CVE-2022-29199: Missing validation causes denial of service in TensorFlow via `LoadAndRemapMatrix`

Published May 20, 2022
·
Updated

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.rawops.LoadAndRemapMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes initializingvalues is a vector but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Affected Software

9 affected components
Google TensorFlow<2.6.4
Google TensorFlow>=2.7.0<2.7.2
Google TensorFlow=2.7.0-rc0
Google TensorFlow=2.7.0-rc1
Google TensorFlow=2.8.0
Google TensorFlow=2.8.0-rc0
Google TensorFlow=2.8.0-rc1
Google TensorFlow=2.9.0-rc0
Google TensorFlow=2.9.0-rc1

Event History

May 20, 2022
CVE Published
via MITRE·09:40 PM
Data Sourced
via MITRE·09:40 PM
DescriptionSeverityWeakness

Frequently Asked Questions

1

What is the severity of CVE-2022-29199?

CVE-2022-29199 has a severity rating that indicates a potential denial of service vulnerability in TensorFlow.

2

How do I fix CVE-2022-29199?

To fix CVE-2022-29199, upgrade TensorFlow to version 2.9.0 or later, or to 2.8.1, 2.7.2, or 2.6.4.

3

Which versions of TensorFlow are affected by CVE-2022-29199?

CVE-2022-29199 affects TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

4

What component of TensorFlow is vulnerable in CVE-2022-29199?

The vulnerability in CVE-2022-29199 is found in the `tf.raw_ops.LoadAndRemapMatrix` implementation.

5

Can CVE-2022-29199 cause data loss?

CVE-2022-29199 primarily poses a risk of denial of service, but it does not specifically indicate potential data loss.

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