CVE-2022-21735: Division by zero in Tensorflow
First published: Thu Feb 03 2022(Updated: )
### Impact The [implementation of `FractionalMaxPool`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192) can be made to crash a TensorFlow process via a division by 0: ```python import tensorflow as tf import numpy as np tf.raw_ops.FractionalMaxPool( value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64), pooling_ratio=[1.0, 1.44, 1.73, 1.0], pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None) ``` ### Patches We have patched the issue in GitHub commit [ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb](https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb). 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 Faysal Hossain Shezan from University of Virginia.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How 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 |
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Reference Links
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192
- https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj
- https://nvd.nist.gov/vuln/detail/CVE-2022-21735
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-59.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-114.yaml
- https://github.com/advisories/GHSA-87v6-crgm-2gfj (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-21735?
CVE-2022-21735 is considered a high severity vulnerability due to its potential to crash a TensorFlow process.
How do I fix CVE-2022-21735?
To fix CVE-2022-21735, upgrade TensorFlow to versions 2.5.3, 2.6.3, or 2.7.1 or later.
What is the impact of CVE-2022-21735?
The impact of CVE-2022-21735 can lead to a denial of service caused by a division by zero error in the TensorFlow implementation.
Which versions of TensorFlow are affected by CVE-2022-21735?
CVE-2022-21735 affects TensorFlow versions up to 2.5.2, between 2.6.0 and 2.6.2, and the specific version 2.7.0.
What components in TensorFlow are vulnerable due to CVE-2022-21735?
The vulnerability in CVE-2022-21735 resides in the implementation of the FractionalMaxPool operation within TensorFlow.
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