Security readout for executives and security teams
Plain-English summary
Affected TensorFlow versions can crash when a specific sparse/dense division operation hits a zero-division case. This is an availability issue, not a data theft issue. Business urgency is moderate and depends on whether untrusted users or jobs can run TensorFlow workloads in your environment.
Executive priority
Treat as a moderate availability risk. Prioritize patching shared ML platforms, hosted notebooks, and any service where untrusted users can run TensorFlow workloads. Lower priority is reasonable for isolated, trusted-only research environments.
Technical view
CVE-2021-37636 is a CWE-369 divide-by-zero flaw in tf.raw_ops.SparseDenseCwiseDiv. TensorFlow reused shared binary-operation logic without special handling for division by zero, causing a floating point exception. CVSS 3.1 is 5.5: local attack vector, low complexity, low privileges, no user interaction, high availability impact.
Likely exposure
Exposure is limited to TensorFlow installations in the affected version ranges: before 2.3.4, 2.4.0 through before 2.4.3, and 2.5.0 through before 2.5.1. Risk is higher where users can submit or execute TensorFlow models, notebooks, or jobs.
Exploitation context
The source bundle does not show active exploitation, and KEV is false. The described impact is denial of service through a local, low-privileged ability to run the affected TensorFlow operation. No confidentiality or integrity impact is identified in the supplied CVSS vector.
Researcher notes
The public details identify a divide-by-zero condition in SparseDenseCwiseDiv and cite TensorFlow commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9 as the patch. Evidence supports crash/availability impact only. The bundle does not provide evidence of remote exploitation or broader product impact.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or later where practical.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 for supported older branches.
- Identify containers, notebooks, and services bundling affected TensorFlow versions.
- Restrict untrusted TensorFlow workload execution until upgraded.
- Monitor TensorFlow advisory channels for any additional guidance.
Validation and detection
- Check runtime and dependency lockfiles for affected TensorFlow versions.
- Confirm deployed container images include a patched TensorFlow build.
- Review ML job platforms for tenant-supplied TensorFlow execution paths.
- Search codebases for use of tf.raw_ops.SparseDenseCwiseDiv.
- Run normal regression tests after upgrading TensorFlow.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37636 mapping review
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Open ATT&CK lookup- Severity
- Medium
- CVSS
- 5.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
CVSS vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H1.83.6Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
5.5MediumVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hp4c-x6r7-6555CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9CVE reference · x_refsource_MISC
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Divide By Zero
Divide By Zero represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.