{"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\/es\/2024\/01\/the-nist-publishes-paper-on-four-possible-types-of-generative-ai-attacks\/","title":{"rendered":"El NIST publica un documento sobre cuatro posibles tipos de ataques a la IA generativa"},"content":{"rendered":"

El Instituto Nacional de Normas y Tecnolog\u00eda de Estados Unidos (NIST) ha expresado su preocupaci\u00f3n por la seguridad de los sistemas de IA predictiva y generativa.<\/b><\/p>\n

Seg\u00fan Apostol Vassilev, inform\u00e1tico del NIST, a pesar de los avances en seguridad, estas tecnolog\u00edas siguen siendo vulnerables a diversos ataques.<\/span><\/p>\n

En un documento de colaboraci\u00f3n<\/a> titulado \"<\/span>Aprendizaje autom\u00e1tico adversarial: Taxonom\u00eda y terminolog\u00eda de ataques y soluciones<\/span>,\" Vassilev, junto con colegas de la Universidad Northeastern y Robust Intelligence, clasifican los riesgos de seguridad que plantean los sistemas de IA.\u00a0<\/span><\/p>\n

Vassilev declar\u00f3: \"A pesar de los importantes avances de la IA y el aprendizaje autom\u00e1tico, estas tecnolog\u00edas son vulnerables a ataques que pueden provocar fallos espectaculares con consecuencias nefastas.\"\u00a0<\/span><\/p>\n

Tambi\u00e9n advirti\u00f3 contra cualquier empresa que afirme ofrecer una \"IA totalmente segura\".<\/span><\/p>\n

Esto forma parte del <\/span>Iniciativa del NIST sobre IA fiable y responsable<\/span><\/a>, en consonancia con los objetivos del gobierno estadounidense en materia de seguridad de la IA. Examina las t\u00e9cnicas de aprendizaje autom\u00e1tico adversario, centr\u00e1ndose en cuatro problemas de seguridad principales: evasi\u00f3n, envenenamiento, privacidad y ataques de abuso.<\/span><\/p>\n

Los ataques de evasi\u00f3n se producen despu\u00e9s del despliegue, alterando las entradas para confundir a los sistemas de IA. Por ejemplo, modificando las se\u00f1ales de stop para que los veh\u00edculos aut\u00f3nomos las interpreten err\u00f3neamente como se\u00f1ales de l\u00edmite de velocidad, o creando marcas de carril enga\u00f1osas para despistar a los veh\u00edculos.<\/p>\n

En los ataques de envenenamiento, se introducen datos corruptos durante el entrenamiento. Esto podr\u00eda implicar la incrustaci\u00f3n de lenguaje inapropiado frecuente en los conjuntos de datos de entrenamiento, lo que llevar\u00eda a un chatbot a adoptar este lenguaje en las interacciones con los clientes.<\/p>\n

Los ataques a la privacidad pretenden extraer informaci\u00f3n sensible sobre la IA o sus datos de entrenamiento, a menudo mediante m\u00e9todos de ingenier\u00eda inversa. Esto puede implicar el uso de las respuestas de un chatbot para discernir sus fuentes de entrenamiento y sus puntos d\u00e9biles.<\/p>\n

Los ataques de abuso manipulan fuentes leg\u00edtimas, como p\u00e1ginas web, alimentando los sistemas de IA con informaci\u00f3n falsa para alterar su funcionamiento. Esto difiere de los ataques de envenenamiento, que corrompen el propio proceso de entrenamiento.<\/p>\n

Los ataques de evasi\u00f3n consisten en crear ejemplos adversos para enga\u00f1ar a los sistemas de IA durante su despliegue, como la identificaci\u00f3n err\u00f3nea de se\u00f1ales de stop en veh\u00edculos aut\u00f3nomos.\u00a0<\/span><\/p>\n

Alina Oprea, de la Northeastern University, que particip\u00f3 en la estudiar<\/a>, <\/span>explic\u00f3: \"La mayor\u00eda de estos ataques son bastante f\u00e1ciles de montar y requieren un conocimiento m\u00ednimo del sistema de IA y capacidades adversarias limitadas\".<\/p>\n

Cr\u00edticas al NIST por sus v\u00ednculos con un grupo de expertos en IA<\/h2>\n

Por separado, <\/span>se han planteado inquietudes<\/span><\/a> sobre un proyecto de asociaci\u00f3n entre el NIST y RAND Corp. para investigar la IA.<\/span><\/p>\n

RAND, conocida por sus v\u00ednculos con multimillonarios de la tecnolog\u00eda y la <\/span>movimiento altruista eficaz<\/span><\/a>ha desempe\u00f1ado un importante papel consultivo en la configuraci\u00f3n de la<\/span> orden ejecutiva sobre seguridad de la IA<\/span><\/a>.\u00a0<\/span><\/p>\n

Miembros de la Comisi\u00f3n de Ciencia, Espacio y Tecnolog\u00eda de la C\u00e1mara de Representantes, entre ellos Frank Lucas y Zoe Lofgren, criticaron la falta de transparencia de esta asociaci\u00f3n.\u00a0<\/span><\/p>\n

El comit\u00e9 tiene dos preocupaciones: En primer lugar, se preguntan por qu\u00e9 no hubo un proceso competitivo para seleccionar a RAND para esta investigaci\u00f3n sobre la seguridad de la IA.\u00a0<\/span><\/p>\n

Normalmente, cuando las agencias gubernamentales como el NIST conceden becas de investigaci\u00f3n, abren la posibilidad de que diferentes organizaciones presenten solicitudes, garantizando un proceso de selecci\u00f3n justo. Pero en este caso, parece que RAND fue elegida sin tal proceso.<\/span><\/p>\n

En segundo lugar, existe cierto malestar por el hecho de que el RAND se centre en la investigaci\u00f3n de la IA. RAND ha participado en estudios sobre IA y bioseguridad y recientemente ha recibido importantes fondos para este trabajo de fuentes estrechamente vinculadas a la industria tecnol\u00f3gica.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

El Instituto Nacional de Normas y Tecnolog\u00eda de Estados Unidos (NIST) ha expresado su preocupaci\u00f3n por la seguridad de los sistemas de IA predictiva y generativa. Seg\u00fan Apostol Vassilev, inform\u00e1tico del NIST, a pesar de los avances en seguridad, estas tecnolog\u00edas siguen siendo vulnerables a diversos ataques. En un documento de colaboraci\u00f3n titulado \"Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations\", Vassilev, junto con colegas de la Northeastern University y Robust Intelligence, clasifica los riesgos de seguridad que plantean los sistemas de IA. Vassilev declar\u00f3: \"A pesar de los importantes avances de la IA y el aprendizaje autom\u00e1tico, estas tecnolog\u00edas son vulnerables a ataques que pueden provocar fallos 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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