CVE-2021-37645: Integer overflow due to conversion to unsigned in TensorFlow
Impact The implementation of tf.rawops.QuantizeAndDequantizeV4Grad is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value.
python import tensorflow as tf
tf.rawops.QuantizeAndDequantizeV4Grad( gradients=[1.0,2.0], input=[1.0,1.0], inputmin=[0.0], inputmax=[10.0], axis=-100)
The implementation uses the axis value as the size argument to absl::InlinedVector constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer.
Patches We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, 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 the implementation of tf.rawops.QuantizeAndDequantizeV4Grad is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation uses the axis value as the size argument to absl::InlinedVector constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, 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-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/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
tensorflowto a version that resolves this vulnerability.Fixed in 2.6.0Patch 96f364a1ca3009f98980021c4b32be5fdcca33a1 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1Patch 96f364a1ca3009f98980021c4b32be5fdcca33a1 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3Patch 96f364a1ca3009f98980021c4b32be5fdcca33a1
Event History
Frequently Asked Questions
What is the severity of CVE-2021-37645?
The severity of CVE-2021-37645 has not been explicitly classified, but it involves an integer overflow, which can lead to potential vulnerabilities in the affected versions of TensorFlow.
How do I fix CVE-2021-37645?
To fix CVE-2021-37645, upgrade TensorFlow to version 2.5.1 or 2.4.3, depending on your previous version.
Which versions of TensorFlow are affected by CVE-2021-37645?
CVE-2021-37645 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0.
What type of vulnerability is CVE-2021-37645?
CVE-2021-37645 is an integer overflow vulnerability resulting from improper memory allocation.
Is CVE-2021-37645 a critical vulnerability?
While CVE-2021-37645 involves an integer overflow, its criticality depends on its exploitation context; additional evaluation may be required to determine impact.