Security readout for executives and security teams
Plain-English summary
This is a low-severity TensorFlow Lite denial-of-service issue. A crafted model can cause a divide-by-zero crash in the BatchToSpaceNd operator. Business urgency is highest where untrusted or customer-supplied models are loaded by affected TensorFlow versions.
Executive priority
Treat as routine patching unless the organization processes untrusted models. For exposed model-ingestion workflows, schedule remediation promptly because the impact is service disruption, not data compromise.
Technical view
TensorFlow Lite BatchToSpaceNd can divide by zero when a crafted model sets a block input dimension to 0, making block_shape 0. CVSS 3.1 is 2.5: local attack vector, high complexity, low privileges, and low availability impact only.
Likely exposure
Exposure is likely limited to applications using affected TensorFlow or TFLite versions that load crafted or untrusted models. Systems using only trusted models or patched releases have materially lower exposure.
Exploitation context
The source bundle does not indicate active exploitation, KEV listing, public exploitation, or remote attackability. The advisory describes a crafted model causing a local availability impact.
Researcher notes
This is CWE-369 in TFLite BatchToSpaceNd. The cited advisory ties the issue to zero-valued block_shape input and patched TensorFlow releases. Evidence supports availability impact only; confidentiality and integrity impact are not indicated.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or patched supported maintenance releases.
- For 2.4, 2.3, 2.2, and 2.1 branches, use the vendor cherry-picked fixes.
- Do not load untrusted TFLite models until patched or appropriately sandboxed.
- Check TensorFlow advisory guidance for version-specific remediation.
Validation and detection
- Inventory TensorFlow and TensorFlow Lite versions in applications and build artifacts.
- Identify services or edge apps that load externally supplied TFLite models.
- Confirm affected versions are no longer present in dependency manifests or runtime images.
- Review model provenance controls for untrusted or customer-provided models.
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-2021-29593 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-cfx7-2xpc-8w4hCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044CVE 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.