{"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\/sv\/blog\/news\/gpuhammer-variant-ataki-rowhammer-na-pamyat-gpu","title":{"rendered":"GPUHammer \u2014 en variant av Rowhammer-attacken mot GPU-minnet","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Forskare fr\u00e5n Toronto University har presenterat den f\u00f6rsta Rowhammer-attacken som anv\u00e4nds f\u00f6r att f\u00f6rvr\u00e4nga inneh\u00e5llet i grafiskt minne. M\u00f6jligheten att genomf\u00f6ra en attack som leder till ers\u00e4ttning av upp till 8 bitar data har demonstrerats p\u00e5 det diskreta GPU-kortet NVIDIA A6000 med GDDR6-minne. Ett praktiskt exempel visar hur man kan utnyttja en s\u00e5dan f\u00f6rvr\u00e4ngning f\u00f6r att p\u00e5verka genomf\u00f6randet av maskininl\u00e4rningsmodeller och d\u00e4rigenom uppn\u00e5 en betydande (fr\u00e5n 80% till 0.1%) minskning av noggrannheten i resultaten, genom att \u00e4ndra v\u00e4rdet av bara en bit.

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

Hitintills har skapandet av Rowhammer-attacker f\u00f6r grafiskt minne varit komplicerat p\u00e5 grund av sv\u00e5righeten att fastst\u00e4lla den fysiska layouten av minnet i GDDR-chip, stora f\u00f6rdr\u00f6jningar vid minnes\u00e5tkomst (4 g\u00e5nger l\u00e5ngsammare) och h\u00f6gre uppdateringsfrekvenser f\u00f6r minnet. Dessutom har forskning hindrats av anv\u00e4ndningen av propriet\u00e4ra skyddsmekanismer mot manipulation i GDDR-chip, vilket kr\u00e4vde att specialiserade h\u00e5rdvarutestb\u00e4nkar baserat p\u00e5 FPGA skapades f\u00f6r analys. <\/p>\n

F\u00f6r att studera grafiskt minne har forskarna utvecklat en ny teknik f\u00f6r omv\u00e4nd ingenj\u00f6rskonst av GDDR DRAM, och i attacken har tekniker f\u00f6r att optimera minnes\u00e5tkomst som anv\u00e4nds vid parallell ber\u00e4kning anv\u00e4nts som intensitetsf\u00f6rst\u00e4rkare f\u00f6r \u00e5tkomst till individuella celler. En l\u00e5g-niv\u00e5 CUDA-kod anv\u00e4ndes f\u00f6r att genomf\u00f6ra attacken mot NVIDIA GPU. <\/p>\n

I anv\u00e4ndarkoden f\u00f6r CUDA g\u00f6rs inga anrop till fysiska minnesadresser, men i NVIDIA-drivrutinen \u00e5terspeglades virtuell minne i samma fysiska minne, vilket forskarna utnyttjade f\u00f6r att ber\u00e4kna f\u00f6rskjutningen av det virtuella minnet och fastst\u00e4lla layouten av minnesbankerna. Identifieringen av \u00e5tkomsten till olika minnesbanker genomf\u00f6rdes genom att analysera f\u00f6rdr\u00f6jningar, som varierade vid \u00e5tkomst till en eller flera bankar. <\/p>\n

I system som gemensamt anv\u00e4nder GPU, s\u00e5som servrar<\/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

Enligt forskare leder aktivering av ECC p\u00e5 system med GPU A6000 till en minskning av prestandan vid utf\u00f6rande av modeller f\u00f6r maskininl\u00e4rning p\u00e5 ungef\u00e4r 10% och en minskning av minneskapaciteten p\u00e5 6,25%. Verktygen f\u00f6r att utf\u00f6ra attacken och genomf\u00f6ra omv\u00e4nd ingenj\u00f6rskonst av l\u00e5g niv\u00e5 av videominne p\u00e5 system med NVIDIA GPU har publicerats p\u00e5 GitHub. <\/p>\n

RowHammer-attacken m\u00f6jligg\u00f6r f\u00f6rvr\u00e4ngning av inneh\u00e5llet i enskilda bitar av DRAM-minne genom cykliskt l\u00e4sande av data fr\u00e5n angr\u00e4nsande minnesceller. Eftersom DRAM-minnet utg\u00f6rs av en tv\u00e5dimensionell matris av celler, d\u00e4r varje cell best\u00e5r av en kondensator och en transistor, leder kontinuerligt l\u00e4sande av samma minnesomr\u00e5de till sp\u00e4nningsfluktuationer och anomalier som orsakar en liten laddningsf\u00f6rlust i angr\u00e4nsande celler. Om l\u00e4sintensiteten \u00e4r h\u00f6g kan den angr\u00e4nsande cellen f\u00f6rlora en tillr\u00e4ckligt stor m\u00e4ngd laddning s\u00e5 att den efterf\u00f6ljande regenereringscykeln inte hinner \u00e5terst\u00e4lla dess ursprungliga tillst\u00e5nd, vilket leder till en f\u00f6r\u00e4ndring av det lagrade datav\u00e4rdet i cellen. <\/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.
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K\u00e4lla: 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 \u0434\u0430\u043d\u043d\u044b\u0445, \u043f\u0440\u043e\u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0430 \u043d\u0430 \u0434\u0438\u0441\u043a\u0440\u0435\u0442\u043d\u043e\u043c GPU NVIDIA A6000 \u0441 \u0432\u0438\u0434\u0435\u043e\u043f\u0430\u043c\u044f\u0442\u044c\u044e GDDR6. \u0412 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u0430 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e \u043a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u043e\u0434\u043e\u0431\u043d\u043e\u0435 \u0438\u0441\u043a\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u0432\u043c\u0435\u0448\u0430\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u0430 \u0432 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 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