CVE-2021-37685: Heap OOB in TensorFlow Lite

Published Aug 12, 2021
·
Updated

Impact TFLite's expanddims.cc contains a vulnerability which allows reading one element outside of bounds of heap allocated data:

cc if (axis < 0) { axis = inputdims.size + 1 + axis; } TFLITEENSURE(context, axis <= inputdims.size);

TfLiteIntArray outputdims = TfLiteIntArrayCreate(inputdims.size + 1); for (int i = 0; i < outputdims->size; ++i) { if (i < axis) { outputdims->data[i] = inputdims.data[i]; } else if (i == axis) { outputdims->data[i] = 1; } else { outputdims->data[i] = inputdims.data[i - 1]; } }

If axis is a large negative value (e.g., -100000), then after the first if it would still be negative. The check following the if statement will pass and the for loop would read one element before the start of inputdims.data (when i = 0).

Patches We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.

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 Yakun Zhang of Baidu Security.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's expanddims.cc contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If axis is a large negative value (e.g., -100000), then after the first if it would still be negative. The check following the if statement will pass and the for loop would read one element before the start of inputdims.data (when i = 0). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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 d94ffe08a65400f898241c0374e9edc6fa8ed257
  11. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.5.1Patch d94ffe08a65400f898241c0374e9edc6fa8ed257
  12. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.4.3Patch d94ffe08a65400f898241c0374e9edc6fa8ed257
  13. Upgrade

    Upgrade TensorFlow to a version that resolves this vulnerability.

    Fixed in 2.3.4Patch d94ffe08a65400f898241c0374e9edc6fa8ed257

Event History

Aug 12, 2021
CVE Published
via MITRE·10:15 PM
Data Sourced
via MITRE·10:15 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·11:15 PM
RemedyDescriptionSeverityWeaknessAffected Software
Aug 25, 2021
Advisory Published
via GitHub·02:40 PM

Frequently Asked Questions

1

What is the severity of CVE-2021-37685?

CVE-2021-37685 has a high severity level due to the potential for out-of-bounds memory access.

2

How do I fix CVE-2021-37685?

To fix CVE-2021-37685, upgrade to TensorFlow version 2.5.1 or later.

3

Which versions of TensorFlow are affected by CVE-2021-37685?

CVE-2021-37685 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and specific 2.6.0 release candidates.

4

What type of vulnerability is CVE-2021-37685?

CVE-2021-37685 is an out-of-bounds read vulnerability that can lead to unauthorized data access.

5

Can CVE-2021-37685 be exploited remotely?

CVE-2021-37685 may be exploitable remotely, depending on how TensorFlow is used in an application.

Contact

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