crypto: qcom-rng - ensure buffer for generate is completely filled
CVSS score
8.1
EPSS probability
%0.5
Risk score
0.0
Published
2 y ago
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Vulnerability description
DayBreach CVE AI Research
CVE-2022-48629 has vendor, distribution, patch and security-source records on file. We can compare them and prepare an explanation linked to the evidence.
In the Linux kernel, the following vulnerability has been resolved: crypto: qcom-rng - ensure buffer for generate is completely filled The generate function in struct rng_alg expects that the destination buffer is completely filled if the function returns 0. qcom_rng_read() can run into a situation where the buffer is partially filled with randomness and the remaining part of the buffer is zeroed since qcom_rng_generate() doesn't check the return value. This issue can be reproduced by running the following from libkcapi: kcapi-rng -b 9000000 > OUTFILE The generated OUTFILE will have three huge sections that contain all zeros, and this is caused by the code where the test 'val & PRNG_STATUS_DATA_AVAIL' fails. Let's fix this issue by ensuring that qcom_rng_read() always returns with a full buffer if the function returns success. Let's also have qcom_rng_generate() return the correct value. Here's some statistics from the ent project (https://www.fourmilab.ch/random/) that shows information about the quality of the generated numbers: $ ent -c qcom-random-before Value Char Occurrences Fraction 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 Entropy = 7.811590 bits per byte. Optimum compression would reduce the size of this 9000000 byte file by 2 percent. Chi square distribution for 9000000 samples is 9329962.81, and randomly would exceed this value less than 0.01 percent of the times. Arithmetic mean value of data bytes is 119.3731 (127.5 = random). Monte Carlo value for Pi is 3.197293333 (error 1.77 percent). Serial correlation coefficient is 0.159130 (totally uncorrelated = 0.0). Without this patch, the results of the chi-square test is 0.01%, and the numbers are certainly not random according to ent's project page. The results improve with this patch: $ ent -c qcom-random-after Value Char Occurrences Fraction 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 Entropy = 7.999979 bits per byte. Optimum compression would reduce the size of this 9000000 byte file by 0 percent. Chi square distribution for 9000000 samples is 258.77, and randomly would exceed this value 42.24 percent of the times. Arithmetic mean value of data bytes is 127.5006 (127.5 = random). Monte Carlo value for Pi is 3.141277333 (error 0.01 percent). Serial correlation coefficient is 0.000468 (totally uncorrelated = 0.0). This change was tested on a Nexus 5 phone (msm8974 SoC).
IMPACT: Affected Products and Software
Affected Products
The following products are affected by CVE-2022-48629 vulnerability. Where our threat engine knows the exact affected versions, they are listed below.
RED HAT CSAF/VEX: Product Impact Check
SCORING: CVSS from Multiple Sources
CVSS Scores
The Common Vulnerability Scoring System is a standardised framework for assessing the severity of vulnerabilities in software and systems. We collect and display CVSS scores from several sources for each CVE.
| SCORE | VERSION | SEVERITY | VECTOR | SOURCE |
|---|---|---|---|---|
| 8.1 | CVSS V31 | HIGH | CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N | NVD |
| 8.1 | CVSS V3_1 | HIGH | CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N | CVE CNA |
| 5.5 | CVSS V31 | MEDIUM | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H | NVD |
Patch and Remediation Guide
Solution & Remediation Advisory
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Public exploit and active exploitation status
Verified exploit intelligence
No verified public PoC, exploit repository or Metasploit module has been found for this CVE yet.
Weakness Classification and Attack Patterns (CWE & CAPEC)
CWE - Common Weakness Enumeration
While CVE identifies specific instances of vulnerabilities, CWE categorises the common flaws or weaknesses that can lead to vulnerabilities. CVE-2022-48629 is associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit the CVE-2022-48629 weaknesses.
Attack Vector and Accessibility
Official Sources and Advisories
CVSS Vector Radar Chart
8.1 / 10Vulnerability Scoring Details
CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:NYama doğrulandı
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