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CVE Record

CVE-2021-29547: Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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

CVE-2021-29547 is a low-severity TensorFlow denial-of-service issue. A local attacker with some access could make an affected TensorFlow process crash by causing a specific quantized batch-normalization operation to receive empty inputs. The public sources do not show active exploitation.

Executive priority

Treat this as routine patching unless TensorFlow is exposed to untrusted model or input execution. It can crash affected processes but does not indicate data theft, privilege escalation, or widespread exploitation in the provided evidence.

Technical view

The bug is a heap out-of-bounds read in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization. The implementation assumes inputs are non-empty and reads element 0 from empty flat buffers, causing a segfault. Affected TensorFlow branches include versions before 2.1.4, 2.2.3, 2.3.3, and 2.4.2.

Likely exposure

Exposure is most likely where affected TensorFlow versions execute untrusted or user-controlled models, graphs, or tensor inputs. Internal ML training or inference jobs using only trusted inputs have lower practical risk, but still should patch during normal dependency maintenance.

Exploitation context

The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and limited availability impact. KEV status is false, and the provided sources do not report active exploitation or public weaponization.

Researcher notes

The root cause is an unchecked empty-buffer assumption in quantized_batch_norm_op.cc. The referenced TensorFlow commit is the authoritative fix source. Public evidence supports denial of service through segmentation fault, not confidentiality or integrity compromise.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or a patched supported branch release.
  • For 2.4.x, update to TensorFlow 2.4.2 or later.
  • For 2.3.x, update to TensorFlow 2.3.3 or later.
  • For 2.2.x, update to TensorFlow 2.2.3 or later.
  • For 2.1.x, update to TensorFlow 2.1.4 or later.
  • Until upgraded, avoid running untrusted TensorFlow graphs or tensor inputs.

Validation and detection

  • Inventory deployed TensorFlow package versions across training and inference environments.
  • Confirm no affected version ranges remain in application dependencies or container images.
  • Check whether workloads expose TensorFlow execution to untrusted users or uploaded models.
  • Review use of tf.raw_ops.QuantizedBatchNormWithGlobalNormalization in code or generated graphs.
  • Track vendor advisory status for any branch-specific backport guidance.
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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cwe · low confidence lookup

CWE-125: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29547 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-2021-29547Attack 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-125

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

CWE-125 · source CWE mapping

Out-of-bounds Read

Out-of-bounds Read represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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