{"id":8944,"date":"2024-01-06T20:37:14","date_gmt":"2024-01-06T20:37:14","guid":{"rendered":"https:\/\/dailyai.com\/?p=8944"},"modified":"2024-01-06T23:30:16","modified_gmt":"2024-01-06T23:30:16","slug":"the-nist-publishes-paper-on-four-possible-types-of-generative-ai-attacks","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/01\/the-nist-publishes-paper-on-four-possible-types-of-generative-ai-attacks\/","title":{"rendered":"O NIST publica um documento sobre quatro tipos poss\u00edveis de ataques de IA generativa"},"content":{"rendered":"

O Instituto Nacional de Normas e Tecnologia (NIST) dos EUA manifestou a sua preocupa\u00e7\u00e3o com a seguran\u00e7a dos sistemas de IA preditiva e generativa.<\/b><\/p>\n

De acordo com Apostol Vassilev, um cientista inform\u00e1tico do NIST, apesar dos avan\u00e7os na seguran\u00e7a, estas tecnologias continuam a ser vulner\u00e1veis a uma variedade de ataques.<\/span><\/p>\n

Num documento colaborativo<\/a> intitulado \"<\/span>Aprendizagem autom\u00e1tica adversarial: Uma Taxonomia e Terminologia de Ataques e Mitiga\u00e7\u00f5es<\/span>,\" Vassilev, juntamente com colegas da Northeastern University e da Robust Intelligence, categorizam os riscos de seguran\u00e7a colocados pelos sistemas de IA.\u00a0<\/span><\/p>\n

Vassilev declarou: \"Apesar dos progressos significativos que a IA e a aprendizagem autom\u00e1tica t\u00eam feito, estas tecnologias s\u00e3o vulner\u00e1veis a ataques que podem causar falhas espectaculares com consequ\u00eancias terr\u00edveis\".\u00a0<\/span><\/p>\n

Tamb\u00e9m advertiu contra qualquer empresa que afirme oferecer \"IA totalmente segura\".<\/span><\/p>\n

Isto faz parte do <\/span>Iniciativa de IA fi\u00e1vel e respons\u00e1vel do NIST<\/span><\/a>alinhando com os objectivos do governo dos EUA para a seguran\u00e7a da IA. Examina t\u00e9cnicas de aprendizagem autom\u00e1tica advers\u00e1rias, centrando-se em quatro principais preocupa\u00e7\u00f5es de seguran\u00e7a: evas\u00e3o, envenenamento, privacidade e ataques abusivos.<\/span><\/p>\n

Os ataques de evas\u00e3o ocorrem ap\u00f3s a implementa\u00e7\u00e3o, alterando as entradas para confundir os sistemas de IA. Por exemplo, a modifica\u00e7\u00e3o de sinais de paragem para que os ve\u00edculos aut\u00f3nomos os interpretem erradamente como sinais de limite de velocidade ou a cria\u00e7\u00e3o de marcas de faixa enganadoras para desviar os ve\u00edculos.<\/p>\n

Nos ataques de envenenamento, s\u00e3o introduzidos dados corrompidos durante o treino. Isto pode envolver a incorpora\u00e7\u00e3o de linguagem inadequada frequente em conjuntos de dados de treino, levando um chatbot a adotar essa linguagem nas interac\u00e7\u00f5es com os clientes.<\/p>\n

Os ataques \u00e0 privacidade t\u00eam como objetivo extrair informa\u00e7\u00f5es sens\u00edveis sobre a IA ou os seus dados de treino, muitas vezes atrav\u00e9s de m\u00e9todos de engenharia inversa. Isto pode envolver a utiliza\u00e7\u00e3o das respostas de um chatbot para discernir as suas fontes de forma\u00e7\u00e3o e os seus pontos fracos.<\/p>\n

Os ataques de abuso manipulam fontes leg\u00edtimas, como p\u00e1ginas Web, alimentando os sistemas de IA com informa\u00e7\u00f5es falsas para alterar o seu funcionamento. Isto difere dos ataques de envenenamento, que corrompem o pr\u00f3prio processo de forma\u00e7\u00e3o.<\/p>\n

Os ataques de evas\u00e3o envolvem a cria\u00e7\u00e3o de exemplos advers\u00e1rios para enganar os sistemas de IA durante a implementa\u00e7\u00e3o, como a identifica\u00e7\u00e3o errada de sinais de stop em ve\u00edculos aut\u00f3nomos.\u00a0<\/span><\/p>\n

Alina Oprea, da Northeastern University, que esteve envolvida no projeto estudo<\/a>, <\/span>explicou: \"A maior parte destes ataques s\u00e3o bastante f\u00e1ceis de montar e requerem um conhecimento m\u00ednimo do sistema de IA e capacidades advers\u00e1rias limitadas\".<\/p>\n

NIST criticado por liga\u00e7\u00f5es a grupo de reflex\u00e3o sobre IA<\/h2>\n

Separadamente, <\/span>foram manifestadas preocupa\u00e7\u00f5es<\/span><\/a> sobre uma planeada parceria de investiga\u00e7\u00e3o em IA entre o NIST e a RAND Corp.<\/span><\/p>\n

A RAND, conhecida pelas suas liga\u00e7\u00f5es a bilion\u00e1rios da tecnologia e \u00e0 <\/span>movimento de altru\u00edsmo efetivo<\/span><\/a>desempenhou um papel consultivo significativo na elabora\u00e7\u00e3o do<\/span> Ordem executiva relativa \u00e0 seguran\u00e7a da IA<\/span><\/a>.\u00a0<\/span><\/p>\n

Os membros da Comiss\u00e3o de Ci\u00eancia, Espa\u00e7o e Tecnologia da C\u00e2mara dos Representantes, incluindo Frank Lucas e Zoe Lofgren, criticaram a falta de transpar\u00eancia desta parceria.\u00a0<\/span><\/p>\n

As preocupa\u00e7\u00f5es da comiss\u00e3o s\u00e3o de duas ordens: Em primeiro lugar, questionam o facto de n\u00e3o ter havido um processo competitivo para selecionar a RAND para esta investiga\u00e7\u00e3o sobre seguran\u00e7a da IA.\u00a0<\/span><\/p>\n

Normalmente, quando ag\u00eancias governamentais como o NIST concedem bolsas de investiga\u00e7\u00e3o, abrem a oportunidade a diferentes organiza\u00e7\u00f5es de se candidatarem, garantindo um processo de sele\u00e7\u00e3o justo. Mas, neste caso, parece que a RAND foi escolhida sem esse processo.<\/span><\/p>\n

Em segundo lugar, existe alguma inquieta\u00e7\u00e3o quanto ao facto de o RAND se concentrar na investiga\u00e7\u00e3o sobre a IA. A RAND tem estado envolvida em estudos sobre a IA e a biosseguran\u00e7a e recebeu recentemente um financiamento significativo para este trabalho de fontes estreitamente ligadas \u00e0 ind\u00fastria tecnol\u00f3gica.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

O Instituto Nacional de Normas e Tecnologia (NIST) dos EUA manifestou a sua preocupa\u00e7\u00e3o quanto \u00e0 seguran\u00e7a dos sistemas de IA preditiva e generativa. De acordo com Apostol Vassilev, um cientista inform\u00e1tico do NIST, apesar dos avan\u00e7os na seguran\u00e7a, estas tecnologias continuam vulner\u00e1veis a uma variedade de ataques. Num documento de colabora\u00e7\u00e3o intitulado \"Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations\", Vassilev, juntamente com colegas da Northeastern University e da Robust Intelligence, categorizam os riscos de seguran\u00e7a colocados pelos sistemas de IA. Vassilev afirmou: \"Apesar dos progressos significativos que a IA e a aprendizagem autom\u00e1tica t\u00eam feito, estas tecnologias s\u00e3o vulner\u00e1veis a ataques que podem causar falhas espectaculares<\/p>","protected":false},"author":2,"featured_media":8945,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[512],"class_list":["post-8944","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-nist"],"yoast_head":"\nThe NIST publishes paper on four possible types of generative AI attacks | DailyAI<\/title>\n<meta name=\"description\" content=\"The US National Institute of Standards and Technology (NIST) has released a new paper on the risks of generative AI.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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