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CVE Record

CVE-2021-29602: Division by zero in TFLite's implementation of `DepthwiseConv`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 0. 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 crash risk in TensorFlow Lite. A specially crafted model can trigger a division by zero in the DepthwiseConv operator, causing limited availability impact. It does not indicate data theft or integrity compromise in the cited sources.

Executive priority

Treat as routine maintenance unless the business accepts third-party TFLite models. Prioritize affected products that process external models because the realistic impact is service disruption, not compromise of sensitive data.

Technical view

CVE-2021-29602 is CWE-369 in TFLite DepthwiseConv. A crafted model can set the input fourth dimension to zero, reaching a division by zero. CVSS is 2.5, local, high complexity, low privileges, no confidentiality or integrity impact, and low availability impact.

Likely exposure

Exposure is most likely where affected TensorFlow/TFLite versions load untrusted or user-supplied models. Products using only trusted, bundled models have lower practical risk, based on the cited attack precondition.

Exploitation context

The source bundle does not show active exploitation, and KEV is false. Exploitation requires a crafted model and local attack conditions reflected by the CVSS vector, so this is mainly a denial-of-service concern.

Researcher notes

The affected ranges are TensorFlow before 2.1.4, 2.2.0 to before 2.2.3, 2.3.0 to before 2.3.3, and 2.4.0 to before 2.4.2. Evidence is limited to the advisory, CVE data, and fixing commit.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or fixed backport releases listed by the advisory.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 for supported older branches.
  • Restrict loading of untrusted TensorFlow Lite models where feasible.
  • Check TensorFlow vendor guidance for any environment-specific remediation details.

Validation and detection

  • Inventory TensorFlow and TensorFlow Lite versions in products, containers, and mobile builds.
  • Confirm whether workflows load models from users, partners, or untrusted local paths.
  • Review SBOMs and lockfiles for the affected version ranges.
  • Verify upgraded builds include the vendor fix or fixed release line.
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-2021-29602 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-2021-29602Attack 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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