CVE-2021-37644: `std::abort` raised from `TensorListReserve` in TensorFlow

Published Aug 12, 2021
·
Updated

Impact Providing a negative element to numelements list argument of tf.rawops.TensorListReserve causes the runtime to abort the process due to reallocating a std::vector to have a negative number of elements:

python import tensorflow as tf

tf.rawops.TensorListReserve( elementshape = tf.constant([1]), numelements=tf.constant([-1]), elementdtype = tf.int32)

The implementation calls std::vector.resize() with the new size controlled by input given by the user, without checking that this input is valid.

Patches We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.

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 providing a negative element to numelements list argument of tf.rawops.TensorListReserve causes the runtime to abort the process due to reallocating a std::vector to have a negative number of elements. The implementation calls std::vector.resize() with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. 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.

    Patch 8a6e874437670045e6c7dc6154c7412b4a2135e2
  11. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.6.0Patch 8a6e874437670045e6c7dc6154c7412b4a2135e2
  12. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.5.1Patch 8a6e874437670045e6c7dc6154c7412b4a2135e2
  13. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.4.3Patch 8a6e874437670045e6c7dc6154c7412b4a2135e2
  14. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.3.4Patch 8a6e874437670045e6c7dc6154c7412b4a2135e2

Event History

Aug 12, 2021
CVE Published
via MITRE·08:35 PM
Data Sourced
via MITRE·08:35 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-2021-37644?

CVE-2021-37644 has a severity rating of medium as it causes a runtime abort when a negative number is provided to `num_elements`.

2

How do I fix CVE-2021-37644?

To fix CVE-2021-37644, upgrade to TensorFlow version 2.3.4, 2.4.3, or 2.5.1 depending on your current version.

3

Which versions of TensorFlow are affected by CVE-2021-37644?

CVE-2021-37644 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, as well as version 2.5.0, and 2.6.0-rc0, rc1, and rc2.

4

What happens if I exploit CVE-2021-37644?

Exploiting CVE-2021-37644 by passing a negative `num_elements` can cause the TensorFlow process to abort, leading to service disruption.

5

Is CVE-2021-37644 a client-side or server-side vulnerability?

CVE-2021-37644 is considered a server-side vulnerability as it affects the runtime behavior of TensorFlow on the server.

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