medium
6.5
CWE
190 400
Advisory Published
Advisory Published
Updated

CVE-2022-21733: Memory exhaustion in Tensorflow

First published: Thu Feb 03 2022(Updated: )

### Impact The [implementation of `StringNGrams`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/string_ngrams_op.cc#L29-L161) can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow: ```python import tensorflow as tf tf.raw_ops.StringNGrams( data=['123456'], data_splits=[0,1], separator='a'*15, ngram_widths=[], left_pad='', right_pad='', pad_width=-5, preserve_short_sequences=True) ``` We are missing a validation on `pad_witdh` and that result in computing a negative value for `ngram_width` which is later used to allocate parts of the output. ### Patches We have patched the issue in GitHub commit [f68fdab93fb7f4ddb4eb438c8fe052753c9413e8](https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.

Credit: security-advisories@github.com security-advisories@github.com

Affected SoftwareAffected VersionHow to fix
Google TensorFlow<=2.5.2
Google TensorFlow>=2.6.0<=2.6.2
Google TensorFlow=2.7.0
pip/tensorflow-gpu=2.7.0
2.7.1
pip/tensorflow-gpu>=2.6.0<2.6.3
2.6.3
pip/tensorflow-gpu<2.5.3
2.5.3
pip/tensorflow-cpu=2.7.0
2.7.1
pip/tensorflow-cpu>=2.6.0<2.6.3
2.6.3
pip/tensorflow-cpu<2.5.3
2.5.3
pip/tensorflow=2.7.0
2.7.1
pip/tensorflow>=2.6.0<2.6.3
2.6.3
pip/tensorflow<2.5.3
2.5.3

Remedy

https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8

Remedy

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g

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Reference Links

Frequently Asked Questions

  • What is the severity of CVE-2022-21733?

    CVE-2022-21733 has high severity due to its potential to cause a denial of service through out-of-memory conditions.

  • How do I fix CVE-2022-21733?

    To fix CVE-2022-21733, you should upgrade to TensorFlow version 2.5.3, 2.6.3, or 2.7.1 depending on your current version.

  • Which versions of TensorFlow are affected by CVE-2022-21733?

    CVE-2022-21733 affects TensorFlow versions up to 2.5.2 and between 2.6.0 to 2.6.2, as well as 2.7.0.

  • What type of attack does CVE-2022-21733 enable?

    CVE-2022-21733 enables a denial of service attack by triggering an out-of-memory condition.

  • Is CVE-2022-21733 related to a specific TensorFlow component?

    Yes, CVE-2022-21733 is related to the implementation of 'StringNGrams' in TensorFlow.

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