Improper Initialization Affecting tensorflow/tensorflow package, versions [,2.1.4)[2.2.0,2.2.3)[2.3.0,2.3.3)[2.4.0,2.4.2)


Severity

Recommended
0.0
high
0
10

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

Threat Intelligence

EPSS
0.04% (6th percentile)

Do your applications use this vulnerable package?

In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.

Test your applications
  • Snyk IDSNYK-UNMANAGED-TENSORFLOWTENSORFLOW-2333397
  • published12 Jan 2022
  • disclosed14 May 2021
  • creditUnknown

Introduced: 14 May 2021

CVE-2021-29610  (opens in a new tab)
CWE-665  (opens in a new tab)

How to fix?

Upgrade tensorflow/tensorflow to version 2.1.4, 2.2.3, 2.3.3, 2.4.2 or higher.

Overview

Affected versions of this package are vulnerable to Improper Initialization. TensorFlow is an end-to-end open source platform for machine learning. The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for axis argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses || to mix two different conditions. If axis_ < -1 the condition in OP_REQUIRES will still be true, but this value of axis_ results in heap underflow. This allows attackers to read/write to other data on the heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

CVSS Scores

version 3.1