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    <title>Most recent entries from all</title>
    <link>https://cve.radiocsirt.org</link>
    <description>Contains only the most 10 recent entries.</description>
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    <lastBuildDate>Fri, 16 Oct 2026 17:18:48 +0000</lastBuildDate>
    <item>
      <title>BIT-tensorflow-2021-29520 — Heap buffer overflow in `Conv3DBackprop*`</title>
      <link>https://cve.radiocsirt.org/vuln/bit-tensorflow-2021-29520</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Bitnami: tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Bitnami: tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/bit-tensorflow-2021-29520</guid>
    </item>
    <item>
      <title>cnvd-2021-46660</title>
      <link>https://cve.radiocsirt.org/vuln/cnvd-2021-46660</link>
      <description>cnvd-2021-46660</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/cnvd-2021-46660</guid>
    </item>
    <item>
      <title>EUVD-2026-26379</title>
      <link>https://cve.radiocsirt.org/vuln/euvd-2026-26379</link>
      <description>EUVD-2026-26379</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/euvd-2026-26379</guid>
    </item>
    <item>
      <title>fkie_cve-2021-29520</title>
      <link>https://cve.radiocsirt.org/vuln/fkie_cve-2021-29520</link>
      <description>&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/fkie_cve-2021-29520</guid>
    </item>
    <item>
      <title>GHSA-wcv5-qrj6-9pfm — Heap buffer overflow in `Conv3DBackprop*`</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-wcv5-qrj6-9pfm</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
Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows:&lt;/p&gt;
&lt;p&gt;```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;input_sizes = tf.constant([1, 1, 1, 1, 2], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([734.6274508233133, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
                            -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
                            -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[4, 1, 6, 1, 1], dtype=tf.float32)
out_backprop = tf.constant([-10.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)&lt;/p&gt;
&lt;p&gt;tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 89, 29, 89, 1], padding=&amp;#39;SAME&amp;#39;, data_format=&amp;#39;NDHWC&amp;#39;, dilations=[1, 1, 1, 1, 1])
```
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;input_values = [-10.0] * (7 * 7 * 7 * 7 * 7)
input_values[0] = 429.6491056791816
input_sizes = tf.constant(input_values, shape=[7, 7, 7, 7, 7], dtype=tf.float32)
filter_tensor = tf.constant([7, 7, 7, 1, 1], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[7, 1, 1, 1, 1], dtype=tf.float32)
  
tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 37, 65, 93, 1], padding=&amp;#39;VALID&amp;#39;, data_format=&amp;#39;NDHWC&amp;#39;, dilations=[1, 1, 1, 1, 1])
```&lt;/p&gt;
&lt;p&gt;This is because the [implementation](https://github.com/tensorflow/tensorflow/bl…&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
Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows:&lt;/p&gt;
&lt;p&gt;```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;input_sizes = tf.constant([1, 1, 1, 1, 2], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([734.6274508233133, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
                            -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
                            -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[4, 1, 6, 1, 1], dtype=tf.float32)
out_backprop = tf.constant([-10.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)&lt;/p&gt;
&lt;p&gt;tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 89, 29, 89, 1], padding=&amp;#39;SAME&amp;#39;, data_format=&amp;#39;NDHWC&amp;#39;, dilations=[1, 1, 1, 1, 1])
```
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;input_values = [-10.0] * (7 * 7 * 7 * 7 * 7)
input_values[0] = 429.6491056791816
input_sizes = tf.constant(input_values, shape=[7, 7, 7, 7, 7], dtype=tf.float32)
filter_tensor = tf.constant([7, 7, 7, 1, 1], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[7, 1, 1, 1, 1], dtype=tf.float32)
  
tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 37, 65, 93, 1], padding=&amp;#39;VALID&amp;#39;, data_format=&amp;#39;NDHWC&amp;#39;, dilations=[1, 1, 1, 1, 1])
```&lt;/p&gt;
&lt;p&gt;This is because the [implementation](https://github.com/tensorflow/tensorflow/bl…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-wcv5-qrj6-9pfm</guid>
    </item>
    <item>
      <title>gsd-2021-29520</title>
      <link>https://cve.radiocsirt.org/vuln/gsd-2021-29520</link>
      <description>gsd-2021-29520</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/gsd-2021-29520</guid>
    </item>
    <item>
      <title>PYSEC-2021-157</title>
      <link>https://cve.radiocsirt.org/vuln/pysec-2021-157</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/pysec-2021-157</guid>
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