CVE-2021-29603: Heap OOB write in TFLite

Published May 14, 2021
·
Updated

Impact A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of ArgMin/ArgMax:

cc TfLiteIntArray outputdims = TfLiteIntArrayCreate(NumDimensions(input) - 1); int j = 0; for (int i = 0; i < NumDimensions(input); ++i) { if (i != axisvalue) { outputdims->data[j] = SizeOfDimension(input, i); ++j; } }

If axisvalue is not a value between 0 and NumDimensions(input), then the condition in the if is never true, so code writes past the last valid element of outputdims->data. Patches We have patched the issue in GitHub commit c59c37e7b2d563967da813fa50fe20b21f4da683.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of ArgMin/ArgMax(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/argminmax.cc#L52-L59). If axisvalue is not a value between 0 and NumDimensions(input), then the condition in the if is never true, so code writes past the last valid element of outputdims->data. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Affected Software

16 affected componentsFixes available
pip/tensorflow-gpu>=2.4.0<2.4.2
2.4.2
pip/tensorflow-gpu>=2.3.0<2.3.3
2.3.3
pip/tensorflow-gpu>=2.2.0<2.2.3
2.2.3
pip/tensorflow-gpu<2.1.4
2.1.4
pip/tensorflow-cpu>=2.4.0<2.4.2
2.4.2
pip/tensorflow-cpu>=2.3.0<2.3.3
2.3.3
pip/tensorflow-cpu>=2.2.0<2.2.3
2.2.3
pip/tensorflow-cpu<2.1.4
2.1.4
pip/tensorflow>=2.4.0<2.4.2
2.4.2
pip/tensorflow>=2.3.0<2.3.3
2.3.3
pip/tensorflow>=2.2.0<2.2.3
2.2.3
pip/tensorflow<2.1.4
2.1.4
Google TensorFlow<2.1.4
Google TensorFlow>=2.2.0<2.2.3
Google TensorFlow>=2.3.0<2.3.3
Google TensorFlow>=2.4.0<2.4.2

Event History

May 14, 2021
CVE Published
via MITRE·07:21 PM
Data Sourced
via MITRE·07:21 PM
DescriptionSeverityWeakness
May 21, 2021
Advisory Published
via GitHub·02:28 PM

Frequently Asked Questions

1

What is the severity of CVE-2021-29603?

CVE-2021-29603 has been classified as a high severity vulnerability.

2

How do I fix CVE-2021-29603?

To remediate CVE-2021-29603, upgrade TensorFlow to version 2.4.2 or later.

3

What versions are affected by CVE-2021-29603?

CVE-2021-29603 affects TensorFlow versions prior to 2.1.4 and between 2.2.0 and 2.4.1.

4

What is the impact of CVE-2021-29603?

CVE-2021-29603 can cause an out-of-bounds write on the heap in the TFLite implementation of ArgMin/ArgMax.

5

Which package installations are involved with CVE-2021-29603?

CVE-2021-29603 involves installations of TensorFlow using pip, including tensorflow-gpu and tensorflow-cpu.

Contact

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