Security readout for executives and security teams
Plain-English summary
This TensorFlow flaw can crash affected machine-learning workloads when convolution operators process invalid shapes. It does not expose data or allow code execution in the provided sources, but it can interrupt availability for systems running vulnerable TensorFlow versions.
Executive priority
Prioritize patching internet-adjacent or multi-tenant ML environments first. This is availability-focused, not a confidentiality or integrity issue in the provided evidence, so urgency depends on how critical TensorFlow-backed services are to operations.
Technical view
CVE-2021-37675 is a CWE-369 divide-by-zero issue in TensorFlow convolution operator shape inference. Missing validation before division and modulo operations can cause a denial-of-service crash. The CVSS 3.1 vector is local, low complexity, low privilege, no user interaction, availability impact high.
Likely exposure
Exposure is most likely in ML services, notebooks, pipelines, or products using TensorFlow versions >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, or <2.3.4, especially where users can submit models, graphs, or workloads.
Exploitation context
The bundle does not show CISA KEV listing or active exploitation. Sources describe denial of service via crash, requiring local access and low privileges under CVSS. Treat untrusted ML workload execution as the main risk scenario.
Researcher notes
The vulnerable area is TensorFlow shape inference for most convolution operators, specifically missing validation before divisions and modulo operations. The advisory names commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4 as the patch. No public exploit evidence is provided in the bundle.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a fixed supported branch release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where branch constraints apply.
- Review the TensorFlow advisory before relying on compensating controls.
- Restrict untrusted model or workload execution until vulnerable runtimes are patched.
- Rebuild containers and redeploy services that bundle affected TensorFlow versions.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and CI images.
- Check dependency lockfiles and runtime package metadata for affected version ranges.
- Confirm deployed runtimes include the referenced TensorFlow fix or fixed release.
- Review ML service logs for unexplained TensorFlow crashes or availability events.
- Run normal 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
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-2021-37675 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-9c8h-2mv3-49wwCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4CVE 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.