First published: Thu Feb 03 2022(Updated: )
### Impact The [implementation of `QuantizedMaxPool`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/quantized_pooling_ops.cc#L114-L130) has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. ```python import tensorflow as tf tf.raw_ops.QuantizedMaxPool( input = tf.constant([[[[4]]]], dtype=tf.quint8), min_input = [], max_input = [1], ksize = [1, 1, 1, 1], strides = [1, 1, 1, 1], padding = "SAME", name=None ) ``` ### Patches We have patched the issue in GitHub commit [53b0dd6dc5957652f35964af16b892ec9af4a559](https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559). 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 |
Sign up to SecAlerts for real-time vulnerability data matched to your software, aggregated from hundreds of sources.
CVE-2022-21739 is classified as a moderate severity vulnerability.
To fix CVE-2022-21739, upgrade to TensorFlow version 2.7.1 or apply any available patches.
CVE-2022-21739 affects TensorFlow versions up to 2.5.2 and versions between 2.6.0 and 2.6.2, as well as version 2.7.0.
CVE-2022-21739 is a vulnerability related to undefined behavior in the QuantizedMaxPool implementation.
Yes, CVE-2022-21739 can potentially be exploited by attackers using user-controlled inputs.