Security readout for executives and security teams
Plain-English summary
A malicious or malformed TensorFlow Lite model can trigger a crash in affected TensorFlow versions by setting TransposeConv stride values to zero. The documented impact is limited availability loss, not data theft or code execution.
Executive priority
Treat as routine patching unless the organization accepts third-party TensorFlow Lite models. Prioritize higher if untrusted model ingestion is part of a customer-facing workflow.
Technical view
The optimized TFLite TransposeConv operator can divide by zero when stride_h or stride_w is 0. Code paths calling this function must validate arguments. Affected TensorFlow ranges include versions before 2.1.4 and specific 2.2.x, 2.3.x, and 2.4.x ranges.
Likely exposure
Exposure is most likely where affected TensorFlow/TFLite versions load attacker-supplied or otherwise untrusted models. Systems using only trusted, internally built models have lower practical risk.
Exploitation context
The source bundle does not indicate active exploitation, and KEV is false. CVSS rates exploitation as local, high complexity, and requiring low privileges, with no confidentiality or integrity impact.
Researcher notes
This is CWE-369 in the optimized TFLite TransposeConv implementation. Evidence supports denial-of-service risk through invalid model parameters, but not code execution or active exploitation.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or the patched supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Reject untrusted TensorFlow Lite models unless business-required.
- Validate TransposeConv stride values before invoking affected code paths.
- Check TensorFlow advisory and commit guidance for integration details.
Validation and detection
- Inventory TensorFlow and TFLite versions across applications and embedded builds.
- Review dependency lockfiles, mobile packages, containers, and ML inference images.
- Identify services or apps that accept externally supplied TFLite models.
- Confirm model validation rejects zero TransposeConv stride values.
- Verify upgraded builds use a fixed 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-2021-29588 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-vfr4-x8j2-3rf9CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834dCVE 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.