CVE-2022-35970

2022-09-1621:15:09

CWE-20

[email protected]

web.nvd.nist.gov

cve-2022-35970

tensorflow

quantizedinstancenorm

dos

github

nvd

security

vulnerability

patch

7.5 High

CVSS3

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

NONE

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

0.001 Low

EPSS

Percentile

32.5%

JSON

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.

Affected configurations

Vulners

NVD

Node

tensorflowtensorflowRange<2.7.2

tensorflowtensorflowRange2.8.0–2.8.1

tensorflowtensorflowRange2.9.0–2.9.1

CPENameOperatorVersion
google:tensorflowgoogle tensorflowlt2.7.2
google:tensorflowgoogle tensorflowlt2.8.1
google:tensorflowgoogle tensorflowlt2.9.1
google:tensorflowgoogle tensorfloweq2.10

CPE	Name	Operator	Version
google:tensorflow	google tensorflow	lt	2.7.2
google:tensorflow	google tensorflow	lt	2.8.1
google:tensorflow	google tensorflow	lt	2.9.1
google:tensorflow	google tensorflow	eq	2.10

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "< 2.7.2"
      },
      {
        "status": "affected",
        "version": ">= 2.8.0, < 2.8.1"
      },
      {
        "status": "affected",
        "version": ">= 2.9.0, < 2.9.1"
      }
    ]
  }
]