Vulnerability Details : CVE-2022-35970

TensorFlow is an open source platform for machine learning. If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Published 2022-09-16 21:15:09
Updated 2022-09-20 20:01:19
Source GitHub, Inc.
View at NVD,   CVE.org
Vulnerability category: Input validationDenial of service

Products affected by CVE-2022-35970

Exploit prediction scoring system (EPSS) score for CVE-2022-35970

EPSS FAQ
0.08%
Probability of exploitation activity in the next 30 days EPSS Score History
~ 31 %
Percentile, the proportion of vulnerabilities that are scored at or less

CVSS scores for CVE-2022-35970

Base Score Base Severity CVSS Vector Exploitability Score Impact Score Score Source First Seen
7.5
 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
3.9
3.6
 NIST
5.9
 MEDIUM CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
2.2
3.6
 GitHub, Inc.

CWE ids for CVE-2022-35970

  • The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
    Assigned by: security-advisories@github.com (Secondary)

References for CVE-2022-35970

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