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CVE Record

CVE-2021-29583: Heap buffer overflow and undefined behavior in `FusedBatchNorm`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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 is a low-severity TensorFlow memory-safety flaw in the FusedBatchNorm operation. A malformed use of the operation can make TensorFlow read past tensor buffers or dereference null pointers, mainly affecting availability rather than data theft or tampering.

Executive priority

Treat this as routine patching unless untrusted users can run TensorFlow workloads in your environment. The expected business impact is service instability, not confirmed data compromise, based on the supplied evidence.

Technical view

Affected TensorFlow versions do not validate that scale, offset, mean, and variance tensor sizes match the channel count of x in tf.raw_ops.FusedBatchNorm. Mismatches can cause heap out-of-bounds reads; empty tensors can trigger undefined behavior through null dereferences.

Likely exposure

Exposure is most relevant where affected TensorFlow versions run local or user-submitted ML workloads, models, or tensor operations that can reach FusedBatchNorm. The supplied sources do not identify affected hosted services, downstream products, or internet-exposed attack paths.

Exploitation context

The CVSS vector is local, high complexity, low privileges, no user interaction, and limited to low availability impact. The source bundle says this CVE is not in KEV, and no supplied source states active exploitation.

Researcher notes

The root issue is missing tensor shape validation in fused_batch_norm_op.cc. The published fix validates tensor element counts against the x channel dimension, preventing both out-of-bounds reads and empty-tensor undefined behavior noted in the advisory.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
  • Use patched cherrypick releases 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Inventory environments using affected TensorFlow versions before accepting untrusted ML workloads.
  • Check TensorFlow advisory guidance for unsupported or older deployments.

Validation and detection

  • Confirm installed TensorFlow versions across development, training, and inference environments.
  • Review dependency lockfiles and container images for affected TensorFlow ranges.
  • Identify workloads that execute tf.raw_ops.FusedBatchNorm or imported models using fused batch normalization.
  • Verify patched versions are deployed after remediation.
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-476: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29583 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-2021-29583Attack 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-476

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

CWE-476 · source CWE mapping

NULL Pointer Dereference

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

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