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CVE Record

CVE-2021-29597: Division by zero in TFLite's implementation of `SpaceToBatchNd`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` 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-2021-29597 is a low-severity TensorFlow Lite flaw that can make affected software crash when processing a specially crafted model. The cited impact is limited availability loss, not data theft or tampering. It matters most where applications load models from users, partners, or other untrusted sources.

Executive priority

Treat as routine patching unless your products process untrusted TFLite models. For model-hosting, mobile ML, or edge inference workflows, prioritize validation and upgrade during the next maintenance cycle.

Technical view

TensorFlow Lite's SpaceToBatchNd operator can divide by zero when a crafted model sets a block input dimension to 0. The CVSS vector is local, high complexity, low privileges, no user interaction, unchanged scope, and low availability impact only. Supported affected TensorFlow branches received fixed releases.

Likely exposure

Exposure is likely limited to systems using affected TensorFlow versions with TensorFlow Lite model execution, especially where TFLite models can come from untrusted or semi-trusted sources.

Exploitation context

The bundle does not cite active exploitation, and KEV status is false. The CVSS vector indicates local access and high attack complexity, with no cited confidentiality or integrity impact.

Researcher notes

Evidence supports a CWE-369 division-by-zero in TFLite SpaceToBatchNd. The fix is tied to TensorFlow 2.5.0 and backported supported releases. No public exploit activity is provided in the source bundle.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or a patched supported release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 for older supported branches.
  • Restrict acceptance and execution of untrusted TFLite models.
  • Check TensorFlow advisory guidance before applying branch-specific fixes.

Validation and detection

  • Inventory TensorFlow and TensorFlow Lite versions in applications, containers, and build artifacts.
  • Confirm no affected version ranges remain in production or release pipelines.
  • Identify services that load third-party, user-supplied, or partner-supplied models.
  • Verify deployed runtimes use fixed TensorFlow releases, not only updated source repositories.
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-2021-29597 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-2021-29597Attack 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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