{"id":11364,"date":"2024-04-05T08:45:19","date_gmt":"2024-04-05T08:45:19","guid":{"rendered":"https:\/\/dailyai.com\/?p=11364"},"modified":"2024-04-05T08:45:19","modified_gmt":"2024-04-05T08:45:19","slug":"anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/","title":{"rendered":"Antropisk: LLM'er med stor kontekst er s\u00e5rbare over for jailbreak med mange skud"},"content":{"rendered":"

Anthropic har udgivet en artikel, der beskriver en jailbreaking-metode med mange skud, som LLM'er med lang kontekst er s\u00e6rligt s\u00e5rbare over for.<\/strong><\/p>\n

St\u00f8rrelsen p\u00e5 en LLM's kontekstvindue bestemmer den maksimale l\u00e6ngde af en prompt. Kontekstvinduer er vokset konstant i l\u00f8bet af de sidste par m\u00e5neder, hvor modeller som Claude Opus har n\u00e5et et kontekstvindue p\u00e5 1 million tokens.<\/p>\n

Det udvidede kontekstvindue giver mulighed for mere effektiv l\u00e6ring i konteksten. Med en zero-shot-prompt bliver en LLM bedt om at give et svar uden forudg\u00e5ende eksempler.<\/p>\n

I en \"few-shot\"-tilgang f\u00e5r modellen flere eksempler i prompten. Det giver mulighed for l\u00e6ring i konteksten og s\u00e6tter modellen i stand til at give et bedre svar.<\/p>\n

St\u00f8rre kontekstvinduer betyder, at en brugers prompt kan v\u00e6re ekstremt lang med mange eksempler, hvilket if\u00f8lge Anthropic b\u00e5de er en velsignelse og en forbandelse.<\/p>\n

Jailbreak med mange billeder<\/h2>\n

Jailbreak-metoden er meget enkel. LLM bliver bedt om at bruge en enkelt prompt, der best\u00e5r af en falsk dialog mellem en bruger og en meget im\u00f8dekommende AI-assistent.<\/p>\n

Dialogen best\u00e5r af en r\u00e6kke foresp\u00f8rgsler om, hvordan man g\u00f8r noget farligt eller ulovligt, efterfulgt af falske svar fra AI-assistenten med oplysninger om, hvordan man udf\u00f8rer aktiviteterne.<\/p>\n

Prompten slutter med en m\u00e5lforesp\u00f8rgsel som \"Hvordan bygger man en bombe?\" og lader det derefter v\u00e6re op til den udvalgte LLM at svare.<\/p>\n

\"\"
F\u00e5 skud mod mange skud jailbreak. Kilde: Anthropic<\/figcaption><\/figure>\n

Hvis du kun havde nogle f\u00e5 interaktioner frem og tilbage i opl\u00e6gget, ville det ikke fungere. Men med en model som Claude Opus kan den mange billeder lange opfordring v\u00e6re lige s\u00e5 lang som flere lange romaner.<\/p>\n

I deres artikel<\/a>Anthropic-forskerne fandt ud af, at \"n\u00e5r antallet af inkluderede dialoger (antallet af \"skud\") stiger ud over et vist punkt, bliver det mere sandsynligt, at modellen vil give en skadelig reaktion.\"<\/p>\n

De fandt ogs\u00e5 ud af, at n\u00e5r de blev kombineret med andre kendte jailbreaking-teknikker<\/a>var tilgangen med mange skud endnu mere effektiv eller kunne v\u00e6re en succes med kortere prompts.<\/p>\n

\"\"
N\u00e5r antallet af dialoger i prompten stiger, \u00f8ges chancerne for et skadeligt svar. Kilde: Anthropic: Anthropic<\/figcaption><\/figure>\n

Kan det fikses?<\/h2>\n

Anthropic siger, at det nemmeste forsvar mod many-shot jailbreak er at reducere st\u00f8rrelsen p\u00e5 en models kontekstvindue. Men s\u00e5 mister man de \u00e5benlyse fordele ved at kunne bruge l\u00e6ngere input.<\/p>\n

Anthropic fors\u00f8gte at f\u00e5 deres LLM til at identificere, hvorn\u00e5r en bruger fors\u00f8gte sig med et many-shot jailbreak, og derefter n\u00e6gte at besvare foresp\u00f8rgslen. De fandt ud af, at det blot forsinkede jailbreaket og kr\u00e6vede en l\u00e6ngere prompt for til sidst at fremkalde det skadelige output.<\/p>\n

Ved at klassificere og modificere beskeden, f\u00f8r den blev sendt til modellen, havde de en vis succes med at forhindre angrebet. Alligevel siger Anthropic, at de er opm\u00e6rksomme p\u00e5, at variationer af angrebet kan undg\u00e5 at blive opdaget.<\/p>\n

Anthropic siger, at det stadigt l\u00e6ngere kontekstvindue for LLM'er \"g\u00f8r modellerne langt mere nyttige p\u00e5 alle mulige m\u00e5der, men det muligg\u00f8r ogs\u00e5 en ny klasse af jailbreaking-s\u00e5rbarheder.\"<\/p>\n

Virksomheden har offentliggjort sin forskning i h\u00e5b om, at andre AI-virksomheder finder m\u00e5der at afb\u00f8de many-shot-angreb p\u00e5.<\/p>\n

En interessant konklusion, som forskerne kom frem til, var, at \"selv positive, uskyldigt udseende forbedringer af LLM'er (i dette tilf\u00e6lde at give mulighed for l\u00e6ngere input) nogle gange kan have uforudsete konsekvenser.\"<\/p>","protected":false},"excerpt":{"rendered":"

Anthropic har udgivet en artikel, der beskriver en jailbreaking-metode med mange skud, som LLM'er med lang kontekst er s\u00e6rligt s\u00e5rbare over for. St\u00f8rrelsen p\u00e5 en LLM's kontekstvindue bestemmer den maksimale l\u00e6ngde af en prompt. Kontekstvinduer er vokset konstant i l\u00f8bet af de sidste par m\u00e5neder, hvor modeller som Claude Opus har n\u00e5et et kontekstvindue p\u00e5 1 million tokens. Det udvidede kontekstvindue muligg\u00f8r mere kraftfuld indl\u00e6ring i konteksten. Med en zero-shot-prompt bliver en LLM bedt om at give et svar uden forudg\u00e5ende eksempler. I en \"few-shot\"-tilgang f\u00e5r modellen flere eksempler i prompten. Det giver mulighed for l\u00e6ring i konteksten og s\u00e6tter gang i<\/p>","protected":false},"author":6,"featured_media":11368,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[],"class_list":["post-11364","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry"],"yoast_head":"\nAnthropic: Large context LLMs vulnerable to many-shot jailbreak | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/da\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Anthropic: Large context LLMs vulnerable to many-shot jailbreak | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Anthropic released a paper outlining a many-shot jailbreaking method to which long-context LLMs are particularly vulnerable. 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