Security readout for executives and security teams
Plain-English summary
This is a low-severity denial-of-service issue in TensorFlow Lite. A user who can supply a specially crafted model to an affected TensorFlow installation may trigger a division-by-zero crash in hashtable lookup handling. The sources do not report data theft, data modification, or active exploitation.
Executive priority
Treat as routine maintenance unless the business accepts third-party TFLite models. For exposed model-processing services, schedule prompt upgrade work to reduce crash risk and operational disruption.
Technical view
TensorFlow Lite's hashtable lookup kernel can divide by zero when a crafted model sets the first dimension of `values` to 0. The advisory lists affected TensorFlow release lines before 2.1.4, 2.2.3, 2.3.3, and 2.4.2. Fixes were planned for TensorFlow 2.5.0 and supported backports.
Likely exposure
Exposure is most likely where affected TensorFlow versions load TFLite models supplied by users, partners, customers, or automated pipelines. Systems using only trusted models have lower practical exposure.
Exploitation context
The CVSS vector indicates local access, high attack complexity, low privileges, and low availability impact. CISA KEV status is false, and the provided sources do not claim active exploitation.
Researcher notes
The issue maps to CWE-369 and is limited by the published CVSS vector. The public description identifies the vulnerable TFLite hashtable lookup path and the zero first dimension condition, but no weaponized exploit or active campaign is cited.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or the fixed supported backport for your release line.
- Prioritize systems that accept or process externally supplied TFLite models.
- Restrict untrusted model uploads and model ingestion paths until upgraded.
- Review TensorFlow's advisory and commit for vendor-confirmed remediation details.
Validation and detection
- Inventory TensorFlow versions in applications, containers, notebooks, and ML build images.
- Identify services that load TFLite models from untrusted or semi-trusted sources.
- Confirm affected versions are upgraded to a fixed release line.
- Run existing ML inference regression tests after upgrading TensorFlow.
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-2021-29604 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-8rm6-75mf-7r7rCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2CVE 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.