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CVE Record

CVE-2021-29599: Division by zero in TFLite's implementation of `Split`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` 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

CVE-2021-29599 is a low-severity TensorFlow Lite flaw where a specially crafted model can crash processing by triggering division by zero in the Split operator. The documented impact is limited availability loss, not data theft or tampering.

Executive priority

Treat this as routine security maintenance unless the business accepts untrusted ML models at scale. Prioritize customer-facing or automated model ingestion paths, but it does not warrant emergency response based on the supplied evidence.

Technical view

TensorFlow Lite's Split operator did not guard against num_splits being zero. A crafted model could reach division by zero in split.cc, causing a crash. The CVSS vector is local, high complexity, low privileges, no user interaction, and low availability impact only.

Likely exposure

Exposure is most likely in products, services, or pipelines using affected TensorFlow versions that load TensorFlow Lite models from users, partners, marketplaces, or other low-trust sources.

Exploitation context

The bundle does not show CISA KEV listing or active exploitation. Practical abuse requires an attacker to get a crafted model loaded by an affected TensorFlow Lite runtime, causing denial of service rather than confidentiality or integrity compromise.

Researcher notes

Focus validation on version evidence and trust boundaries around model ingestion. The reported weakness is CWE-369, with CVSS 3.1 score 2.5 and vector CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or a fixed supported backport release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Restrict loading of untrusted TensorFlow Lite models until patched.
  • Review vendor advisory guidance for unsupported or forked TensorFlow deployments.

Validation and detection

  • Inventory applications and containers using TensorFlow or TensorFlow Lite.
  • Check versions against the affected ranges in the source bundle.
  • Identify workflows that accept externally supplied TensorFlow Lite models.
  • Confirm upgraded builds include the fixed TensorFlow release.
  • Regression test model-loading paths after upgrading.
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

Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.

cwe · low confidence lookup

CWE-369: Exact CWE lookup

Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.

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cve · low confidence lookup

CVE-2021-29599 mapping review

Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.

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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-2021-29599Attack 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.

Open CWE intelligenceSame-CWE CVEs