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    <lastBuildDate>Wed, 07 Oct 2026 18:26:31 +0000</lastBuildDate>
    <item>
      <title>CVE-2022-36027 — Segfault TFLite converter on per-channel quantized transposed convolutions in TensorFlow</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2022-36027</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. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450. 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. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450. 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-2022-36027</guid>
    </item>
    <item>
      <title>GHSA-79h2-q768-fpxr — TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-79h2-q768-fpxr</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
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;class QuantConv2DTransposed(tf.keras.layers.Layer):
    def build(self, input_shape):
        self.kernel = self.add_weight(&amp;#34;kernel&amp;#34;, [3, 3, input_shape[-1], 24])&lt;/p&gt;
&lt;p&gt;def call(self, inputs):
        filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(
            self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True
        )
        filters = tf.transpose(filters, (0, 1, 3, 2))
        return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1)&lt;/p&gt;
&lt;p&gt;inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)
x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True)
x = QuantConv2DTransposed()(x)
x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True)&lt;/p&gt;
&lt;p&gt;model = tf.keras.Model(inp, x)&lt;/p&gt;
&lt;p&gt;model.save(&amp;#34;/tmp/testing&amp;#34;)
converter = tf.lite.TFLiteConverter.from_saved_model(&amp;#34;/tmp/testing&amp;#34;)
converter.optimizations = [tf.lite.Optimize.DEFAULT]&lt;/p&gt;
&lt;p&gt;# terminated by signal SIGSEGV (Address boundary error)
tflite_model = converter.convert()
```&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [aa0b852a4588cea4d36b74feb05d93055540b450](https://github.com/tensorflow/tensorflow/commit/aa0b852a4588cea4d36b74feb05d93055540b450).&lt;/p&gt;
&lt;p&gt;The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow…&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
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.
```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;class QuantConv2DTransposed(tf.keras.layers.Layer):
    def build(self, input_shape):
        self.kernel = self.add_weight(&amp;#34;kernel&amp;#34;, [3, 3, input_shape[-1], 24])&lt;/p&gt;
&lt;p&gt;def call(self, inputs):
        filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(
            self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True
        )
        filters = tf.transpose(filters, (0, 1, 3, 2))
        return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1)&lt;/p&gt;
&lt;p&gt;inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)
x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True)
x = QuantConv2DTransposed()(x)
x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True)&lt;/p&gt;
&lt;p&gt;model = tf.keras.Model(inp, x)&lt;/p&gt;
&lt;p&gt;model.save(&amp;#34;/tmp/testing&amp;#34;)
converter = tf.lite.TFLiteConverter.from_saved_model(&amp;#34;/tmp/testing&amp;#34;)
converter.optimizations = [tf.lite.Optimize.DEFAULT]&lt;/p&gt;
&lt;p&gt;# terminated by signal SIGSEGV (Address boundary error)
tflite_model = converter.convert()
```&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [aa0b852a4588cea4d36b74feb05d93055540b450](https://github.com/tensorflow/tensorflow/commit/aa0b852a4588cea4d36b74feb05d93055540b450).&lt;/p&gt;
&lt;p&gt;The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-79h2-q768-fpxr</guid>
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