{"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\/sv\/2024\/01\/the-nist-publishes-paper-on-four-possible-types-of-generative-ai-attacks\/","title":{"rendered":"NIST publicerar dokument om fyra m\u00f6jliga typer av generativa AI-attacker"},"content":{"rendered":"

USA:s National Institute of Standards and Technology (NIST) har uttryckt oro f\u00f6r s\u00e4kerheten i prediktiva och generativa AI-system.<\/b><\/p>\n

Enligt Apostol Vassilev, datavetare vid NIST, \u00e4r dessa tekniker trots s\u00e4kerhetsframsteg fortfarande s\u00e5rbara f\u00f6r en rad olika attacker.<\/span><\/p>\n

I en samarbetsdokument<\/a> titeln \"<\/span>Adversarial maskininl\u00e4rning: En taxonomi och terminologi f\u00f6r attacker och begr\u00e4nsnings\u00e5tg\u00e4rder<\/span>\" Vassilev, tillsammans med kollegor fr\u00e5n Northeastern University och Robust Intelligence, kategoriserar de s\u00e4kerhetsrisker som AI-system utg\u00f6r.\u00a0<\/span><\/p>\n

Vassilev konstaterade: \"Trots de betydande framsteg som AI och maskininl\u00e4rning har gjort \u00e4r dessa tekniker s\u00e5rbara f\u00f6r attacker som kan orsaka spektakul\u00e4ra misslyckanden med allvarliga konsekvenser.\"\u00a0<\/span><\/p>\n

Han varnade ocks\u00e5 f\u00f6r alla f\u00f6retag som p\u00e5st\u00e5r sig erbjuda \"helt s\u00e4ker AI\".<\/span><\/p>\n

Detta \u00e4r en del av <\/span>NIST:s initiativ f\u00f6r p\u00e5litlig och ansvarsfull AI<\/span><\/a>, i linje med den amerikanska regeringens m\u00e5l f\u00f6r AI-s\u00e4kerhet. Den unders\u00f6ker kontradiktoriska maskininl\u00e4rningstekniker med fokus p\u00e5 fyra huvudsakliga s\u00e4kerhetsproblem: undvikande, f\u00f6rgiftning, integritet och missbruksattacker.<\/span><\/p>\n

Undvikande attacker sker efter utplacering och \u00e4ndrar indata f\u00f6r att f\u00f6rvirra AI-system. Det kan till exempel handla om att \u00e4ndra stoppskyltar s\u00e5 att autonoma fordon missuppfattar dem som skyltar f\u00f6r hastighetsgr\u00e4nser, eller att skapa vilseledande k\u00f6rf\u00e4ltsmarkeringar f\u00f6r att leda fordon p\u00e5 villov\u00e4gar.<\/p>\n

Vid f\u00f6rgiftningsattacker inf\u00f6rs korrupta data under utbildningen. Det kan handla om att b\u00e4dda in frekvent ol\u00e4mpligt spr\u00e5k i tr\u00e4ningsdataset, vilket leder till att en chatbot anv\u00e4nder detta spr\u00e5k i kundinteraktioner.<\/p>\n

Sekretessattacker syftar till att extrahera k\u00e4nslig information om AI eller dess tr\u00e4ningsdata, ofta genom metoder f\u00f6r omv\u00e4nd ingenj\u00f6rskonst. Detta kan inneb\u00e4ra att man anv\u00e4nder en chatbots svar f\u00f6r att urskilja dess tr\u00e4ningsk\u00e4llor och svagheter.<\/p>\n

Missbruksattacker manipulerar legitima k\u00e4llor, t.ex. webbsidor, och matar AI-system med falsk information f\u00f6r att \u00e4ndra deras funktion. Detta skiljer sig fr\u00e5n f\u00f6rgiftningsattacker, som korrumperar sj\u00e4lva tr\u00e4ningsprocessen.<\/p>\n

Undvikande attacker inneb\u00e4r att man skapar kontradiktoriska exempel f\u00f6r att lura AI-system under drifts\u00e4ttning, som att felidentifiera stoppskyltar i autonoma fordon.\u00a0<\/span><\/p>\n

Alina Oprea fr\u00e5n Northeastern University, som var involverad i studie<\/a>, <\/span>\"De flesta av dessa attacker \u00e4r ganska l\u00e4tta att genomf\u00f6ra och kr\u00e4ver minimal kunskap om AI-systemet och begr\u00e4nsad motst\u00e5ndskapacitet.\"<\/p>\n

NIST kritiseras f\u00f6r l\u00e4nkar till AI-tankesmedja<\/h2>\n

Separat, <\/span>farh\u00e5gor har framf\u00f6rts<\/span><\/a> om ett planerat AI-forskningssamarbete mellan NIST och RAND Corp.<\/span><\/p>\n

RAND, som \u00e4r k\u00e4nt f\u00f6r sina band till teknikmiljard\u00e4rer och <\/span>r\u00f6relsen f\u00f6r effektiv altruism<\/span><\/a>har haft en viktig r\u00e5dgivande roll i utformningen av<\/span> F\u00f6rordning om AI-s\u00e4kerhet<\/span><\/a>.\u00a0<\/span><\/p>\n

Medlemmar i representanthusets kommitt\u00e9 f\u00f6r vetenskap, rymd och teknik, d\u00e4ribland Frank Lucas och Zoe Lofgren, kritiserade bristen p\u00e5 transparens i detta partnerskap.\u00a0<\/span><\/p>\n

Utskottets oro \u00e4r tv\u00e5faldig: F\u00f6r det f\u00f6rsta ifr\u00e5gas\u00e4tter de varf\u00f6r det inte fanns en konkurrensutsatt process f\u00f6r att v\u00e4lja RAND f\u00f6r denna AI-s\u00e4kerhetsforskning.\u00a0<\/span><\/p>\n

N\u00e4r statliga myndigheter som NIST ger forskningsanslag \u00f6ppnar de vanligtvis upp m\u00f6jligheten f\u00f6r olika organisationer att ans\u00f6ka, vilket s\u00e4kerst\u00e4ller en r\u00e4ttvis urvalsprocess. Men i det h\u00e4r fallet verkar det som om RAND valdes ut utan en s\u00e5dan process.<\/span><\/p>\n

F\u00f6r det andra finns det en viss oro \u00f6ver RAND: s fokus p\u00e5 AI-forskning. RAND har varit involverat i studier om AI och bios\u00e4kerhet och har nyligen f\u00e5tt betydande finansiering f\u00f6r detta arbete fr\u00e5n k\u00e4llor med n\u00e4ra koppling till teknikindustrin.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

USA:s National Institute of Standards and Technology (NIST) har uttryckt oro f\u00f6r s\u00e4kerheten i prediktiva och generativa AI-system. Enligt Apostol Vassilev, datavetare vid NIST, \u00e4r dessa tekniker trots s\u00e4kerhetsframsteg fortfarande s\u00e5rbara f\u00f6r en rad olika attacker. I ett samarbetspapper med titeln \"Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations\" kategoriserar Vassilev, tillsammans med kollegor fr\u00e5n Northeastern University och Robust Intelligence, de s\u00e4kerhetsrisker som AI-system utg\u00f6r. Vassilev konstaterade: \"Trots de betydande framsteg som AI och maskininl\u00e4rning har gjort \u00e4r dessa tekniker s\u00e5rbara f\u00f6r attacker som kan orsaka spektakul\u00e4ra misslyckanden<\/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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