Security readout for executives and security teams
Plain-English summary
A specially crafted TensorFlow Lite model can crash affected TensorFlow versions by triggering a divide-by-zero condition. The main business impact is denial of service for systems that process TFLite models, especially where models come from users, partners, or external pipelines.
Executive priority
Treat as a targeted availability risk, not a broad compromise issue. Patch during the normal vulnerability cycle, faster for systems that accept external TFLite models or where service interruption has business impact.
Technical view
CVE-2021-37691 is a CWE-369 divide-by-zero flaw in TensorFlow Lite LSH projection logic. The vendor patched it in commit 0575b640091680cfb70f4dd93e70658de43b94f9, with fixes planned for TensorFlow 2.6.0 and backports to 2.5.1, 2.4.3, and 2.3.4.
Likely exposure
Exposure is most likely where affected TensorFlow versions load TFLite models. Risk rises when model files are user-supplied, partner-provided, downloaded, or otherwise outside direct engineering control.
Exploitation context
The CVSS vector is local, low complexity, low privileges, no user interaction, and high availability impact. The provided sources do not show active exploitation, and the CVE is not listed as KEV in the bundle.
Researcher notes
The strongest evidence is the TensorFlow advisory and fixing commit. The bundle identifies affected TensorFlow ranges and planned fixed releases, but does not provide evidence of exploitation in the wild or broader product-specific impact.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or patched supported releases 2.5.1, 2.4.3, or 2.3.4.
- Inventory applications, services, and mobile or edge builds that load TFLite models.
- Restrict processing of untrusted TFLite models until affected TensorFlow builds are patched.
- Check TensorFlow vendor guidance for downstream packaging or additional backport details.
Validation and detection
- Confirm deployed TensorFlow versions are outside the affected version ranges listed in the CVE bundle.
- Review model ingestion paths for user-supplied, partner-supplied, or third-party TFLite files.
- Verify the TensorFlow commit or patched release is present in builds that process TFLite models.
- Prioritize regression testing for services where model parsing failure can interrupt production workflows.
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-2021-37691 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
- Medium
- CVSS
- 5.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
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:L/PR:L/UI:N/S:U/C:N/I:N/A:H1.83.6Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
5.5MediumVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27qf-jwm8-g7f3CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9CVE 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.