Security readout for executives and security teams
Plain-English summary
TensorFlow Lite pooling operations can crash because affected versions do not check for division by zero. The published impact is availability only, not data theft or tampering. Business urgency is highest where TensorFlow Lite supports production inference, mobile or edge workloads, or services that process untrusted ML inputs.
Executive priority
Treat as a moderate availability risk. It should be remediated in normal patch cycles, faster for production ML inference systems where a crash could disrupt customer-facing workflows or operational automation.
Technical view
Affected TensorFlow versions lack zero-divisor checks in TFLite pooling implementations, causing CWE-369 division by zero. CVSS 3.1 is 5.5: local access, low complexity, low privileges, no user interaction, unchanged scope, and high availability impact. TensorFlow patched the issue and scheduled fixed releases.
Likely exposure
Exposure is limited to systems using TensorFlow/TFLite versions >=2.5.0 and <2.5.1, >=2.4.0 and <2.4.3, or <2.3.4. Risk is more relevant where local users or application workflows can feed data or models into affected TFLite pooling paths.
Exploitation context
The source bundle does not show CISA KEV listing or active exploitation. The CVSS vector indicates local, low-privilege exploitation with no user interaction and availability impact. Evidence supports denial-of-service risk, not remote code execution or confidentiality compromise.
Researcher notes
The advisory attributes the flaw to missing divisor checks in TFLite pooling code. Available evidence is enough to identify affected version ranges and fixed release targets, but it does not describe active exploitation, broad remote exposure, or compensating controls beyond upgrading and limiting untrusted inputs.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or a fixed supported backport when available.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 for affected supported branches.
- Review TensorFlow advisory GHSA-q7f7-544h-67h9 for vendor-specific guidance.
- Restrict who can provide models or inputs to TFLite inference workflows.
Validation and detection
- Inventory deployed TensorFlow and TensorFlow Lite versions across applications and build artifacts.
- Confirm no runtime uses affected versions listed in the advisory.
- Prioritize systems that process user-controlled or partner-supplied ML inputs.
- Verify patched builds include commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695 or fixed release versions.
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-2021-37684 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-q7f7-544h-67h9CVE reference · x_refsource_CONFIRM
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.