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  <DocumentTitle xml:lang="en">CVE-2021-29591</DocumentTitle>
  <DocumentType>SUSE CVE</DocumentType>
  <DocumentPublisher Type="Vendor">
    <ContactDetails>security@suse.de</ContactDetails>
    <IssuingAuthority>SUSE Security Team</IssuingAuthority>
  </DocumentPublisher>
  <DocumentTracking>
    <Identification>
      <ID>SUSE CVE-2021-29591</ID>
    </Identification>
    <Status>Interim</Status>
    <Version>1</Version>
    <RevisionHistory>
      <Revision>
        <Number>6</Number>
        <Date>2025-02-17T00:42:58Z</Date>
        <Description>current</Description>
      </Revision>
    </RevisionHistory>
    <InitialReleaseDate>2021-05-30T14:49:55Z</InitialReleaseDate>
    <CurrentReleaseDate>2025-02-17T00:42:58Z</CurrentReleaseDate>
    <Generator>
      <Engine>cve-database/bin/generate-cvrf-cve.pl</Engine>
      <Date>2020-12-27T01:00:00Z</Date>
    </Generator>
  </DocumentTracking>
  <DocumentNotes>
    <Note Title="CVE" Type="Summary" Ordinal="1" xml:lang="en">CVE-2021-29591</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. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the `While` implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space. 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. 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.</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>
  </DocumentReferences>
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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. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the `While` implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space. 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. 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.</Note>
    </Notes>
    <CVE>CVE-2021-29591</CVE>
    <ProductStatuses/>
    <Threats>
      <Threat Type="Impact">
        <Description>important</Description>
      </Threat>
    </Threats>
    <CVSSScoreSets>
      <ScoreSetV2>
        <BaseScoreV2>4.6</BaseScoreV2>
        <VectorV2>AV:L/AC:L/Au:N/C:P/I:P/A:P</VectorV2>
      </ScoreSetV2>
      <ScoreSetV3>
        <BaseScoreV3>7.8</BaseScoreV3>
        <VectorV3>CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H</VectorV3>
      </ScoreSetV3>
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