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CVE Record

CVE-2021-33654: When performing the initialization operation of the Split operator, if a dimension in the input shape is 0,...

When performing the initialization operation of the Split operator, if a dimension in the input shape is 0, it will cause a division by 0 exception.

UnknownCVSS not scoredNot KEV-listedUpdated
Glexia's TakeAutomated analysisunknown

Security readout for executives and security teams

Plain-English summary

This issue can make affected MindSpore deployments fail when the Split operator receives an input shape containing a zero dimension. The reported effect is a division-by-zero exception, so the main business concern is service disruption rather than confirmed data theft or code execution.

Executive priority

Treat this as a targeted availability risk for affected AI systems. It does not have enough public evidence for emergency response, but teams running the listed MindSpore versions should validate exposure and plan remediation promptly.

Technical view

CVE-2021-33654 is a CWE-369 division-by-zero flaw in MindSpore Split operator initialization. The source states that a zero dimension in the input shape triggers the exception. The affected range is openEuler:mindspore versions from 0.7.0-beta before 1.3.0.

Likely exposure

Exposure is limited to environments running openEuler:mindspore versions >= 0.7.0-beta and < 1.3.0. Risk is higher where model inputs, tensor shapes, or workloads can be influenced by untrusted users or external pipelines.

Exploitation context

The provided sources do not report active exploitation, and this CVE is not listed as KEV. A plausible impact is denial of service through application failure when malformed or unexpected tensor shapes reach the affected Split operator path.

Researcher notes

The public record is sparse: no CVSS, no named exploit, and no detailed attack prerequisites. The reliable facts are the affected version range, Split operator initialization trigger, zero-dimension condition, and CWE-369 classification.

Mitigation direction

  • Inventory MindSpore versions across AI workloads and build images.
  • Check the MindSpore advisory for the supported fix or upgrade path.
  • Prioritize moving systems outside the listed affected version range.
  • Reject zero-dimension tensor shapes where preprocessing is under your control.
  • Restrict untrusted model or input-shape submission to affected services.

Validation and detection

  • Confirm whether any deployment runs openEuler:mindspore >= 0.7.0-beta and < 1.3.0.
  • Review inference and training paths that use the Split operator.
  • Check logs for division-by-zero exceptions in MindSpore workloads.
  • Verify input validation handles unexpected zero-dimension shapes.
  • Re-test affected workflows after applying vendor guidance.
Prepared
Confidence
medium
Sources
3

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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Open ATT&CK lookup
cve · low confidence lookup

CVE-2021-33654 mapping review

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Vulnerability profileCVE Program record
Severity
Unknown
CVSS
Not scored
Known Exploited
No
Published
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.

0CVSS vectors
0Timeline events
0ADP providers
2Source links

CVSS and timeline data

No CVSS vectors or timeline events were available in the normalized CVE source material.

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
n/aopenEuler:mindspore>= 0.7.0-beta, < 1.3.0Listed
Weakness

CWE details

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.