Security readout for executives and security teams
Plain-English summary
Affected TensorFlow versions can mishandle malformed input to MatrixSetDiagV* operations, causing undefined behavior. Business impact is mainly reliability and integrity risk for ML workloads where a local or already authorized user can supply tensors or model logic.
Executive priority
Prioritize remediation for shared ML platforms, production inference/training systems, and environments accepting user-submitted notebooks or models. Lower priority for isolated systems without untrusted local users or submitted workloads.
Technical view
TensorFlow incompletely validates the k tensor in tf.raw_ops.MatrixSetDiagV* operations. An empty tensor can reach code that reads its first element, binding a reference to a null pointer. The source assigns CVSS 7.1 with local attack vector, low complexity, low privileges, no user interaction, and high integrity and availability impact.
Likely exposure
Exposure is most likely in applications, notebooks, pipelines, or services running affected TensorFlow versions and allowing local users or submitted ML code/data to invoke raw matrix diagonal operations.
Exploitation context
The supplied sources do not show active exploitation, and KEV is false. The CVSS vector indicates local, low-privilege exploitation with no user interaction, so multi-user ML environments and shared compute are higher concern.
Researcher notes
Evidence is limited to the TensorFlow advisory, CVE record, and fixing commit. Do not infer remote exploitability, broader TensorFlow operations, or public exploitation from the supplied material.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a patched supported branch release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where those branches are required.
- Review vendor advisory and commit before accepting alternative backports.
- Limit untrusted ML code or tensor inputs until affected runtimes are upgraded.
Validation and detection
- Inventory TensorFlow versions in applications, containers, notebooks, and dependency lockfiles.
- Flag versions >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, and <2.3.4.
- Confirm workloads exposing tf.raw_ops.MatrixSetDiagV* are not reachable by untrusted users.
- Verify the deployed package contains commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b or a fixed release.
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
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Open ATT&CK lookupCVE-2021-37658 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.1 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/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:H/A:H1.85.2Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
7.1HighVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6p5r-g9mq-ggh2CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1bCVE 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.
Access of Uninitialized Pointer
Access of Uninitialized Pointer represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.