Security readout for executives and security teams
Plain-English summary
This is a denial-of-service flaw in TensorFlow. A user who can make TensorFlow run crafted in-place operations can trigger a divide-by-zero condition and crash the affected process. The published impact is availability, not data theft or tampering.
Executive priority
Prioritize remediation where TensorFlow runs in shared, automated, or customer-influenced workflows. This is not described as remote code execution, but a crash in ML services can disrupt training, inference, or batch processing.
Technical view
TensorFlow in-place operations mishandle empty x and v arguments because the implementation used a logical OR where it should skip only when both are empty. This can cause a floating-point exception from division by zero. CVSS 3.1 is 5.5: local, low privilege, no user interaction, high availability impact.
Likely exposure
Exposure is limited to systems running affected TensorFlow releases: >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, or <2.3.4. Risk is highest where local users, jobs, plugins, notebooks, or data-processing pipelines can influence TensorFlow operation arguments.
Exploitation context
The source bundle does not state active exploitation, and KEV is false. The CVSS vector indicates local access with low privileges is required. Treat this mainly as a workload-crash risk in shared ML environments or automated pipelines.
Researcher notes
The key evidence is the GitHub advisory and fix commit. Validate the exact TensorFlow build, not only package metadata, especially in custom containers. The bundle does not identify exploit tooling, affected downstream products, or non-upgrade mitigations.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a patched supported release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 for affected branches.
- Check current vendor guidance before relying on unsupported branches.
- Restrict who can submit or modify TensorFlow workloads in shared environments.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Confirm affected branches include the fixed release or patched commit.
- Review shared ML platforms for untrusted users submitting TensorFlow jobs.
- Add regression coverage for malformed or empty in-place operation inputs.
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-2021-37660 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
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-cm5x-837x-jf3cCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618CVE 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.