Security readout for executives and security teams
Plain-English summary
This TensorFlow Lite issue can crash affected machine-learning workloads when a specially crafted model is processed. The known impact is limited availability disruption, not data theft or code execution. Business urgency is low unless the organization accepts models from users, partners, or other untrusted sources.
Executive priority
Treat as a low-priority patching item unless external model uploads are part of the business process. The main risk is localized service or application disruption, so remediation should fit normal dependency maintenance unless exposed ingestion paths exist.
Technical view
Optimized TFLite pooling code failed to reject zero stride height or width before padding calculations. A crafted model can set those parameters to zero, causing division by zero. Sources describe low CVSS 3.1 severity, local attack vector, high complexity, required privileges, and availability-only impact.
Likely exposure
Exposure is most likely in systems using affected TensorFlow or TFLite versions while loading user-supplied, partner-supplied, or otherwise untrusted models. Environments using fixed TensorFlow releases or only tightly controlled internal models have lower practical risk.
Exploitation context
The source bundle does not show CISA KEV listing or public evidence of active exploitation. The described abuse requires the ability to supply a specially crafted model to an affected local TensorFlow Lite processing path.
Researcher notes
Focus review on TFLite pooling operators in affected TensorFlow versions and model-loading trust boundaries. Do not assume remote exploitation from the provided evidence. The fix is linked to commit 5f7975d09eac0f10ed8a17dbb6f5964977725adc and planned fixed releases are named in the advisory text.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or vendor backported fixed releases.
- Apply TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Restrict ingestion of untrusted TFLite models until fixed.
- Review TensorFlow advisory for supported upgrade guidance.
- Prioritize exposed model-ingestion services over closed internal pipelines.
Validation and detection
- Inventory TensorFlow and TFLite versions across applications and build artifacts.
- Compare discovered versions against the listed affected ranges.
- Identify workflows that accept external or user-provided TFLite models.
- Confirm patched versions are deployed in production and mobile releases.
- Check crash telemetry for pooling-related model processing failures.
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
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29586 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-26j7-6w8w-7922CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adcCVE 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.