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  <DocumentTitle xml:lang="en">CVE-2021-29580</DocumentTitle>
  <DocumentType>SUSE CVE</DocumentType>
  <DocumentPublisher Type="Vendor">
    <ContactDetails>security@suse.de</ContactDetails>
    <IssuingAuthority>SUSE Security Team</IssuingAuthority>
  </DocumentPublisher>
  <DocumentTracking>
    <Identification>
      <ID>SUSE CVE-2021-29580</ID>
    </Identification>
    <Status>Interim</Status>
    <Version>1</Version>
    <RevisionHistory>
      <Revision>
        <Number>6</Number>
        <Date>2025-02-17T00:43:08Z</Date>
        <Description>current</Description>
      </Revision>
    </RevisionHistory>
    <InitialReleaseDate>2021-05-30T14:49:52Z</InitialReleaseDate>
    <CurrentReleaseDate>2025-02-17T00:43:08Z</CurrentReleaseDate>
    <Generator>
      <Engine>cve-database/bin/generate-cvrf-cve.pl</Engine>
      <Date>2020-12-27T01:00:00Z</Date>
    </Generator>
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  <DocumentNotes>
    <Note Title="CVE" Type="Summary" Ordinal="1" xml:lang="en">CVE-2021-29580</Note>
    <Note Title="Mitre CVE Description" Type="Description" Ordinal="2" xml:lang="en">TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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.</Note>
    <Note Title="Terms of Use" Type="Legal Disclaimer" Ordinal="4" xml:lang="en">The CVRF data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).</Note>
  </DocumentNotes>
  <DocumentReferences>
    <Reference Type="Self">
      <URL>https://www.suse.com/support/security/rating/</URL>
      <Description>SUSE Security Ratings</Description>
    </Reference>
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    <Notes>
      <Note Title="Vulnerability Description" Type="General" Ordinal="1" xml:lang="en">TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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.</Note>
    </Notes>
    <CVE>CVE-2021-29580</CVE>
    <ProductStatuses/>
    <Threats>
      <Threat Type="Impact">
        <Description>important</Description>
      </Threat>
    </Threats>
    <CVSSScoreSets>
      <ScoreSetV2>
        <BaseScoreV2>2.1</BaseScoreV2>
        <VectorV2>AV:L/AC:L/Au:N/C:N/I:N/A:P</VectorV2>
      </ScoreSetV2>
      <ScoreSetV3>
        <BaseScoreV3>5.5</BaseScoreV3>
        <VectorV3>CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H</VectorV3>
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