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CVE Record

CVE-2021-37673: `CHECK`-fail in `MapStage` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

MediumCVSS 5.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysismoderate

Security readout for executives and security teams

Plain-English summary

This TensorFlow flaw can let a local authenticated user crash a process by causing an internal assertion failure in MapStage. It affects availability, not data confidentiality or integrity. Business urgency is mainly for shared ML platforms, notebooks, or job runners where untrusted users can execute TensorFlow workloads.

Executive priority

Treat this as a moderate availability risk. Patch during the normal security maintenance cycle, faster for shared ML infrastructure where one user crash can disrupt other workloads.

Technical view

CVE-2021-37673 is a CWE-20 input validation issue in tf.raw_ops.MapStage. A key input is not validated as a non-empty tensor, causing a CHECK-fail denial of service. The CVSS 3.1 score is 5.5 with local access and low privileges required.

Likely exposure

Exposure is most likely in environments running affected TensorFlow versions with multi-user ML execution, notebooks, plugins, or job submission. The source does not indicate direct remote exploitation through TensorFlow alone.

Exploitation context

The CVE record marks KEV as false, and the provided sources do not state active exploitation. Exploitation requires local access with low privileges and no user interaction, according to the CVSS vector.

Researcher notes

Affected ranges are TensorFlow >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, and <2.3.4. The vendor advisory names MapStage and the fixed commit. Sources do not provide exploit-in-the-wild evidence or additional mitigations beyond patching.

Mitigation direction

  • Upgrade TensorFlow to 2.6.0 or later where the fix is included.
  • For supported older branches, upgrade to 2.5.1, 2.4.3, or 2.3.4.
  • Prioritize shared ML hosts, notebook services, and job runners first.
  • Check TensorFlow vendor guidance for unsupported or pinned deployments.

Validation and detection

  • Inventory dependency manifests, containers, and runtime environments for affected TensorFlow versions.
  • Confirm deployed TensorFlow resolves to a fixed version or includes commit d7de67733925de196ec8863a33445b73f9562d1d.
  • Review whether untrusted users can run TensorFlow graphs or raw ops.
  • Monitor ML worker crashes consistent with assertion failures or denial of service.
Prepared
Confidence
high
Sources
4

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

Conservative CVE-to-ATT&CK context

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ATT&CK lookup starting points

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cwe · low confidence lookup

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cve · low confidence lookup

CVE-2021-37673 mapping review

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Vulnerability profileCVE Program record
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

Official CVE source material

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.

1CVSS vectors
0Timeline events
0ADP providers
3Source links

CVSS vector scores

1 official score

We 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.

ScoreVersionSeverityVectorExploitImpactSource
5.5CVSS 3.1MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H1.83.6Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

5.5Medium
CVSS 3.1 vector shape for CVE-2021-37673Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
tensorflowtensorflow>= 2.5.0, < 2.5.1, >= 2.4.0, < 2.4.3, < 2.3.4Listed
Weakness

CWE details

CWE-20

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.