{"id":113971,"date":"2024-02-29T18:25:41","date_gmt":"2024-02-29T16:25:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/v-repozitorii-hugging-face-vyyavleny-vredonosnye-ai-modeli-vypolnyayushhie-kod"},"modified":"2024-02-29T18:25:41","modified_gmt":"2024-02-29T16:25:42","slug":"v-repozitorii-hugging-face-vyyavleny-vredonosnye-ai-modeli-vypolnyayushhie-kod","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hr\/blog\/news\/v-repozitorii-hugging-face-vyyavleny-vredonosnye-ai-modeli-vypolnyayushhie-kod","title":{"rendered":"U repozitoriju Hugging Face otkrivene zlonamjerne AI modele koji izvr\u0161avaju kod","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Istra\u017eiva\u010di iz kompanije JFrog otkrili su zlonamerne modele ma\u0161inskog u\u010denja u repozitoriju Hugging Face, \u010dija instalacija mo\u017ee omogu\u0107iti izvr\u0161enje koda napada\u010da za preuzimanje kontrole nad sistemom korisnika. Problem je uzrokovan time \u0161to neki formati distribucije modela omogu\u0107avaju umetanje izvr\u0161nog koda; na primer, modeli koji koriste format \u201epickle\u201c mogu uklju\u010divati serijalizovane objekte napisane u Python-u, kao i kod koji se izvr\u0161ava prilikom u\u010ditavanja datoteke, dok modeli Tensorflow Keras mogu izvr\u0161avati kod putem Lambda Laye-a. <\/p>\n

Kako bi se spre\u010dilo \u0161irenje sli\u010dnih zlo\u0107udnih modela, Hugging Face primenjuje skeniranje radi otkrivanja ugra\u0111enog serializovanog koda, ali otkrivene zlo\u0107udne varijante pokazuju da se postoje\u0107e provere mogu zaobi\u0107i. Pored toga, Hugging Face u ve\u0107ini slu\u010dajeva samo ozna\u010dava modele kao opasne, bez blokiranja pristupa njima. Ukupno je otkriveno oko 100 potencijalno zlo\u0107udnih modela, od kojih je 95% namenjeno kori\u0161\u0107enju sa PyTorch-om, a 5% sa Tensorflow-om. Naj\u010de\u0161\u0107i oblici zlo\u0107udnih promena uklju\u010duju preuzimanje objekta, organizaciju spoljnog pristupa (reverse shell), pokretanje aplikacija i pisanje u datoteku.

\"U<\/center> <\/p>\n

Napominje se da, sude\u0107i prema preduzetim radnjama, ve\u0107ina otkrivenih zlo\u0107udnih modela je stvorena od strane istra\u017eiva\u010da sigurnosti koji poku\u0161avaju da dobiju nagradu za otkrivanje ranjivosti i metoda zaobila\u017eenja za\u0161tite Hugging Face-a (na primer, umesto prave napade, ovakvi modeli poku\u0161avaju da pokrenu kalkulator ili po\u0161alju mre\u017eni zahtev sa informacijama o uspehu napada). Tako\u0111e se mogu prona\u0107i primeri koji pokre\u0107u povratni shell radi povezivanja napada\u010da sa sistemom. <\/p>\n

Na primer, modeli \u201eballer423\/goober2\u201c i \u201estar23\/baller13\u201c ciljanju na izvo\u0111enje napada na sisteme koji u\u010ditavaju datoteku modela u PyTorch pomo\u0107u funkcije torch.load(). Za organizaciju izvr\u0161enja koda koristi se metoda \u201e__reduce__\u201c iz modula pickle, koja omogu\u0107ava umetanje proizvoljnog Python-koda u proces deseralizacije koji se izvr\u0161ava prilikom u\u010ditavanja modela.
\n
Izvor: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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\u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u0438 Hugging Face \u0432\u044b\u044f\u0432\u043b\u0435\u043d\u044b \u0432\u0440\u0435\u0434\u043e\u043d\u043e\u0441\u043d\u044b\u0435 AI-\u043c\u043e\u0434\u0435\u043b\u0438, \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u044e\u0449\u0438\u0435 \u043a\u043e\u0434 | ProHoster","og:description":"\u0418\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0438 \u0438\u0437 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 JFrog \u0432\u044b\u044f\u0432\u0438\u043b\u0438 \u0432 \u0440\u0435\u043f\u043e\u0437\u0438\u0442\u043e\u0440\u0438\u0438 Hugging Face \u0432\u0440\u0435\u0434\u043e\u043d\u043e\u0441\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043c\u043e\u0436\u0435\u0442 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