Security readout for executives and security teams
Plain-English summary
This vulnerability can let a low-privileged local user crash TensorFlow by triggering a fault in MaxPoolGrad. The practical business impact is availability loss for affected ML workloads, especially shared compute environments where users can run TensorFlow operations.
Executive priority
Treat this as a moderate availability risk. It is not reported as actively exploited and does not indicate data compromise, but it can disrupt ML workloads where untrusted or semi-trusted users can run TensorFlow code.
Technical view
TensorFlow missed validation for orig_input and orig_output tensors in tf.raw_ops.MaxPoolGrad. Malformed inputs can cause a segmentation fault. This is CWE-20 with CVSS 5.5: local attack vector, low privileges required, no user interaction, and high availability impact only.
Likely exposure
Exposure is most likely in TensorFlow 2.5.0, 2.4.x before 2.4.3, and versions before 2.3.4, especially shared notebooks, batch ML systems, or services that allow users to execute TensorFlow graphs or operations.
Exploitation context
The source bundle does not show CISA KEV listing or cited active exploitation. The advisory describes denial of service, not data theft or code execution. Exploitation requires the ability to run or supply TensorFlow operations in an affected environment.
Researcher notes
This issue is an incomplete fix for CVE-2021-29579. The vulnerable area is MaxPoolGrad input validation in TensorFlow kernel code. The authoritative remediation reference is commit 136b51f10903e044308cf77117c0ed9871350475 and the patched release guidance in TFSA-2021-068/GHSA-7ghq-fvr3-pj2x.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or the patched 2.5.1, 2.4.3, or 2.3.4 releases.
- Apply vendor guidance from the TensorFlow advisory if pinned to affected branches.
- Restrict untrusted users from executing arbitrary TensorFlow operations on shared ML infrastructure.
- Prioritize patching shared or multi-tenant ML environments first.
Validation and detection
- Inventory TensorFlow package versions across notebooks, training images, inference images, and batch workers.
- Confirm affected deployments are no longer on the vulnerable version ranges listed by TensorFlow.
- Review ML platforms for untrusted users who can execute TensorFlow ops or submit graphs.
- Check dependency lockfiles and container base images for pinned TensorFlow versions.
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-20: 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-2021-37674 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-7ghq-fvr3-pj2xCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475CVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.mdCVE 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.
Improper Input Validation
Improper Input Validation represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.