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CVE Record

CVE-2021-29544: CHECK-fail in `QuantizeAndDequantizeV4Grad`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29544 is a low-severity TensorFlow denial-of-service issue. A user who can run TensorFlow operations on an affected system may crash a process by triggering an internal check failure. The sources do not show data theft, code execution, or active exploitation.

Executive priority

Treat this as routine patching unless affected TensorFlow environments are multi-tenant or run untrusted jobs. Business impact is availability disruption to ML workloads, not known data compromise.

Technical view

TensorFlow failed to validate tensor rank in tf.raw_ops.QuantizeAndDequantizeV4Grad. Non-1D input_* tensors could reach QuantizeAndDequantizePerChannelGradientImpl, where vec<T> requires rank 1 and triggers a CHECK failure. Affected versions are TensorFlow >=2.4.0 and <2.4.2.

Likely exposure

Exposure is most likely in systems running TensorFlow 2.4.0 or 2.4.1 where low-privileged users can execute local TensorFlow workloads, jobs, notebooks, or submitted graphs.

Exploitation context

The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and low availability impact. KEV is false, and the provided sources do not report exploitation in the wild.

Researcher notes

The root issue is missing rank validation before per-channel gradient handling. Public sources describe a CHECK-fail denial of service only. No exploit code, active exploitation, confidentiality impact, or integrity impact is cited in the provided bundle.

Mitigation direction

  • Upgrade affected TensorFlow deployments to 2.4.2 or 2.5.0 where the fix is included.
  • Prioritize shared ML platforms that run user-submitted TensorFlow jobs or notebooks.
  • Restrict untrusted TensorFlow workload execution on affected versions until upgraded.
  • Check TensorFlow vendor guidance if upgrade constraints prevent immediate remediation.

Validation and detection

  • Inventory TensorFlow package versions across hosts, containers, notebooks, and model-serving images.
  • Flag TensorFlow versions >=2.4.0 and <2.4.2 as affected.
  • Identify services allowing low-privileged users to run TensorFlow operations.
  • After upgrade, run existing ML and quantization workflow regression tests.
Prepared
Confidence
high
Sources
6

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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cwe · low confidence lookup

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cve · low confidence lookup

CVE-2021-29544 mapping review

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Vulnerability profileCVE Program record
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

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
5Source 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
2.5CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

2.5Low
CVSS 3.1 vector shape for CVE-2021-29544Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

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

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>= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-754

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

CWE-754 · source CWE mapping

Improper Check for Unusual or Exceptional Conditions

Improper Check for Unusual or Exceptional Conditions represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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