First published: Fri May 20 2022(Updated: )
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
Credit: security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
Google TensorFlow | <2.6.4 | |
Google TensorFlow | >=2.7.0<2.7.2 | |
Google TensorFlow | =2.7.0-rc0 | |
Google TensorFlow | =2.7.0-rc1 | |
Google TensorFlow | =2.8.0 | |
Google TensorFlow | =2.8.0-rc0 | |
Google TensorFlow | =2.8.0-rc1 | |
Google TensorFlow | =2.9.0-rc0 | |
Google TensorFlow | =2.9.0-rc1 |
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CVE-2022-29211 has a medium-severity rating as it allows a crash due to processing of NaN values.
To fix CVE-2022-29211, upgrade to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later.
CVE-2022-29211 affects TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
The vulnerability in CVE-2022-29211 is caused by the `tf.histogram_fixed_width` function not handling NaN elements in the values array.
CVE-2022-29211 is not classified as a remote exploit since it requires manipulating the input data to trigger the crash.