CVE-2021-37660: Division by 0 in inplace operations in TensorFlow
Impact An attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0:
python import tensorflow as tf
tf.rawops.InplaceSub(x=[],i=[-99,-1,-1],v=[1,1,1])
The implementation has a logic error: it should skip processing if x and v are empty but the code uses || instead of &&.
Patches We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618.
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 a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The implementation has a logic error: it should skip processing if x and v are empty but the code uses || instead of &&. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. 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 e86605c0a336c088b638da02135ea6f9f6753618 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1Patch e86605c0a336c088b638da02135ea6f9f6753618 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3Patch e86605c0a336c088b638da02135ea6f9f6753618 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.3.4Patch e86605c0a336c088b638da02135ea6f9f6753618
Event History
Frequently Asked Questions
What is the severity of CVE-2021-37660?
CVE-2021-37660 has a medium severity rating due to the potential for a denial of service via a floating point exception.
How do I fix CVE-2021-37660?
To fix CVE-2021-37660, upgrade to a patched version of TensorFlow, such as 2.5.1 or newer.
Which versions of TensorFlow are affected by CVE-2021-37660?
CVE-2021-37660 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and specific release candidates of 2.6.0.
What type of vulnerability is CVE-2021-37660?
CVE-2021-37660 is a vulnerability that allows an attacker to trigger a floating point exception through crafted arguments.
Is there a workaround for CVE-2021-37660?
There is no official workaround for CVE-2021-37660; the recommended action is to update to a secure version.