{"id":138938,"date":"2025-07-14T11:11:58","date_gmt":"2025-07-14T09:11:59","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/gpuhammer-variant-ataki-rowhammer-na-pamyat-gpu"},"modified":"2025-07-14T11:11:58","modified_gmt":"2025-07-14T09:11:59","slug":"gpuhammer-variant-ataki-rowhammer-na-pamyat-gpu","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sl\/blog\/news\/gpuhammer-variant-ataki-rowhammer-na-pamyat-gpu","title":{"rendered":"GPUHammer \u2014 varijanta Rowhammer napada na GPU memoriju","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Raziskovalci z Univerze v Torontu so predstavili prvi napad tipa Rowhammer, ki se uporablja za spreminjanje vsebine video pomnilnika. Mo\u017enost izvedbe napada, ki povzro\u010di zamenjavo do 8 bitov podatkov, je bila prikazana na diskretnem GPU NVIDIA A6000 z video pomnilnikom GDDR6. Kot prakti\u010den primer je bilo prikazano, kako je mogo\u010de to vrsto spreminjanja uporabiti za poseganje v izvajanje modelov strojnega u\u010denja, kar znatno zmanj\u0161a natan\u010dnost njihovih rezultatov (z 80 % na 0,1 %) zlahka z spremembo vrednosti le enega bita.

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

Doslej je bilo ustvarjanje napadov tipa Rowhammer za video pomnilnik ote\u017eeno zaradi te\u017eav pri dolo\u010danju fizi\u010dne razporeditve pomnilnika v \u010dipih GDDR, velikega latence dostopa do pomnilnika (4-krat po\u010dasnej\u0161a) in vi\u0161je frekvence osve\u017eevanja pomnilnika. Poleg tega so izvajanje raziskav ovirali za\u0161\u010ditni mehanizmi v \u010dipih GDDR proti manipulacijam, ki vodijo do prezgodnje izgube napolnjenosti, zaradi katerih so morali raziskovalci ustvariti posebne testne naprave na osnovi FPGA za analizo. <\/p>\n

Za prou\u010devanje video pomnilnika so raziskovalci razvili novo tehniko obratnega in\u017eeniringa GDDR DRAM, pri napadu pa so uporabili optimizacije dostopa do pomnilnika, ki se uporabljajo pri organizaciji paralelnih izra\u010dunov, kot oja\u010devalnike intenzivnosti dostopa do posameznih celic. Za izvedbo napada na GPU NVIDIA se je uporabila nizkonivojska koda CUDA. <\/p>\n

V uporabni\u0161kem kodi CUDA se ne izvaja dostop do fizi\u010dnih naslovov pomnilnika, vendar je v gonilniku NVIDIA potekalo preslikavanje virtualnega pomnilnika v enako fizi\u010dno pomnilnik, kar so raziskovalci izkoristili za izra\u010dun odklona virtualnega pomnilnika in dolo\u010ditev razporeditve spominskih bank. Prepoznavanje dostopov do razli\u010dnih pomnilni\u0161kih bank je bilo izvedeno s pomo\u010djo analize zamud, ki so se razlikovale pri dostopu do ene ali razli\u010dnih pomnilni\u0161kih bank. <\/p>\n

V sistemih, ki so skupno uporabljali GPU, kot so servere<\/a> \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0430\u0442\u0430\u043a\u0430 \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u043e\u0434\u043d\u043e\u043c\u0443 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044e \u0438\u0437\u043c\u0435\u043d\u0438\u0442\u044c \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0435 \u0432\u0438\u0434\u0435\u043e\u043f\u0430\u043c\u044f\u0442\u0438 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f. \u0412 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043c\u0435\u0440\u044b \u0437\u0430\u0449\u0438\u0442\u044b \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f NVIDIA \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442 \u0432\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u043a\u043e\u0434\u044b \u043a\u043e\u0440\u0440\u0435\u043a\u0446\u0438\u0438 \u043e\u0448\u0438\u0431\u043e\u043a (ECC, Error Correction Codes) \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439 «nvidia-smi -e 1» \u0438\u043b\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0435\u0440\u0438\u0438 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442 \u0441 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 OD-ECC (On-Die ECC), \u0442\u0430\u043a\u0438\u0435 \u043a\u0430\u043a GeForce RTX 50, RTX PRO, GB200, B200, B100, H100, H200, H20 \u0438 GH200. <\/p>\n

Po podatkih raziskovalcev omogo\u010da vklju\u010ditev ECC na sistemih z GPU A6000 zmanj\u0161anje u\u010dinkovitosti izvajanja modelov strojnega u\u010denja za pribli\u017eno 10 % in zmanj\u0161anje prostornine pomnilnika za 6,25 %. Orodja za izvedbo napada in izvajanje obrata in\u017eeniringa nizko nivojske postavitve video pomnilnika na sistemih z GPU NVIDIA so objavljena na GitHubu. <\/p>\n

RowHammer napad omogu\u0107ava iskrivljenje sadr\u017eaja pojedina\u010dnih bitova DRAM memorije putem cikli\u010dnog \u010ditanja podataka iz susednih memorijskih \u0107elija. Kako DRAM memorija predstavlja dvodimenzionalnu mre\u017eu \u0107elija, od kojih svaka sadr\u017ei kondenzator i tranzistor, kontinuirano \u010ditanje iste oblasti memorije dovodi do fluktuacija napona i anomalija koje izazivaju mali gubitak naboja u susednim \u0107elijama. Ukoliko je intenzitet \u010ditanja visok, susedna \u0107elija mo\u017ee izgubiti dovoljno zna\u010dajan deo naboja i slede\u0107i ciklus regeneracije ne uspeva da povrati njeno prvobitno stanje, \u0161to rezultira promenom vrednosti sa\u010duvanih u \u0107eliji. <\/p>\n

\u041c\u0435\u0442\u043e\u0434 \u0430\u0442\u0430\u043a\u0438 Rowhammer \u0431\u044b\u043b \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d \u0432 2014 \u0433\u043e\u0434\u0443, \u043f\u043e\u0441\u043b\u0435 \u0447\u0435\u0433\u043e \u043c\u0435\u0436\u0434\u0443 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044f\u043c\u0438 \u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 \u0438 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044f\u043c\u0438 \u043e\u0431\u043e\u0440\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u0430\u0447\u0430\u043b\u0430\u0441\u044c \u0438\u0433\u0440\u0430 \u0432 «\u043a\u043e\u0448\u043a\u0438-\u043c\u044b\u0448\u043a\u0438» — \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0438 \u0447\u0438\u043f\u043e\u0432 \u043f\u0430\u043c\u044f\u0442\u0438 \u043f\u044b\u0442\u0430\u043b\u0438\u0441\u044c \u0431\u043b\u043e\u043a\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u044c, \u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0438 \u043d\u0430\u0445\u043e\u0434\u0438\u043b\u0438 \u043d\u043e\u0432\u044b\u0435 \u0441\u043f\u043e\u0441\u043e\u0431\u044b \u0435\u0451 \u043e\u0431\u0445\u043e\u0434\u0430. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u043b\u044f \u0437\u0430\u0449\u0438\u0442\u044b \u043e\u0442 RowHammer \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0438 \u0447\u0438\u043f\u043e\u0432 \u0434\u043e\u0431\u0430\u0432\u0438\u043b\u0438 \u043c\u0435\u0445\u0430\u043d\u0438\u0437\u043c TRR (Target Row Refresh), \u043d\u043e \u043e\u043a\u0430\u0437\u0430\u043b\u043e\u0441\u044c, \u0447\u0442\u043e \u043e\u043d \u0431\u043b\u043e\u043a\u0438\u0440\u0443\u0435\u0442 \u0438\u0441\u043a\u0430\u0436\u0435\u043d\u0438\u0435 \u044f\u0447\u0435\u0435\u043a \u043b\u0438\u0448\u044c \u0432 \u0447\u0430\u0441\u0442\u043d\u044b\u0445 \u0441\u043b\u0443\u0447\u0430\u044f\u0445, \u043d\u043e \u043d\u0435 \u0437\u0430\u0449\u0438\u0449\u0430\u0435\u0442 \u043e\u0442 \u0432\u0441\u0435\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 \u0430\u0442\u0430\u043a\u0438. \u041c\u0435\u0442\u043e\u0434\u044b \u0430\u0442\u0430\u043a\u0438 \u0431\u044b\u043b\u0438 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u044b \u0434\u043b\u044f \u0447\u0438\u043f\u043e\u0432 DDR3, DDR4 \u0438 DDR5 \u043d\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0445 \u0441 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430\u043c\u0438 Intel, AMD \u0438 ARM. \u0422\u0430\u043a\u0436\u0435 \u0431\u044b\u043b\u0438 \u043d\u0430\u0439\u0434\u0435\u043d\u044b \u0441\u043f\u043e\u0441\u043e\u0431\u044b \u043e\u0431\u0445\u043e\u0434\u0430 \u043a\u043e\u0440\u0440\u0435\u043a\u0446\u0438\u0438 \u043e\u0448\u0438\u0431\u043e\u043a ECC \u0438 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u044b \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u044b \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u0430\u0442\u0430\u043a\u0438 \u043f\u043e \u0441\u0435\u0442\u0438 \u0438 \u0447\u0435\u0440\u0435\u0437 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 JavaScript-\u043a\u043e\u0434\u0430 \u0432 \u0431\u0440\u0430\u0443\u0437\u0435\u0440\u0435.
\n
Izvor: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0418\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0438 \u0438\u0437 \u0423\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u0422\u043e\u0440\u043e\u043d\u0442\u043e \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0438 \u043f\u0435\u0440\u0432\u0443\u044e \u0430\u0442\u0430\u043a\u0443 \u043a\u043b\u0430\u0441\u0441\u0430 Rowhammer, \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u043c\u0443\u044e \u0434\u043b\u044f \u0438\u0441\u043a\u0430\u0436\u0435\u043d\u0438\u044f \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0433\u043e \u0432\u0438\u0434\u0435\u043e\u043f\u0430\u043c\u044f\u0442\u0438. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u0430\u0442\u0430\u043a\u0438, \u043f\u0440\u0438\u0432\u043e\u0434\u044f\u0449\u0435\u0439 \u043a \u043f\u043e\u0434\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0435 \u0434\u043e 8 \u0431\u0438\u0442 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