Security readout for executives and security teams
Plain-English summary
This TensorFlow flaw can crash or disrupt workloads by triggering a division-by-zero condition in a raw tensor operation. It is not a data theft issue, but it can affect availability for ML systems using vulnerable TensorFlow versions.
Executive priority
Treat as a moderate availability risk. Patch during the next standard security update cycle, sooner for shared ML infrastructure or services where users can submit TensorFlow workloads.
Technical view
CVE-2021-37642 is a CWE-369 divide-by-zero vulnerability in tf.raw_ops.ResourceScatterDiv. TensorFlow reused common binary-operation handling but did not separately handle division by zero. The CVSS vector is local, low complexity, low privilege, no user interaction, with high availability impact.
Likely exposure
Systems running TensorFlow >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, or <2.3.4 are affected. Exposure is most relevant where local users, jobs, notebooks, or ML pipelines can execute TensorFlow operations.
Exploitation context
The bundle does not indicate active exploitation, and KEV is false. The CVSS vector describes local exploitation requiring low privileges and no user interaction, with availability impact only.
Researcher notes
Focus validation on dependency versions and execution boundaries. The evidence supports an availability-only divide-by-zero issue in ResourceScatterDiv; no source in the bundle supports remote exploitation, confidentiality impact, or active exploitation.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a fixed supported branch release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where those branches apply.
- Check the TensorFlow advisory for any branch-specific guidance.
- Prioritize shared ML environments where untrusted users can run TensorFlow workloads.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Compare versions against the affected ranges listed in the advisory.
- Confirm dependency lockfiles and deployed images contain fixed TensorFlow versions.
- Review shared ML platforms for users able to run TensorFlow jobs locally.
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
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2021-37642 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-ch4f-829c-v5pwCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76CVE 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.