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CVE Record

CVE-2021-29577: Heap buffer overflow in `AvgPool3DGrad`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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-2021-29577 is a low-severity TensorFlow memory safety flaw. A user who can run the affected AvgPool3DGrad operation with crafted tensor shapes may cause a heap buffer overflow and limited availability impact. The sources do not indicate active exploitation.

Executive priority

Treat this as a routine patching item unless your organization exposes TensorFlow execution to untrusted or semi-trusted users. It has low severity and no cited active exploitation, but affected ML platforms should still be upgraded to supported fixed releases.

Technical view

TensorFlow tf.raw_ops.AvgPool3DGrad assumes orig_input_shape and grad have matching first and last dimensions without validating that condition. That can trigger a heap buffer overflow. The CVSS vector is local, high attack complexity, low privileges required, no confidentiality or integrity impact, and low availability impact.

Likely exposure

Exposure is most likely in TensorFlow installations using affected versions: before 2.1.4, 2.2.0 before 2.2.3, 2.3.0 before 2.3.3, and 2.4.0 before 2.4.2. Risk is higher where users can supply TensorFlow graphs, models, or tensor inputs.

Exploitation context

The CVE is not listed as KEV, and the provided sources do not report active exploitation. The CVSS vector indicates local access with low privileges and high complexity. Practical abuse appears limited to environments where an attacker can reach TensorFlow operation execution.

Researcher notes

Primary evidence is the TensorFlow advisory and fix commit. The flaw is a missing shape-consistency check in AvgPool3DGrad, not a broad TensorFlow compromise. The source bundle does not provide proof-of-concept status, exploit telemetry, or downstream vendor impact.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
  • Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Inventory applications, notebooks, containers, and ML services for affected TensorFlow versions.
  • Restrict untrusted model, graph, or tensor execution until fixed.
  • Check TensorFlow advisory guidance for branch-specific remediation.

Validation and detection

  • Check dependency manifests and runtime environments for TensorFlow versions.
  • Confirm deployed TensorFlow versions are outside the affected ranges.
  • Identify services allowing users to submit models, graphs, or tensor inputs.
  • Review security tests around TensorFlow input-shape validation boundaries.
  • Confirm no unsupported TensorFlow branch remains in production.
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-119: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29577 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-2021-29577Attack 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-119

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

CWE-119 · source CWE mapping

Improper Restriction of Operations within the Bounds of a Memory Buffer

Improper Restriction of Operations within the Bounds of a Memory Buffer represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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