Out-of-bounds Write 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)

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  • Snyk IDSNYK-UNMANAGED-TENSORFLOWTENSORFLOW-2333482
  • published12 Jan 2022
  • disclosed14 May 2021
  • creditUnknown

Introduced: 14 May 2021

CVE-2021-29583  (opens in a new tab)
CWE-476  (opens in a new tab)
CWE-787  (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 Out-of-bounds Write. TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FusedBatchNorm is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that scale, offset, mean and variance (the last two only when required) all have the same number of elements as the number of channels of x. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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