{"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\/it\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/","title":{"rendered":"Antropico: Gli LLM di grande contesto sono vulnerabili al jailbreak di molti colpi"},"content":{"rendered":"

Anthropic ha pubblicato un documento che illustra un metodo di jailbreak a pi\u00f9 colpi a cui gli LLM a contesto lungo sono particolarmente vulnerabili.<\/strong><\/p>\n

La dimensione della finestra di contesto di un LLM determina la lunghezza massima di un prompt. Le finestre di contesto sono cresciute costantemente negli ultimi mesi e modelli come Claude Opus hanno raggiunto una finestra di contesto di 1 milione di token.<\/p>\n

La finestra di contesto ampliata rende possibile un apprendimento contestuale pi\u00f9 potente. Con un prompt a zero colpi, un LLM \u00e8 chiamato a fornire una risposta senza esempi precedenti.<\/p>\n

In un approccio a pochi colpi, al modello vengono forniti diversi esempi nel prompt. Ci\u00f2 consente l'apprendimento nel contesto e stimola il modello a dare una risposta migliore.<\/p>\n

Le finestre contestuali pi\u00f9 grandi consentono all'utente di avere un prompt estremamente lungo con molti esempi, il che, secondo Anthropic, \u00e8 sia una benedizione che una maledizione.<\/p>\n

jailbreak a pi\u00f9 riprese<\/h2>\n

Il metodo di jailbreak \u00e8 estremamente semplice. L'LLM viene sollecitato con un singolo prompt composto da un finto dialogo tra un utente e un assistente AI molto accomodante.<\/p>\n

Il dialogo comprende una serie di domande su come fare qualcosa di pericoloso o illegale, seguite da false risposte dell'assistente AI con informazioni su come eseguire le attivit\u00e0.<\/p>\n

Il prompt termina con una domanda mirata, come \"Come si costruisce una bomba?\", e poi lascia che sia il LLM a rispondere.<\/p>\n

\"\"
jailbreak a pochi colpi e jailbreak a molti colpi. Fonte: Antropico<\/figcaption><\/figure>\n

Se il prompt prevedeva solo poche interazioni tra i personaggi, non avrebbe funzionato. Ma con un modello come Claude Opus, la richiesta di molti colpi pu\u00f2 essere lunga come diversi romanzi lunghi.<\/p>\n

Nel loro documento<\/a>I ricercatori di Anthropic hanno scoperto che \"quando il numero di dialoghi inclusi (il numero di \"colpi\") aumenta oltre un certo punto, diventa pi\u00f9 probabile che il modello produca una risposta dannosa\".<\/p>\n

Hanno anche scoperto che, in combinazione con altri tecniche di jailbreak<\/a>L'approccio a pi\u00f9 colpi si \u00e8 rivelato ancora pi\u00f9 efficace o potrebbe avere successo con richieste pi\u00f9 brevi.<\/p>\n

\"\"
All'aumentare del numero di dialoghi nel prompt, aumentano le probabilit\u00e0 di una risposta dannosa. Fonte: Antropico<\/figcaption><\/figure>\n

Si pu\u00f2 rimediare?<\/h2>\n

Anthropic dice che la difesa pi\u00f9 semplice contro il jailbreak a molti colpi \u00e8 ridurre le dimensioni della finestra contestuale di un modello. Ma cos\u00ec si perdono gli ovvi vantaggi di poter usare input pi\u00f9 lunghi.<\/p>\n

Anthropic ha provato a far s\u00ec che il suo LLM identificasse quando un utente stava tentando un jailbreak con molti colpi e quindi si rifiutasse di rispondere alla domanda. Hanno scoperto che questo ritardava semplicemente il jailbreak e richiedeva una richiesta pi\u00f9 lunga per ottenere l'output dannoso.<\/p>\n

Classificando e modificando il prompt prima di passarlo al modello, hanno avuto un certo successo nel prevenire l'attacco. Tuttavia, Anthropic afferma di essere consapevole che variazioni dell'attacco potrebbero eludere il rilevamento.<\/p>\n

Secondo Anthropic, la finestra di contesto sempre pi\u00f9 ampia degli LLM \"rende i modelli molto pi\u00f9 utili in tutti i modi, ma rende anche possibile una nuova classe di vulnerabilit\u00e0 per il jailbreak\".<\/p>\n

L'azienda ha pubblicato la sua ricerca nella speranza che altre aziende di intelligenza artificiale trovino il modo di mitigare gli attacchi a pi\u00f9 colpi.<\/p>\n

Una conclusione interessante a cui sono giunti i ricercatori \u00e8 che \"anche i miglioramenti positivi e apparentemente innocui degli LLM (in questo caso, la possibilit\u00e0 di inserire input pi\u00f9 lunghi) possono talvolta avere conseguenze impreviste\".<\/p>","protected":false},"excerpt":{"rendered":"

Anthropic ha pubblicato un documento che illustra un metodo di jailbreak a pi\u00f9 colpi a cui gli LLM a contesto lungo sono particolarmente vulnerabili. La dimensione della finestra di contesto di un LLM determina la lunghezza massima di un prompt. Le finestre di contesto sono cresciute costantemente negli ultimi mesi e modelli come Claude Opus hanno raggiunto una finestra di contesto di 1 milione di token. L'ampliamento della finestra di contesto rende possibile un apprendimento in contesto pi\u00f9 potente. Con un prompt a zero colpi, un LLM viene sollecitato a fornire una risposta senza esempi precedenti. In un approccio a pochi colpi, il modello riceve diversi esempi nel prompt. Ci\u00f2 consente l'apprendimento in contesto e d\u00e0 origine a un'esperienza di apprendimento<\/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\/it\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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