Fllm-guard","\u002Fimages\u002Fblog\u002Frebuff\u002Fcover.webp","\u002Fimages\u002Fblog\u002Frebuff\u002Fog.jpg",[74,75,76],"Rebuff: négy rétegű prompt-injekció-észlelés, mára archiválva","A Rebuff heurisztika, LLM, támadásvektorbázis és canary-tokenek alapján pontozta a promptokat. Archiválták 2025 májusában, utolsó kiadása 2024. január.","Rebuff: lezárt archívumdoboz áll négy védelmi réteg, árok, kerítés, fal és kapu előtt.",1793451237700]