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CVE Record

CVE-2021-29596: Division by zero in TFLite's implementation of `EmbeddingLookup`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input is 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

CVE-2021-29596 is a low-severity TensorFlow Lite flaw that can crash affected processing when a crafted model triggers division by zero in EmbeddingLookup. Business impact is mainly limited availability loss in systems that accept or process untrusted machine-learning models.

Executive priority

Treat this as routine patching unless your business processes untrusted machine-learning models. Prioritize affected model-processing services that are internet-facing, customer-facing, or part of automated ingestion pipelines.

Technical view

TensorFlow Lite's EmbeddingLookup operator can divide by zero when a model sets the first dimension of the value input to 0. The source CVSS is 2.5, requiring local access, high complexity, and low privileges, with no confidentiality or integrity impact and low availability impact.

Likely exposure

Exposure is most likely in applications using affected TensorFlow versions with TFLite model ingestion, especially where models are supplied by users, partners, or automated pipelines. Standard deployments using only trusted, validated models have lower practical risk.

Exploitation context

The source bundle does not show active exploitation, and CISA KEV status is false. Exploitation requires a crafted model and yields limited denial-of-service impact rather than data theft or code execution, based on the supplied CVSS and advisory details.

Researcher notes

Focus validation on TFLite EmbeddingLookup usage and model ingestion boundaries. The public advisory identifies the fault condition and patch target but the provided evidence does not support broader impact, remote exploitation claims, or active exploitation.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
  • Restrict acceptance of untrusted TFLite models until affected runtimes are patched.
  • Review vendor advisory and commit for exact version guidance.

Validation and detection

  • Inventory TensorFlow and TFLite runtime versions across applications and containers.
  • Check dependency lockfiles for affected TensorFlow version ranges.
  • Identify services that load externally supplied or partner supplied TFLite models.
  • Confirm patched versions are deployed in build, test, and production environments.
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-2021-29596 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-2021-29596Attack 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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