Security readout for executives and security teams
Plain-English summary
CVE-2021-29594 is a low-severity TensorFlow Lite reliability issue. Some convolution calculations could divide by a user-controlled zero value, potentially crashing the affected component. The business risk is mainly limited service or application availability disruption where vulnerable TensorFlow Lite code processes untrusted inputs or models.
Executive priority
Handle through normal vulnerability management unless vulnerable TensorFlow Lite processing is exposed to untrusted users or tenants. The issue is low severity and availability-only, but it should still be patched during regular dependency maintenance because fixed releases are identified.
Technical view
TFLite's convolution code performed divisions where the divisor was user controlled and not checked for non-zero, mapped to CWE-369. The CVSS 3.1 score is 2.5 with local access, high complexity, low privileges, no confidentiality or integrity impact, and low availability impact. Fixes were planned for TensorFlow 2.5.0 and supported patch releases.
Likely exposure
Exposure is most likely in applications or workflows using vulnerable TensorFlow versions with TensorFlow Lite convolution functionality. The listed affected ranges are before 2.1.4, 2.2.0 to before 2.2.3, 2.3.0 to before 2.3.3, and 2.4.0 to before 2.4.2.
Exploitation context
The source bundle does not show CISA KEV listing or other cited evidence of active exploitation. CVSS indicates local attack vector, high attack complexity, low privileges required, and no user interaction. Treat this as a targeted reliability risk rather than a remote compromise issue based on available evidence.
Researcher notes
Evidence is limited to the TensorFlow advisory, CVE data, and fixing commit. Do not assume broader product impact beyond TensorFlow/TFLite. Useful validation centers on version identification and whether local or application-level users can influence convolution parameters or model artifacts reaching vulnerable code.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or later where feasible.
- For older supported branches, apply TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 as applicable.
- Inventory applications bundling TensorFlow or TensorFlow Lite libraries.
- Review vendor advisory and release notes before deploying fixes.
Validation and detection
- Check dependency manifests and runtime packages for TensorFlow versions in affected ranges.
- Confirm deployed artifacts no longer include vulnerable TensorFlow Lite convolution code versions.
- Prioritize systems that process untrusted models, inputs, or tenant-controlled ML artifacts.
- Record compensating controls if immediate upgrade is not possible.
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
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2021-29594 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-3qgw-p4fm-x7gfCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552CVE 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.