Security readout for executives and security teams
Plain-English summary
CVE-2021-29573 is a low-severity TensorFlow availability bug. A crafted or unusual tensor shape can cause a divide-by-zero condition in a TensorFlow pooling-gradient operation, potentially crashing affected local workloads. It does not expose data or alter results according to the supplied CVSS vector.
Executive priority
Treat as routine patch management unless TensorFlow runs shared or user-supplied ML workloads. Business risk is mainly limited service disruption, not data theft or privilege compromise based on supplied evidence.
Technical view
TensorFlow tf.raw_ops.MaxPoolGradWithArgmax failed to validate that the tensor batch dimension was non-zero before division. Affected versions are before 2.1.4, 2.2.0 before 2.2.3, 2.3.0 before 2.3.3, and 2.4.0 before 2.4.2. The fix was included in 2.5.0 and supported patch releases.
Likely exposure
Exposure is most likely where affected TensorFlow versions run local, authenticated, or controlled ML workloads that can reach MaxPoolGradWithArgmax. Internet-facing exposure is not established in the provided sources.
Exploitation context
The source bundle does not show active exploitation, and KEV status is false. CVSS indicates local access, high attack complexity, low privileges, no user interaction, and low availability impact only.
Researcher notes
The key issue is missing validation around a zero batch dimension before division in maxpooling_op.cc. Analysis should stay focused on availability impact and version confirmation; the provided sources do not support broader affected products or active exploitation claims.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a listed patched supported release.
- Patch 2.4.x to 2.4.2, 2.3.x to 2.3.3, 2.2.x to 2.2.3, or 2.1.x to 2.1.4.
- Check TensorFlow advisory guidance before using any unsupported branch.
- Restrict execution of untrusted models or tensor inputs in shared ML environments.
Validation and detection
- Inventory deployed TensorFlow package versions across applications, notebooks, images, and training workers.
- Confirm affected ranges are absent from lockfiles, containers, and runtime environments.
- Review whether workloads invoke MaxPoolGradWithArgmax directly or through model layers.
- Check reliability logs for unexplained TensorFlow worker crashes, without treating them as exploitation proof.
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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ATT&CK lookup starting points
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CWE-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29573 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 2.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
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:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
2.5LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqwCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483dCVE 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.