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CVE Record

CVE-2021-29554: Division by 0 in `DenseCountSparseOutput`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

A crafted local TensorFlow use case can crash a process by triggering a divide-by-zero error in DenseCountSparseOutput. This is a low-severity availability issue, not a data theft or privilege escalation vulnerability, based on the supplied sources.

Executive priority

Treat this as routine patch hygiene unless affected TensorFlow is exposed in shared or user-programmable ML environments. Prioritize upgrades during normal maintenance, with faster action for multi-tenant notebooks or model platforms.

Technical view

CVE-2021-29554 is CWE-369 in TensorFlow count_ops.cc. DenseCountSparseOutput derives a divisor from user-controlled values and can divide by zero, causing an FPE runtime error and denial of service. Affected versions are listed as TensorFlow <2.3.3 and >=2.4.0, <2.4.2.

Likely exposure

Exposure is most likely in ML workloads running affected TensorFlow versions where a local or already-authorized user can influence tensors reaching tf.raw_ops.DenseCountSparseOutput. General TensorFlow presence alone does not prove reachable exposure.

Exploitation context

The CVSS vector requires local access and low privileges, with high attack complexity and low availability impact. The source bundle does not identify active exploitation, and CISA KEV status is false.

Researcher notes

The key evidence is TensorFlow’s advisory and fixing commit. The issue is a divide-by-zero denial of service with no cited confidentiality or integrity impact. Evidence is incomplete for real-world exploitation or broader affected product ecosystems.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a patched 2.4.2 or 2.3.3 release.
  • Inventory applications, notebooks, containers, and model-serving images for affected TensorFlow versions.
  • Restrict untrusted users from executing arbitrary TensorFlow operations in shared ML environments.
  • Review the TensorFlow advisory and commit for version-specific remediation details.

Validation and detection

  • Check dependency manifests and runtime environments for TensorFlow <2.3.3 or >=2.4.0, <2.4.2.
  • Identify code paths using DenseCountSparseOutput or tf.raw_ops.DenseCountSparseOutput.
  • Confirm untrusted input cannot directly reach the affected operation.
  • Run ML regression tests after upgrading TensorFlow.
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

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cwe · low confidence lookup

CWE-369: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29554 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
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
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-2021-29554Attack 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< 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-369

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