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CVE Record

CVE-2021-29550: Division by 0 in `FractionalAvgPool`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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 denial-of-service issue in TensorFlow. A user who can run or submit specific TensorFlow operations can make FractionalAvgPool crash by causing a division by zero. It does not expose data or alter results, but it can interrupt workloads that process untrusted input or models.

Executive priority

Treat as routine patching unless TensorFlow workloads process untrusted user models or shared tenant inputs. Business impact is service interruption, not data loss or compromise, based on the provided sources.

Technical view

In affected TensorFlow versions, tf.raw_ops.FractionalAvgPool derives output_size from user-controlled input shape and pooling_ratio. When input_size is smaller than pooling_ratio, output_size can become zero. A later GeneratePoolingSequence modulo/division operation then crashes at runtime because debug-only checks do not protect release builds.

Likely exposure

Exposure is limited to systems running affected TensorFlow versions where a user can execute TensorFlow code, submit model graphs, or influence tensors and pooling ratios reaching FractionalAvgPool.

Exploitation context

The CVSS vector is local, high complexity, low privileges required, and availability-only impact. The source bundle marks KEV as false, and it provides no evidence of active exploitation.

Researcher notes

The issue is CWE-369 division by zero. The vulnerable path is caused by user-controlled shape and pooling_ratio producing output_size zero, then reaching GeneratePoolingSequence. The fix is linked in TensorFlow commit 548b5eaf23685d86f722233d8fbc21d0a4aecb96.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or later where feasible.
  • For supported older branches, use 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Restrict untrusted users from submitting arbitrary TensorFlow operations or model graphs.
  • Check current TensorFlow vendor guidance before relying on unsupported branches.

Validation and detection

  • Inventory deployed TensorFlow versions in applications, notebooks, workers, and ML services.
  • Confirm no deployed environment remains in the affected version ranges.
  • Review whether untrusted inputs can reach tf.raw_ops.FractionalAvgPool.
  • Verify dependency scanners or SBOMs report the patched TensorFlow versions.
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-369: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29550 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-2021-29550Attack 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-369

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

CWE-369 · source CWE mapping

Divide By Zero

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

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