{"id":13539,"date":"2024-07-22T10:04:27","date_gmt":"2024-07-22T10:04:27","guid":{"rendered":"https:\/\/dailyai.com\/?p=13539"},"modified":"2024-07-22T10:04:27","modified_gmt":"2024-07-22T10:04:27","slug":"llm-refusal-training-easily-bypassed-with-past-tense-prompts","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/07\/llm-refusal-training-easily-bypassed-with-past-tense-prompts\/","title":{"rendered":"A forma\u00e7\u00e3o de recusa de LLM \u00e9 facilmente contornada com avisos no passado"},"content":{"rendered":"

Os investigadores da Escola Polit\u00e9cnica Federal de Lausanne (EPFL) descobriram que a escrita de pedidos perigosos no pret\u00e9rito perfeito ultrapassa o treino de recusa dos LLM mais avan\u00e7ados.<\/strong><\/p>\n

Os modelos de IA s\u00e3o normalmente alinhados atrav\u00e9s de t\u00e9cnicas como a afina\u00e7\u00e3o supervisionada (SFT) ou o feedback humano da aprendizagem por refor\u00e7o (RLHF) para garantir que o modelo n\u00e3o responde a solicita\u00e7\u00f5es perigosas ou indesej\u00e1veis.<\/p>\n

Este treino de recusa entra em a\u00e7\u00e3o quando pedes conselhos ao ChatGPT sobre como fazer uma bomba ou drogas. Cobrimos uma s\u00e9rie de t\u00e9cnicas de jailbreak interessantes<\/a> que contornam estas barreiras de prote\u00e7\u00e3o, mas o m\u00e9todo que os investigadores da EPFL testaram \u00e9 de longe o mais simples.<\/p>\n

Os investigadores pegaram num conjunto de dados de 100 comportamentos nocivos e utilizaram o GPT-3.5 para reescrever os avisos no passado.<\/p>\n

Aqui est\u00e1 um exemplo do m\u00e9todo explicado em o seu trabalho<\/a>.<\/p>\n

\"\"
Usando um LLM para reescrever avisos perigosos no passado. Fonte: arXiv<\/figcaption><\/figure>\n

De seguida, avaliaram as respostas a estes pedidos reescritos por estes 8 LLMs: Llama-3 8B, Claude-3.5 Soneto, GPT-3.5 Turbo, Gemma-2 9B, Phi-3-Mini, GPT-4o-mini<\/a>GPT-4o, e R2D2.<\/p>\n

Utilizaram v\u00e1rios LLMs para avaliar os resultados e classific\u00e1-los como uma tentativa de fuga \u00e0 pris\u00e3o falhada ou bem sucedida.<\/p>\n

A simples mudan\u00e7a do tempo verbal do prompt teve um efeito surpreendentemente significativo na taxa de sucesso do ataque (ASR). O GPT-4o e o GPT-4o mini eram especialmente suscept\u00edveis a esta t\u00e9cnica.<\/p>\n

O ASR deste \"simples ataque ao GPT-4o aumenta de 1% usando pedidos directos para 88% usando 20 tentativas de reformula\u00e7\u00e3o do pret\u00e9rito em pedidos prejudiciais\".<\/p>\n

Aqui est\u00e1 um exemplo de como o GPT-4o se torna compat\u00edvel quando simplesmente reescrevemos o prompt no passado. Usei o ChatGPT para isso e a vulnerabilidade ainda n\u00e3o foi corrigida.<\/p>\n

\"\"
O ChatGPT que usa o GPT-4o recusa um pedido no tempo presente, mas cumpre-o quando \u00e9 reescrito no tempo passado. Fonte: ChatGPT<\/figcaption><\/figure>\n

O treino de recusa utilizando RLHF e SFT treina um modelo para generalizar com sucesso a rejei\u00e7\u00e3o de avisos prejudiciais, mesmo que nunca tenha visto o aviso espec\u00edfico antes.<\/p>\n

Quando a pergunta \u00e9 escrita no passado, os LLMs parecem perder a capacidade de generalizar. Os outros LLMs n\u00e3o se sa\u00edram muito melhor do que GPT-4o, embora Llama-3 8B tenha parecido mais resistente.<\/p>\n

\"\"
Taxas de sucesso de ataques usando prompts perigosos no presente e no passado. Fonte: arXiv<\/figcaption><\/figure>\n

A reescrita do pedido no tempo futuro registou um aumento da ASR, mas foi menos eficaz do que o pedido no tempo passado.<\/p>\n

Os investigadores conclu\u00edram que tal poderia dever-se ao facto de \"os conjuntos de dados de afina\u00e7\u00e3o poderem conter uma maior propor\u00e7\u00e3o de pedidos prejudiciais expressos no tempo futuro ou como eventos hipot\u00e9ticos\".<\/p>\n

Sugeriram tamb\u00e9m que \"o racioc\u00ednio interno do modelo pode interpretar os pedidos orientados para o futuro como potencialmente mais prejudiciais, enquanto as declara\u00e7\u00f5es no passado, como os acontecimentos hist\u00f3ricos, podem ser vistas como mais benignas\".<\/p>\n

Pode ser corrigido?<\/h2>\n

Experi\u00eancias posteriores demonstraram que a adi\u00e7\u00e3o de pedidos no pret\u00e9rito perfeito aos conjuntos de dados de afina\u00e7\u00e3o reduziu efetivamente a suscetibilidade a esta t\u00e9cnica de fuga \u00e0 pris\u00e3o.<\/p>\n

Embora eficaz, esta abordagem exige que se antecipem os tipos de avisos perigosos que um utilizador pode introduzir.<\/p>\n

Os investigadores sugerem que avaliar o resultado de um modelo antes de este ser apresentado ao utilizador \u00e9 uma solu\u00e7\u00e3o mais f\u00e1cil.<\/p>\n

Por mais simples que este jailbreak seja, parece que as principais empresas de IA ainda n\u00e3o encontraram uma maneira de corrigi-lo.<\/p>","protected":false},"excerpt":{"rendered":"

Os investigadores da Escola Polit\u00e9cnica Federal de Lausanne (EPFL) descobriram que a escrita de mensagens perigosas no pret\u00e9rito contornava o treino de recusa dos LLM mais avan\u00e7ados. Os modelos de IA s\u00e3o normalmente alinhados atrav\u00e9s de t\u00e9cnicas como a afina\u00e7\u00e3o supervisionada (SFT) ou o feedback humano da aprendizagem por refor\u00e7o (RLHF) para garantir que o modelo n\u00e3o responde a mensagens perigosas ou indesej\u00e1veis. Este treino de recusa entra em a\u00e7\u00e3o quando se pede conselhos ao ChatGPT sobre como fazer uma bomba ou drogas. J\u00e1 abord\u00e1mos uma s\u00e9rie de t\u00e9cnicas interessantes de jailbreak que contornam estas barreiras, mas o m\u00e9todo que os investigadores da EPFL testaram \u00e9 de longe o mais simples.<\/p>","protected":false},"author":6,"featured_media":13544,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,118],"class_list":["post-13539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-llms"],"yoast_head":"\nLLM refusal training easily bypassed with past tense prompts | 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\/07\/llm-refusal-training-easily-bypassed-with-past-tense-prompts\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLM refusal training easily bypassed with past tense prompts | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from the Swiss Federal Institute of Technology Lausanne (EPFL) found that writing dangerous prompts in the past tense bypassed the refusal training of the most advanced LLMs. AI models are commonly aligned using techniques like supervised fine-tuning (SFT) or reinforcement learning human feedback (RLHF) to make sure the model doesn\u2019t respond to dangerous or undesirable prompts. 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