{"id":147923,"date":"2025-11-10T23:11:54","date_gmt":"2025-11-10T21:11:56","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/ataka-whisper-leak-po-opredeleniyu-tematiki-obshheniya-s-ai-chatbotami-cherez-analiz-tls-trafika"},"modified":"2025-11-10T23:11:54","modified_gmt":"2025-11-10T21:11:56","slug":"ataka-whisper-leak-po-opredeleniyu-tematiki-obshheniya-s-ai-chatbotami-cherez-analiz-tls-trafika","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/novosti-interneta\/ataka-whisper-leak-po-opredeleniyu-tematiki-obshheniya-s-ai-chatbotami-cherez-analiz-tls-trafika","title":{"rendered":"Whisper Leak r\u00fcnnak m\u00e4\u00e4rab AI-vestluste teema TLS-liikluse anal\u00fc\u00fcsi kaudu.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Microsofti turva-uuringute meeskond on v\u00e4lja t\u00f6\u00f6tanud k\u00f5rvalseire r\u00fcnnakute tehnika Whisper Leak, mis v\u00f5imaldab TLS-\u00fchenduse kaudu edastatavat kr\u00fcpteeritud liiklust passiivse anal\u00fc\u00fcsi alusel klassifitseerida suurte keelereeglite mudelite teenuste p\u00e4ringute teemade p\u00f5hjal. Teave v\u00f5rgupakettide suuruse ja edastamise viivituste kohta osutus piisavaks AI-vestlusrobotitele suunatud p\u00e4ringute teemade m\u00e4\u00e4ramiseks rohkem kui 98% t\u00e4psusega. Praktikas v\u00f5ib esitatud meetodit kasutada kasutaja \u00fclekantud liikluses konkreetsete k\u00fcsimuste teemade, n\u00e4iteks teabe hankimise seadusvastaste tegevuste kohta, tuvastamiseks, ilma sisu dekodeerimata. <\/p>\n

Andmete v\u00e4ljav\u00f5tmisvahendid liiklustagastuste anal\u00fc\u00fcsimiseks, mudeli koolitamiseks ja meetodi testimiseks on avaldatud GitHubis. R\u00fcnnaku v\u00f5imalusi on demonstreeritud 28 populaarse suurte keelemodellide seas suurte tootjate poolt. N\u00e4iteks oli mitmete AI-teenuste puhul rahapesu teemaga seotud p\u00e4ringute t\u00e4psus 100% oluliste p\u00e4ringute esinemisel anal\u00fc\u00fcsitavas liikluses, kus 1 p\u00e4ring oli seotud ning 10000 p\u00e4ringut mitte.

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

Teabe lekkimise p\u00f5hjuseks on see, et mudelid genereerivad vastust p\u00e4ringule samm-sammult, \u00fcks t\u00f5kend korraga, kasutades igal sammul eelmist t\u00f5kendi konteksti j\u00e4rgmise k\u00f5ige t\u00f5en\u00e4olisema s\u00f5na v\u00f5i v\u00e4ljendi m\u00e4\u00e4ramiseks. Seet\u00f5ttu saadetakse iga t\u00f5kendi jaoks eraldi v\u00f5rgu pakett ja pakettide vaheline hilinemine vastab mudeli j\u00e4rgmise t\u00f5kendi m\u00e4\u00e4ramise vahelisele viivitusele. <\/p>\n

TLS-is, kui andmekompressiooni ei kasutata, siis kr\u00fcpteeritud teksti suurus on samav\u00e4\u00e4rne dekr\u00fcpteeritud teksti suuruse ja konstantsusega. Modelleerimisel, mis seob otsitavad tokenite kogumid pakettide suuruse ja nende saatmise viibega, on v\u00f5imalik \u00fcsna t\u00e4pselt tuvastada liikluses otsitud teemasid. Uuringu k\u00e4igus valmistati kolm sarnast masin\u00f5ppe mudelit, mis p\u00f5hinevad n\u00e4rviv\u00f5rkude arhitektuuridel LightGBM, Bi-LSTM ja BERT. Iga mudeli puhul viidi l\u00e4bi katsed otsitava teema m\u00e4\u00e4ramiseks, anal\u00fc\u00fcsides ainult pakettide suuruseid, ainult pakettide vahelisi viive ja m\u00f5lemaid kriteeriume.

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

Passiivse anal\u00fc\u00fcsi t\u00f5hususe v\u00e4hendamiseks on AI-teenuste arendajatele soovitatud lisada juhuslikku t\u00e4iendavat t\u00e4idist, vahem\u00e4lusid tokenite edasiandmiseks v\u00f5i asendada valepakette.
\n
Allikas: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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