CVE-2021-37651: Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow

Published Aug 12, 2021
·
Updated

Impact The implementation for tf.rawops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:

python import tensorflow as tf

tf.rawops.FractionalAvgPoolGrad( originputtensorshape=[0,1,2,3], outbackprop = np.array([[[[541],[541]],[[541],[541]]]]), rowpoolingsequence=[0, 0, 0, 0, 0], colpoolingsequence=[-2, 0, 0, 2, 0], overlapping=True)

The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area.

Patches We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.

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.

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 members of the Aivul Team from Qihoo 360.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for tf.rawops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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.

Affected Software

15 affected componentsFixes available
pip/tensorflow-gpu=2.5.0
2.5.1
pip/tensorflow-gpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-gpu<2.3.4
2.3.4
pip/tensorflow-cpu=2.5.0
2.5.1
pip/tensorflow-cpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-cpu<2.3.4
2.3.4
pip/tensorflow=2.5.0
2.5.1
pip/tensorflow>=2.4.0<2.4.3
2.4.3
pip/tensorflow<2.3.4
2.3.4
Google TensorFlow>=2.3.0<2.3.4
Google TensorFlow>=2.4.0<2.4.3
Google TensorFlow=2.5.0
Google TensorFlow=2.6.0-rc0
Google TensorFlow=2.6.0-rc1
Google TensorFlow=2.6.0-rc2

Remediation

Recommended actions to resolve this vulnerability, in priority order.

  1. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.5.1
  2. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.4.3
  3. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.3.4
  4. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.5.1
  5. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.4.3
  6. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.3.4
  7. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.5.1
  8. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.4.3
  9. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.3.4
  10. Upgrade

    Upgrade tensorflow/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.6.0Patch 0f931751fb20f565c4e94aa6df58d54a003cdb30
  11. Upgrade

    Upgrade tensorflow/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.5.1Patch 0f931751fb20f565c4e94aa6df58d54a003cdb30
  12. Upgrade

    Upgrade tensorflow/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.4.3Patch 0f931751fb20f565c4e94aa6df58d54a003cdb30
  13. Upgrade

    Upgrade tensorflow/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.3.4Patch 0f931751fb20f565c4e94aa6df58d54a003cdb30

Event History

Aug 12, 2021
CVE Published
via MITRE·09:00 PM
Data Sourced
via MITRE·09:00 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·09:15 PM
RemedyDescriptionSeverityWeaknessAffected Software
Aug 25, 2021
Advisory Published
via GitHub·02:43 PM

Frequently Asked Questions

1

What is the severity of CVE-2021-37651?

CVE-2021-37651 has a severity rating of Medium due to its potential to cause exploitation through out-of-bounds access.

2

How do I fix CVE-2021-37651?

To remedy CVE-2021-37651, upgrade TensorFlow to version 2.5.1 or later, or to version 2.4.3 if using a vulnerable version between 2.4.0 and 2.4.3.

3

What versions of TensorFlow are affected by CVE-2021-37651?

CVE-2021-37651 affects TensorFlow versions 2.3.0 through 2.3.4, 2.4.0 through 2.4.3, and 2.5.0, including release candidates of 2.6.0.

4

What component of TensorFlow is impacted by CVE-2021-37651?

CVE-2021-37651 impacts the tf.raw_ops.FractionalAvgPoolGrad operation, which can lead to accessing data outside of allocated buffers.

5

Is there a patch for CVE-2021-37651?

Yes, TensorFlow has released patches in version 2.5.1 and 2.4.3 to fix the vulnerabilities related to CVE-2021-37651.

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