CVE-2021-37665: Incomplete validation in MKL requantization in TensorFlow
Impact Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
python import tensorflow as tf
tf.rawops.RequantizationRangePerChannel( input=[], inputmin=[0,0,0,0,0], inputmax=[1,1,1,1,1], clipvaluemax=1) The implementation does not validate the dimensions of the input tensor.
A similar issue occurs in MklRequantizePerChannelOp:
python import tensorflow as tf from tensorflow.python.ops import genmathops
genmathops.requantizeperchannel( input=[], inputmin=[-100,-100,-100,-100,-100], inputmax=[-100,-100,-100], requestedoutputmin=[-100,-100,-100,-100,-100], requestedoutputmax=[], outtype=tf.int)
The implementation does not perform full validation for all the input arguments.
Patches We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.
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 due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation does not validate the dimensions of the input tensor. A similar issue occurs in MklRequantizePerChannelOp. The implementation does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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
Remediation
Recommended actions to resolve this vulnerability, in priority order.
- Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.6.0Patch GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Patch GitHub commit 203214568f5bc237603dbab6e1fd389f1572f5c9 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1Patch GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3Patch GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.3.4Patch GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69
Event History
Frequently Asked Questions
What is the severity of CVE-2021-37665?
CVE-2021-37665 has been classified as a high severity vulnerability due to the risk of undefined behavior and potential data leak.
How do I fix CVE-2021-37665?
To fix CVE-2021-37665, users should upgrade to TensorFlow version 2.5.1 or later, or update to 2.4.3 or 2.3.4 depending on their version.
What software is affected by CVE-2021-37665?
CVE-2021-37665 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0, as well as release candidates of version 2.6.0.
What type of vulnerabilities does CVE-2021-37665 include?
CVE-2021-37665 includes vulnerabilities related to incomplete validation in the MKL implementation leading to memory access issues.
Is CVE-2021-37665 being actively exploited?
As of now, there is no public information indicating that CVE-2021-37665 is being actively exploited in the wild.