CVE-2021-41207: Division by zero in `ParallelConcat`
Impact The implementation of ParallelConcat misses some input validation and can produce a division by 0:
python import tensorflow as tf
@tf.function def test(): y = tf.rawops.ParallelConcat(values=[['tf']],shape=0) return y
test()
Patches We have patched the issue in GitHub commit f2c3931113eaafe9ef558faaddd48e00a6606235.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 open source platform for machine learning. In affected versions the implementation of ParallelConcat misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2021-41207?
CVE-2021-41207 is considered a moderate severity vulnerability due to its potential for division by zero error.
How do I fix CVE-2021-41207?
To fix CVE-2021-41207, update TensorFlow to version 2.4.4, 2.5.2, or 2.6.1 or higher.
Which versions of TensorFlow are affected by CVE-2021-41207?
CVE-2021-41207 affects TensorFlow versions from 2.4.0 to 2.4.4, 2.6.0 to 2.6.1, and specific release candidates of 2.7.0.
What kind of issue does CVE-2021-41207 cause?
CVE-2021-41207 can cause a division by zero error due to insufficient input validation in the ParallelConcat implementation.
Is CVE-2021-41207 a critical vulnerability?
No, CVE-2021-41207 is not classified as a critical vulnerability, but it requires attention to prevent unexpected behavior.