{"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\/pt\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/","title":{"rendered":"Antr\u00f3pico: LLMs de grande contexto vulner\u00e1veis \u00e0 fuga de muitos tiros"},"content":{"rendered":"

A Anthropic publicou um artigo que descreve um m\u00e9todo de jailbreak com muitos tiros, ao qual os LLMs de contexto longo s\u00e3o particularmente vulner\u00e1veis.<\/strong><\/p>\n

O tamanho da janela de contexto de um LLM determina o comprimento m\u00e1ximo de um prompt. As janelas de contexto t\u00eam vindo a aumentar de forma consistente nos \u00faltimos meses, com modelos como o Claude Opus a atingir uma janela de contexto de 1 milh\u00e3o de tokens.<\/p>\n

A janela de contexto alargada torna poss\u00edvel uma aprendizagem em contexto mais poderosa. Com um prompt de disparo zero, um LLM \u00e9 solicitado a dar uma resposta sem exemplos anteriores.<\/p>\n

Numa abordagem de poucas tentativas, o modelo recebe v\u00e1rios exemplos no prompt. Isto permite a aprendizagem no contexto e prepara o modelo para dar uma resposta melhor.<\/p>\n

Janelas de contexto maiores significam que o prompt de um utilizador pode ser extremamente longo, com muitos exemplos, o que, segundo o Anthropic, \u00e9 tanto uma b\u00ean\u00e7\u00e3o como uma maldi\u00e7\u00e3o.<\/p>\n

Fuga da pris\u00e3o com muitos tiros<\/h2>\n

O m\u00e9todo de jailbreak \u00e9 extremamente simples. O LLM \u00e9 solicitado com um \u00fanico prompt composto por um di\u00e1logo falso entre um utilizador e um assistente de IA muito complacente.<\/p>\n

O di\u00e1logo inclui uma s\u00e9rie de perguntas sobre como fazer algo perigoso ou ilegal, seguidas de respostas falsas do assistente de IA com informa\u00e7\u00f5es sobre como realizar as actividades.<\/p>\n

O prompt termina com uma pergunta-alvo como \"Como construir uma bomba?\" e deixa a resposta para o LLM visado.<\/p>\n

\"\"
Poucos tiros vs muitos tiros jailbreak. Fonte: Anthropic<\/figcaption><\/figure>\n

Se apenas tivermos algumas interac\u00e7\u00f5es de ida e volta no prompt, n\u00e3o funciona. Mas com um modelo como o Claude Opus, o prompt de muitas fotografias pode ser t\u00e3o longo como v\u00e1rios romances.<\/p>\n

No seu documento<\/a>Os investigadores do Anthropic descobriram que \"\u00e0 medida que o n\u00famero de di\u00e1logos inclu\u00eddos (o n\u00famero de \"tiros\") aumenta para al\u00e9m de um certo ponto, torna-se mais prov\u00e1vel que o modelo produza uma resposta prejudicial\".<\/p>\n

Descobriram tamb\u00e9m que, quando combinado com outros t\u00e9cnicas de jailbreaking<\/a>A abordagem de muitas tentativas foi ainda mais eficaz ou poderia ser bem sucedida com pedidos mais curtos.<\/p>\n

\"\"
\u00c0 medida que o n\u00famero de di\u00e1logos no prompt aumenta, as probabilidades de uma resposta prejudicial aumentam. Fonte: Antr\u00f3pico<\/figcaption><\/figure>\n

Pode ser corrigido?<\/h2>\n

Anthropic diz que a defesa mais f\u00e1cil contra o jailbreak de muitos tiros \u00e9 reduzir o tamanho da janela de contexto de um modelo. Mas ent\u00e3o perde-se os benef\u00edcios \u00f3bvios de poder usar entradas mais longas.<\/p>\n

A Anthropic tentou fazer com que o seu LLM identificasse quando um utilizador estava a tentar um jailbreak com muitos disparos e depois recusasse responder \u00e0 quest\u00e3o. Eles descobriram que isso simplesmente atrasou o jailbreak e exigiu um prompt mais longo para eventualmente obter a sa\u00edda prejudicial.<\/p>\n

Ao classificar e modificar a mensagem antes de a passar para o modelo, conseguiram evitar o ataque. Mesmo assim, a Anthropic diz estar ciente de que varia\u00e7\u00f5es do ataque podem escapar \u00e0 dete\u00e7\u00e3o.<\/p>\n

O Anthropic diz que a janela de contexto cada vez mais alargada dos LLM \"torna os modelos muito mais \u00fateis em todo o tipo de situa\u00e7\u00f5es, mas tamb\u00e9m torna vi\u00e1vel uma nova classe de vulnerabilidades de fuga \u00e0 pris\u00e3o\".<\/p>\n

A empresa publicou a sua investiga\u00e7\u00e3o na esperan\u00e7a de que outras empresas de IA encontrem formas de atenuar os ataques com muitos tiros.<\/p>\n

Uma conclus\u00e3o interessante a que os investigadores chegaram foi que \"mesmo as melhorias positivas e aparentemente in\u00f3cuas dos LLM (neste caso, permitir entradas mais longas) podem por vezes ter consequ\u00eancias imprevistas\".<\/p>","protected":false},"excerpt":{"rendered":"

A Anthropic publicou um artigo descrevendo um m\u00e9todo de jailbreak com muitas tentativas, ao qual os LLMs de contexto longo s\u00e3o particularmente vulner\u00e1veis. O tamanho da janela de contexto de um LLM determina o comprimento m\u00e1ximo de um prompt. As janelas de contexto t\u00eam vindo a aumentar consistentemente nos \u00faltimos meses, com modelos como o Claude Opus a atingirem uma janela de contexto de 1 milh\u00e3o de tokens. A janela de contexto alargada torna poss\u00edvel uma aprendizagem em contexto mais poderosa. Com um prompt de disparo zero, um LLM \u00e9 solicitado a fornecer uma resposta sem exemplos anteriores. Numa abordagem de poucas tentativas, o modelo recebe v\u00e1rios exemplos na solicita\u00e7\u00e3o. Isto permite a aprendizagem em contexto e prepara<\/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\/pt\/2024\/04\/anthropic-large-context-llms-vulnerable-to-many-shot-jailbreak\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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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