CVE-2021-29578: Heap buffer overflow in `FractionalAvgPoolGrad`

Published May 14, 2021
·
Updated

Impact The implementation of tf.rawops.FractionalAvgPoolGrad is vulnerable to a heap buffer overflow: python import tensorflow as tf

originputtensorshape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64) outbackprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64) rowpoolingsequence = tf.constant([1], shape=[1], dtype=tf.int64) colpoolingsequence = tf.constant([1], shape=[1], dtype=tf.int64)

tf.rawops.FractionalAvgPoolGrad( originputtensorshape=originputtensorshape, outbackprop=outbackprop, rowpoolingsequence=rowpoolingsequence, colpoolingsequence=colpoolingsequence, overlapping=False)

The implementation fails to validate that the pooling sequence arguments have enough elements as required by the outbackprop tensor shape.

Patches We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f.

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.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.rawops.FractionalAvgPoolGrad is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractionalavgpoolop.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the outbackprop tensor shape. 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.

Affected Software

16 affected componentsFixes available
pip/tensorflow-gpu>=2.4.0<2.4.2
2.4.2
pip/tensorflow-gpu>=2.3.0<2.3.3
2.3.3
pip/tensorflow-gpu>=2.2.0<2.2.3
2.2.3
pip/tensorflow-gpu<2.1.4
2.1.4
pip/tensorflow-cpu>=2.4.0<2.4.2
2.4.2
pip/tensorflow-cpu>=2.3.0<2.3.3
2.3.3
pip/tensorflow-cpu>=2.2.0<2.2.3
2.2.3
pip/tensorflow-cpu<2.1.4
2.1.4
pip/tensorflow>=2.4.0<2.4.2
2.4.2
pip/tensorflow>=2.3.0<2.3.3
2.3.3
pip/tensorflow>=2.2.0<2.2.3
2.2.3
pip/tensorflow<2.1.4
2.1.4
Google TensorFlow<2.1.4
Google TensorFlow>=2.2.0<2.2.3
Google TensorFlow>=2.3.0<2.3.3
Google TensorFlow>=2.4.0<2.4.2

Event History

May 14, 2021
CVE Published
via MITRE·07:15 PM
Data Sourced
via MITRE·07:15 PM
DescriptionSeverityWeakness
May 21, 2021
Advisory Published
via GitHub·02:26 PM

Frequently Asked Questions

1

What is the severity of CVE-2021-29578?

CVE-2021-29578 has a high severity due to the potential for a heap buffer overflow, which can lead to arbitrary code execution.

2

How do I fix CVE-2021-29578?

To fix CVE-2021-29578, upgrade TensorFlow to version 2.4.2 or later.

3

What versions of TensorFlow are affected by CVE-2021-29578?

CVE-2021-29578 affects TensorFlow versions prior to 2.4.2, specifically versions 2.1.0 through 2.4.1.

4

Is CVE-2021-29578 a critical vulnerability for TensorFlow?

Yes, CVE-2021-29578 is considered critical due to the risk it poses to application security.

5

What should I do if I cannot upgrade TensorFlow to fix CVE-2021-29578?

If upgrading is not possible, review your application for vulnerabilities and implement additional security measures to mitigate risks.

Contact

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