CVE-2021-37669: Crash in NMS ops caused by integer conversion to unsigned in TensorFlow

Published Aug 12, 2021
·
Updated

Impact An attacker can cause denial of service in applications serving models using tf.rawops.NonMaxSuppressionV5 by triggering a division by 0:

python import tensorflow as tf

tf.rawops.NonMaxSuppressionV5( boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]], scores=[1.0,2.0,3.0], maxoutputsize=-1, iouthreshold=0.5, scorethreshold=0.5, softnmssigma=1.0, padtomaxoutputsize=True) The implementation uses a user controlled argument to resize a std::vector:

cc const int outputsize = maxoutputsize.scalar<int>()(); // ... std::vector<int> selected; // ... if (padtomaxoutputsize) { selected.resize(outputsize, 0); // ... } However, as std::vector::resize takes the size argument as a sizet and outputsize is an int, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.

A similar issue occurs in CombinedNonMaxSuppression:

python import tensorflow as tf

tf.rawops.NonMaxSuppressionV5( boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]], scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]], maxoutputsizeperclass=-1, maxtotalsize=10, iouthreshold=scorethreshold=0.5, padperclass=True, clipboxes=True) Patches We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.

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 an attacker can cause denial of service in applications serving models using tf.rawops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a sizet and outputsize is an int, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in CombinedNonMaxSuppression. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d
  11. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Patch b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58
  12. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.6.0
  13. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.5.1
  14. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.4.3
  15. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.3.4
  16. Compensating control

    For applications serving models using tf.raw_ops.NonMaxSuppressionV5, do not allow user-controlled max_output_size/max_output_size_per_class values to be negative (negative values can trigger the crash due to implicit conversion when resizing a std::vector).

Event History

Aug 12, 2021
CVE Published
via MITRE·10:55 PM
Data Sourced
via MITRE·10:55 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·11:15 PM
RemedyDescriptionSeverityWeaknessAffected Software
Aug 25, 2021
Advisory Published
via GitHub·02:42 PM
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Frequently Asked Questions

1

What is the severity of CVE-2021-37669?

CVE-2021-37669 has a high severity rating due to its potential to cause denial of service.

2

How do I fix CVE-2021-37669?

To fix CVE-2021-37669, upgrade to TensorFlow version 2.5.1 or later.

3

Which versions of TensorFlow are affected by CVE-2021-37669?

CVE-2021-37669 affects TensorFlow versions from 2.3.0 to 2.6.0-rc2.

4

What causes the denial of service in CVE-2021-37669?

The denial of service in CVE-2021-37669 is caused by a division by zero triggered in certain TensorFlow operations.

5

Is CVE-2021-37669 related to any specific TensorFlow operations?

CVE-2021-37669 is specifically related to the `tf.raw_ops.NonMaxSuppressionV5` operation in TensorFlow.

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