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  <title>Most recent entries from all</title>
  <updated>2026-10-07T14:39:22.259133+00:00</updated>
  <author>
    <name>Vulnerability-Lookup</name>
    <email>csirt@opendfir.org</email>
  </author>
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  <subtitle>Contains only the most 10 recent entries.</subtitle>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/bit-tensorflow-2022-35973</id>
    <title>BIT-tensorflow-2022-35973 — Segfault in `QuantizedMatMul` in TensorFlow</title>
    <updated>2026-10-07T14:39:22.282408+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> Bitnami: tensorflow</p>
<p>TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. 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.</p></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/bit-tensorflow-2022-35973"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/euvd-2026-233637</id>
    <title>EUVD-2026-233637</title>
    <updated>2026-10-07T14:39:22.282480+00:00</updated>
    <content>EUVD-2026-233637</content>
    <link href="https://cve.radiocsirt.org/vuln/euvd-2026-233637"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/fkie_cve-2022-35973</id>
    <title>fkie_cve-2022-35973</title>
    <updated>2026-10-07T14:39:22.282502+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml">
        <p>TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. 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.</p>
      </div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/fkie_cve-2022-35973"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/ghsa-689c-r7h2-fv9v</id>
    <title>GHSA-689c-r7h2-fv9v — TensorFlow vulnerable to segfault in `QuantizedMatMul`</title>
    <updated>2026-10-07T14:39:22.282544+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu</p>
<p>### Impact
If `QuantizedMatMul` is given nonscalar input for:
 - `min_a`
 - `max_a`
 - `min_b`
 - `max_b`
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf</p>
<p>Toutput = tf.qint32
transpose_a = False
transpose_b = False
Tactivation = tf.quint8
a = tf.constant(7, shape=[3,4], dtype=tf.quint8)
b = tf.constant(1, shape=[2,3], dtype=tf.quint8)
min_a = tf.constant([], shape=[0], dtype=tf.float32)
max_a = tf.constant(0, shape=[1], dtype=tf.float32)
min_b = tf.constant(0, shape=[1], dtype=tf.float32)
max_b = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation)
```</p>
<p>### Patches
We have patched the issue in GitHub commit [aca766ac7693bf29ed0df55ad6bfcc78f35e7f48](https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48).</p>
<p>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.</p>
<p>### 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.</p>
<p>### Attribution
This vulnerability has been reported by Neophytos Christou, Secure S…</p></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/ghsa-689c-r7h2-fv9v"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/gsd-2022-35973</id>
    <title>gsd-2022-35973</title>
    <updated>2026-10-07T14:39:22.282631+00:00</updated>
    <content>gsd-2022-35973</content>
    <link href="https://cve.radiocsirt.org/vuln/gsd-2022-35973"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/opensuse-su-2024:12355-1</id>
    <title>openSUSE-SU-2024:12355-1 — tensorflow-lite-2.10.0-1.1 on GA media</title>
    <updated>2026-10-07T14:39:22.282650+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml">
        <p>tensorflow-lite-2.10.0-1.1 on GA media</p>
      </div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/opensuse-su-2024:12355-1"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/pysec-2026-3119</id>
    <title>PYSEC-2026-3119 — TensorFlow vulnerable to segfault in `QuantizedMatMul`</title>
    <updated>2026-10-07T14:39:22.282715+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> PyPI: tensorflow</p>
<p>### Impact
If `QuantizedMatMul` is given nonscalar input for:
 - `min_a`
 - `max_a`
 - `min_b`
 - `max_b`
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf</p>
<p>Toutput = tf.qint32
transpose_a = False
transpose_b = False
Tactivation = tf.quint8
a = tf.constant(7, shape=[3,4], dtype=tf.quint8)
b = tf.constant(1, shape=[2,3], dtype=tf.quint8)
min_a = tf.constant([], shape=[0], dtype=tf.float32)
max_a = tf.constant(0, shape=[1], dtype=tf.float32)
min_b = tf.constant(0, shape=[1], dtype=tf.float32)
max_b = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation)
```</p>
<p>### Patches
We have patched the issue in GitHub commit [aca766ac7693bf29ed0df55ad6bfcc78f35e7f48](https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48).</p>
<p>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.</p>
<p>### 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.</p>
<p>### Attribution
This vulnerability has been reported by Neophytos Christou, Secure S…</p></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/pysec-2026-3119"/>
  </entry>
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