Security readout for executives and security teams
Plain-English summary
This is a low-severity TensorFlow Lite availability issue. A specially crafted model can trigger a division-by-zero in the SVDF operator, causing affected processing to fail. The cited sources do not show data theft, integrity impact, remote compromise, or active exploitation.
Executive priority
Treat as routine remediation unless your business processes untrusted ML models. Prioritize patching in ML services, mobile apps, or edge systems where model files can come from outside controlled build pipelines.
Technical view
CVE-2021-29598 is CWE-369 in TensorFlow Lite's SVDF implementation. If a model sets the SVDF rank parameter to zero, vulnerable TensorFlow versions can divide by zero. CVSS 3.1 is 2.5 with local attack vector, high complexity, low privileges, and low availability impact.
Likely exposure
Exposure is most likely where applications use affected TensorFlow/TFLite versions and load untrusted, user-supplied, third-party, or externally generated models. The source bundle lists vulnerable TensorFlow ranges before patched 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0.
Exploitation context
The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and availability-only impact. KEV is false, and the provided sources do not cite active exploitation or public weaponization.
Researcher notes
The source evidence supports a denial-of-service style failure in the SVDF TFLite operator only. It does not support confidentiality or integrity impact. Avoid assuming exploitability beyond crafted model handling in affected TensorFlow versions.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 where feasible.
- Use patched supported releases: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Restrict ingestion of untrusted or unauthenticated TFLite models.
- Review the TensorFlow advisory and fix commit for vendor-specific guidance.
Validation and detection
- Inventory TensorFlow and TensorFlow Lite versions in applications and images.
- Check SBOMs and lockfiles for affected TensorFlow version ranges.
- Identify workflows that accept externally supplied model files.
- Confirm deployed builds use a patched TensorFlow release.
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-2021-29598 mapping review
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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-pmpr-55fj-r229CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682CVE 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.