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


Severity

Recommended
0.0
high
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-2333540
  • published12 Jan 2022
  • disclosed12 Aug 2021
  • creditUnknown

Introduced: 12 Aug 2021

CVE-2021-37663  (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 due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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