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CVE Record

CVE-2021-29517: Division by zero in `Conv3D`

TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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 user crash software that processes a malicious Conv3D tensor shape. The documented impact is limited availability loss, not data theft or code execution.

Executive priority

Treat as routine patching unless TensorFlow workloads are multi-tenant or accept untrusted ML inputs. The main business risk is service interruption, not breach of confidentiality or integrity.

Technical view

CVE-2021-29517 is a CWE-369 division-by-zero issue in TensorFlow Conv3D. User-controlled filter shape input can reach a modulo operation with a zero divisor. Invalid tensor shapes can also trigger an Eigen assertion and crash.

Likely exposure

Exposure is most likely in systems running affected TensorFlow versions below patched maintenance releases and allowing untrusted local users, jobs, models, or tensor inputs to reach Conv3D processing.

Exploitation context

The provided sources do not show active exploitation, and KEV is false. CVSS rates exploitation as local, high complexity, low privilege, no user interaction, with low availability impact only.

Researcher notes

The advisory identifies division by zero in Conv3D and a related assertion crash path. Evidence supports denial of service only. No exploit code, public exploitation, or broader product impact is established in the provided bundle.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or patched branch releases named by TensorFlow.
  • For older branches, apply vendor-supported fixes: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Limit untrusted users or workloads from supplying arbitrary tensor shapes to Conv3D paths.
  • Check TensorFlow guidance before relying on unsupported or end-of-life versions.

Validation and detection

  • Inventory deployed TensorFlow versions across applications, notebooks, containers, and training workers.
  • Identify services or jobs that accept user-controlled models, tensors, or preprocessing inputs.
  • Confirm affected versions are no longer present after upgrade or rebuild.
  • Run non-offensive regression tests for Conv3D input validation and crash resistance.
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

These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.

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-2021-29517 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-2021-29517Attack 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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