CVE-2021-37641: Heap OOB in `RaggedGather` in TensorFlow

Published Aug 12, 2021
·
Updated

Impact If the arguments to tf.rawops.RaggedGather don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. python import tensorflow as tf

tf.rawops.RaggedGather( paramsnestedsplits = [0,0,0], paramsdensevalues = [1,1], indices = [0,0,9,0,0], OUTPUTRAGGEDRANK=0)

In debug mode, the same code triggers a CHECK failure.

The implementation directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by paramsnestedsplits is not an empty list of tensors.

Patches We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.

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 if the arguments to tf.rawops.RaggedGather don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The implementation directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by paramsnestedsplits is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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 to a version that resolves this vulnerability.

    Fixed in 2.6.0Patch a2b743f6017d7b97af1fe49087ae15f0ac634373
  11. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Patch a2b743f6017d7b97af1fe49087ae15f0ac634373
  12. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.5.1Patch a2b743f6017d7b97af1fe49087ae15f0ac634373
  13. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.4.3Patch a2b743f6017d7b97af1fe49087ae15f0ac634373
  14. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.3.4Patch a2b743f6017d7b97af1fe49087ae15f0ac634373

Event History

Aug 12, 2021
CVE Published
via MITRE·08:30 PM
Data Sourced
via MITRE·08:30 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 CVE-2021-37641?

CVE-2021-37641 is a vulnerability in TensorFlow that could allow an attacker to read from outside the bounds of heap allocated buffers.

2

Which versions of TensorFlow are affected by CVE-2021-37641?

Versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, 2.6.0-rc0, 2.6.0-rc1, and 2.6.0-rc2 of TensorFlow are affected by CVE-2021-37641.

3

What is the severity of CVE-2021-37641?

CVE-2021-37641 has a severity rating of 7.1, which is considered high.

4

How can I fix CVE-2021-37641?

To fix CVE-2021-37641, update TensorFlow to a version that is not affected by the vulnerability (2.3.5 or above, 2.4.4 or above, or 2.6.0 or above).

5

Where can I find more information about CVE-2021-37641?

You can find more information about CVE-2021-37641 in the TensorFlow GitHub repository and the associated security advisories: [link1](https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373), [link2](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8).

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