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      <title>Meta Launches AutoPatchBench to Evaluate LLM Agents on Security Fixes</title>
      <link>https://www.infoq.com/news/2025/05/meta-autopatchbench-ai-patching/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Fuzz+Testing</link>
      <description>&lt;img src="https://res.infoq.com/news/2025/05/meta-autopatchbench-ai-patching/en/headerimage/meta-autopatchbench-1746638935976.jpeg"/&gt;&lt;p&gt;AutoPatchBench is a standardized benchmark designed to help researchers and developers evaluate and compare how effectively LLM agents can automatically patch security vulnerabilities in C/C++ native code.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>C</category>
      <category>Open Source</category>
      <category>Large language models</category>
      <category>Fuzz Testing</category>
      <category>C++</category>
      <category>Automated testing</category>
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      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Wed, 07 May 2025 18:00:00 GMT</pubDate>
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      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2025-05-07T18:00:00Z</dc:date>
      <dc:identifier>/news/2025/05/meta-autopatchbench-ai-patching/en</dc:identifier>
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