<?xml version='1.0' encoding='UTF-8'?>
<?xml-stylesheet href="/static/style.xsl" type="text/xsl"?>
<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en">
  <id>https://cve.radiocsirt.org/rss/recent/all/10</id>
  <title>Most recent entries from all</title>
  <updated>2026-10-08T20:59:49.796167+00:00</updated>
  <author>
    <name>Vulnerability-Lookup</name>
    <email>csirt@opendfir.org</email>
  </author>
  <link href="https://cve.radiocsirt.org" rel="alternate"/>
  <generator uri="https://lkiesow.github.io/python-feedgen" version="1.0.0">python-feedgen</generator>
  <subtitle>Contains only the most 10 recent entries.</subtitle>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/cve-2022-23593</id>
    <title>CVE-2022-23593 — Segfault in `simplifyBroadcast` in Tensorflow</title>
    <updated>2026-10-08T20:59:49.799045+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> tensorflow</p>
<p>Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.</p></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/cve-2022-23593"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/ghsa-gwcx-jrx4-92w2</id>
    <title>GHSA-gwcx-jrx4-92w2 — Segfault in `simplifyBroadcast` in Tensorflow</title>
    <updated>2026-10-08T20:59:49.799099+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
The [`simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205) is vulnerable to a segfault (hence, denial of service), if called with scalar shapes.</p>
<p>```cc 
  size_t maxRank = 0;
  for (auto shape : llvm::enumerate(shapes)) {
    auto found_shape = analysis.dimensionsForShapeTensor(shape.value());
    if (!found_shape) return {};
    shapes_found.push_back(*found_shape);
    maxRank = std::max(maxRank, found_shape-&gt;size());
  }</p>
<p>SmallVector&lt;const ShapeComponentAnalysis::SymbolicDimension*&gt;
      joined_dimensions(maxRank);
```</p>
<p>If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`.</p>
<p>### Patches
We have patched the issue in GitHub commit [35f0fabb4c178253a964d7aabdbb15c6a398b69a](https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a).</p>
<p>The fix will be included in TensorFlow 2.8.0. This is the only affected version.</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></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/ghsa-gwcx-jrx4-92w2"/>
  </entry>
</feed>
