Improper Input Validation Affecting tensorflow/tensorflow package, versions [2.3.0,2.3.4)[2.4.0,2.4.3)


Severity

Recommended
0.0
medium
0
10

CVSS assessment made by Snyk's Security Team. Learn more

Threat Intelligence

EPSS
0.04% (16th percentile)

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  • Snyk IDSNYK-UNMANAGED-TENSORFLOWTENSORFLOW-2333427
  • published12 Jan 2022
  • disclosed12 Aug 2021
  • creditUnknown

Introduced: 12 Aug 2021

CVE-2021-37677  (opens in a new tab)
CWE-20  (opens in a new tab)

How to fix?

Upgrade tensorflow/tensorflow to version 2.3.4, 2.4.3 or higher.

Overview

Affected versions of this package are vulnerable to Improper Input Validation. TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

CVSS Scores

version 3.1