{ "id": "CVE-2022-48629", "sourceIdentifier": "416baaa9-dc9f-4396-8d5f-8c081fb06d67", "published": "2024-03-05T12:15:45.707", "lastModified": "2025-02-26T07:00:18.270", "vulnStatus": "Modified", "cveTags": [], "descriptions": [ { "lang": "en", "value": "In the Linux kernel, the following vulnerability has been resolved:\n\ncrypto: qcom-rng - ensure buffer for generate is completely filled\n\nThe generate function in struct rng_alg expects that the destination\nbuffer is completely filled if the function returns 0. qcom_rng_read()\ncan run into a situation where the buffer is partially filled with\nrandomness and the remaining part of the buffer is zeroed since\nqcom_rng_generate() doesn't check the return value. This issue can\nbe reproduced by running the following from libkcapi:\n\n kcapi-rng -b 9000000 > OUTFILE\n\nThe generated OUTFILE will have three huge sections that contain all\nzeros, and this is caused by the code where the test\n'val & PRNG_STATUS_DATA_AVAIL' fails.\n\nLet's fix this issue by ensuring that qcom_rng_read() always returns\nwith a full buffer if the function returns success. Let's also have\nqcom_rng_generate() return the correct value.\n\nHere's some statistics from the ent project\n(https://www.fourmilab.ch/random/) that shows information about the\nquality of the generated numbers:\n\n $ ent -c qcom-random-before\n Value Char Occurrences Fraction\n 0 606748 0.067416\n 1 33104 0.003678\n 2 33001 0.003667\n ...\n 253 \ufffd 32883 0.003654\n 254 \ufffd 33035 0.003671\n 255 \ufffd 33239 0.003693\n\n Total: 9000000 1.000000\n\n Entropy = 7.811590 bits per byte.\n\n Optimum compression would reduce the size\n of this 9000000 byte file by 2 percent.\n\n Chi square distribution for 9000000 samples is 9329962.81, and\n randomly would exceed this value less than 0.01 percent of the\n times.\n\n Arithmetic mean value of data bytes is 119.3731 (127.5 = random).\n Monte Carlo value for Pi is 3.197293333 (error 1.77 percent).\n Serial correlation coefficient is 0.159130 (totally uncorrelated =\n 0.0).\n\nWithout this patch, the results of the chi-square test is 0.01%, and\nthe numbers are certainly not random according to ent's project page.\nThe results improve with this patch:\n\n $ ent -c qcom-random-after\n Value Char Occurrences Fraction\n 0 35432 0.003937\n 1 35127 0.003903\n 2 35424 0.003936\n ...\n 253 \ufffd 35201 0.003911\n 254 \ufffd 34835 0.003871\n 255 \ufffd 35368 0.003930\n\n Total: 9000000 1.000000\n\n Entropy = 7.999979 bits per byte.\n\n Optimum compression would reduce the size\n of this 9000000 byte file by 0 percent.\n\n Chi square distribution for 9000000 samples is 258.77, and randomly\n would exceed this value 42.24 percent of the times.\n\n Arithmetic mean value of data bytes is 127.5006 (127.5 = random).\n Monte Carlo value for Pi is 3.141277333 (error 0.01 percent).\n Serial correlation coefficient is 0.000468 (totally uncorrelated =\n 0.0).\n\nThis change was tested on a Nexus 5 phone (msm8974 SoC)." }, { "lang": "es", "value": "En el kernel de Linux, se ha resuelto la siguiente vulnerabilidad: crypto: qcom-rng: aseg\u00farese de que el b\u00fafer para generar est\u00e9 completamente lleno. La funci\u00f3n de generaci\u00f3n en la estructura rng_alg espera que el b\u00fafer de destino est\u00e9 completamente lleno si la funci\u00f3n devuelve 0. qcom_rng_read() puede ejecutarse en una situaci\u00f3n en la que el b\u00fafer est\u00e1 parcialmente lleno de aleatoriedad y la parte restante del b\u00fafer se pone a cero ya que qcom_rng_generate() no verifica el valor de retorno. Este problema se puede reproducir ejecutando lo siguiente desde libkcapi: kcapi-rng -b 9000000 > OUTFILE El OUTFILE generado tendr\u00e1 tres secciones enormes que contienen todos ceros, y esto se debe al c\u00f3digo donde falla la prueba 'val & PRNG_STATUS_DATA_AVAIL'. Solucionemos este problema asegur\u00e1ndonos de que qcom_rng_read() siempre regrese con un b\u00fafer lleno si la funci\u00f3n tiene \u00e9xito. Tambi\u00e9n hagamos que qcom_rng_generate() devuelva el valor correcto. Aqu\u00ed hay algunas estad\u00edsticas del proyecto ent (https://www.fourmilab.ch/random/) que muestra informaci\u00f3n sobre la calidad de los n\u00fameros generados: $ ent -c qcom-random-before Valor Char Ocurrencias Fracci\u00f3n 0 606748 0.067416 1 33104 0,003678 2 33001 0,003667 ... 253 ? 32883 0,003654 254 ? 33035 0,003671 255 ? 33239 0,003693 Total: 9000000 1,000000 Entrop\u00eda = 7,811590 bits por byte. Una compresi\u00f3n \u00f3ptima reducir\u00eda el tama\u00f1o de este archivo de 9000000 bytes en un 2 por ciento. La distribuci\u00f3n de chi cuadrado para 9000000 muestras es 9329962,81 y aleatoriamente exceder\u00eda este valor menos del 0,01 por ciento de las veces. El valor medio aritm\u00e9tico de los bytes de datos es 119,3731 (127,5 = aleatorio). El valor de Monte Carlo para Pi es 3,197293333 (error del 1,77 por ciento). El coeficiente de correlaci\u00f3n serial es 0,159130 (totalmente no correlacionado = 0,0). Sin este parche, los resultados de la prueba de chi-cuadrado son del 0,01% y los n\u00fameros ciertamente no son aleatorios seg\u00fan la p\u00e1gina del proyecto de ent. Los resultados mejoran con este parche: $ ent -c qcom-random-after Valor Char Ocurrencias Fracci\u00f3n 0 35432 0.003937 1 35127 0.003903 2 35424 0.003936 ... 253 ? 35201 0,003911 254 ? 34835 0,003871 255 ? 35368 0,003930 Total: 9000000 1,000000 Entrop\u00eda = 7,999979 bits por byte. Una compresi\u00f3n \u00f3ptima reducir\u00eda el tama\u00f1o de este archivo de 9000000 bytes en un 0 por ciento. La distribuci\u00f3n de chi cuadrado para 9000000 muestras es 258,77 y aleatoriamente exceder\u00eda este valor el 42,24 por ciento de las veces. El valor medio aritm\u00e9tico de los bytes de datos es 127,5006 (127,5 = aleatorio). El valor de Monte Carlo para Pi es 3,141277333 (error del 0,01 por ciento). El coeficiente de correlaci\u00f3n serial es 0,000468 (totalmente no correlacionado = 0,0). Este cambio se prob\u00f3 en un tel\u00e9fono Nexus 5 (SoC msm8974)." } ], "metrics": { "cvssMetricV31": [ { "source": "nvd@nist.gov", "type": "Primary", "cvssData": { "version": "3.1", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H", "baseScore": 5.5, "baseSeverity": "MEDIUM", "attackVector": "LOCAL", "attackComplexity": "LOW", "privilegesRequired": "LOW", "userInteraction": "NONE", "scope": "UNCHANGED", "confidentialityImpact": "NONE", "integrityImpact": "NONE", "availabilityImpact": "HIGH" }, "exploitabilityScore": 1.8, "impactScore": 3.6 } ] }, "weaknesses": [ { "source": "nvd@nist.gov", "type": "Primary", "description": [ { "lang": "en", "value": "NVD-CWE-noinfo" } ] } ], "configurations": [ { "nodes": [ { "operator": "OR", "negate": false, "cpeMatch": [ { "vulnerable": true, "criteria": "cpe:2.3:o:linux:linux_kernel:*:*:*:*:*:*:*:*", "versionStartIncluding": "4.19", "versionEndExcluding": "4.19.236", "matchCriteriaId": "D1F07A37-C787-4411-B951-D5CEC6EFBA6D" }, { 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