TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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 21:15:10
Updated 2022-09-20 19:19:45
Source GitHub, Inc.
View at NVD,   CVE.org
Vulnerability category: Input validationDenial of service

Exploit prediction scoring system (EPSS) score for CVE-2022-35974

Probability of exploitation activity in the next 30 days: 0.08%

Percentile, the proportion of vulnerabilities that are scored at or less: ~ 31 % EPSS Score History EPSS FAQ

CVSS scores for CVE-2022-35974

Base Score Base Severity CVSS Vector Exploitability Score Impact Score Score Source
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-2022-35974

  • 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-2022-35974

Products affected by CVE-2022-35974

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