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CVE Record

CVE-2021-29567: Lack of validation in `SparseDenseCwiseMul`

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

This TensorFlow flaw can let a low-privileged local attacker crash a process by providing invalid inputs to a sparse/dense multiplication operation. The business impact is mainly availability: affected ML jobs or services could terminate unexpectedly. Public sources do not show active exploitation.

Executive priority

Treat as a low-priority availability issue unless TensorFlow is exposed through shared compute, hosted notebooks, or user-submitted ML workloads. Patch during normal maintenance for isolated systems.

Technical view

`tf.raw_ops.SparseDenseCwiseMul` validated input rank but not dimension relationships. Malformed shapes could trigger internal `CHECK` failures or access/write outside heap-allocated tensor buffers. TensorFlow fixed it in 2.5.0 and planned backports for supported 2.4, 2.3, 2.2, and 2.1 releases.

Likely exposure

Exposure is likely limited to systems running affected TensorFlow versions where local users, tenants, notebooks, pipelines, or submitted workloads can influence tensors passed to this raw operation.

Exploitation context

No KEV listing or cited source indicates active exploitation. CVSS describes local access, low privileges, high complexity, no user interaction, and low availability impact, with no stated confidentiality or integrity impact.

Researcher notes

The advisory identifies CWE-617 and dimension validation gaps in `SparseDenseCwiseMul`. Evidence supports denial of service and out-of-bounds heap buffer access/write, but not active exploitation or remote unauthenticated exposure.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 where possible.
  • Use fixed supported releases: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Check TensorFlow advisory guidance for branch-specific upgrade direction.
  • Prioritize shared ML platforms accepting user-submitted workloads.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, images, and ML pipelines.
  • Flag versions below 2.1.4 or affected 2.2.x, 2.3.x, and 2.4.x ranges.
  • Identify services where untrusted users can run TensorFlow operations.
  • Confirm upgraded deployments use a fixed TensorFlow release.
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

CWE-617: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29567 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-2021-29567Attack 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< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-617

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

CWE-617 · source CWE mapping

Reachable Assertion

Reachable Assertion represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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