Security readout for executives and security teams
Plain-English summary
A crafted TensorFlow Lite model can trigger a division-by-zero crash in the SpaceToDepth operator. The known impact is limited availability loss, not data theft or code execution, and the CVSS score is low. Business urgency rises only where untrusted TFLite models are accepted or processed.
Executive priority
Treat as routine patching unless the organization processes untrusted TFLite models. Prioritize affected model-ingestion services, but this CVE does not indicate confidentiality loss, integrity loss, remote code execution, or known active exploitation in the supplied sources.
Technical view
In TFLite SpaceToDepth, the Prepare step did not check whether params->block_size was zero before division. A crafted model could set block_size to zero and cause a division-by-zero condition. TensorFlow listed fixes for 2.5.0 and supported patched branches 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
Likely exposure
Exposure is most likely in applications, services, or pipelines using affected TensorFlow/TFLite versions and loading TFLite models from users, partners, or other untrusted sources. Systems using only trusted, internally generated models have lower practical exposure.
Exploitation context
The source bundle does not report active exploitation, and KEV status is false. The CVSS vector indicates local attack vector, high attack complexity, low privileges, no user interaction, and low availability impact only.
Researcher notes
Focus validation on TFLite SpaceToDepth model handling and version exposure. Avoid assuming broader TensorFlow runtime impact beyond the affected ranges and operator behavior stated by the advisory. Evidence is sufficient for impact and fixed versions, but incomplete for real-world exploitation status beyond KEV=false.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or fixed supported releases 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Inventory services and pipelines that load TensorFlow Lite models.
- Restrict externally supplied TFLite models until affected deployments are upgraded.
- Review TensorFlow advisory and fixing commit for exact vendor guidance.
Validation and detection
- Check deployed TensorFlow and TFLite versions against the affected ranges.
- Identify workflows accepting TFLite models from users, partners, or automated imports.
- Confirm upgraded builds include the referenced TensorFlow fix commit.
- Run existing model ingestion regression tests after upgrade.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
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-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29587 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- 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
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.
CVSS vector scores
1 official scoreWe 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.
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
2.5LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2gCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7CVE reference · x_refsource_MISC
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Divide By Zero
Divide By Zero represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.