Security readout for executives and security teams
Plain-English summary
This issue can crash affected TensorFlow applications that process models using `tf.raw_ops.UnravelIndex`. It is an availability risk, not a data theft or privilege escalation issue. Business urgency depends on whether vulnerable TensorFlow versions are used in model-serving paths that process untrusted inputs.
Executive priority
Treat as a moderate availability issue. Patch during the next controlled maintenance window, faster for customer-facing ML services or shared platforms where a crash could interrupt production workloads.
Technical view
Affected TensorFlow versions fail to reject an invalid `dims` tensor for `tf.raw_ops.UnravelIndex`. If a dimension value is zero, the kernel can divide by zero and cause denial of service. The vendor patched this in commit a776040a and included fixes in TensorFlow 2.6.0, 2.5.1, 2.4.3, and 2.3.4.
Likely exposure
Exposure is likely limited to environments running affected TensorFlow versions and executing models or code paths that use `tf.raw_ops.UnravelIndex`. The CVSS vector indicates local access and low privileges, with high availability impact and no confidentiality or integrity impact.
Exploitation context
The provided sources do not show public exploitation or CISA KEV listing. The advisory describes denial of service through malformed dimensions reaching the vulnerable operation. No exploit steps should be inferred from the sources.
Researcher notes
The root cause is missing validation that `dims` is non-empty and contains no zero dimension before division. The source bundle ties the issue to CWE-369 and TensorFlow commit a776040a. Evidence is sufficient for affected-version validation, but not for claims of active exploitation.
Mitigation direction
- Upgrade TensorFlow to 2.6.0, 2.5.1, 2.4.3, 2.3.4, or later supported versions.
- Apply vendor guidance if pinned dependencies prevent immediate TensorFlow upgrades.
- Restrict untrusted model execution or tensor inputs reaching affected TensorFlow Lite paths.
- Prioritize exposed model-serving systems where crashes affect customer-facing availability.
Validation and detection
- Inventory TensorFlow versions across ML services, containers, build manifests, and notebooks.
- Review models and code for `tf.raw_ops.UnravelIndex` usage.
- Confirm deployed versions are not in the affected ranges listed by TensorFlow.
- Check service reliability logs for crashes in TensorFlow unravel index handling.
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-2021-37668 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-2wmv-37vq-52g5CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233CVE 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.