Security readout for executives and security teams
Plain-English summary
CVE-2021-37683 is a TensorFlow Lite availability issue. A division operation can divide by zero because affected versions did not check divisor tensor values. The documented impact is denial of service, not data theft or code execution.
Executive priority
Schedule remediation through normal vulnerability management, with higher priority for production services where TensorFlow Lite crashes would interrupt business workflows. This is not evidenced as data compromise or remote takeover from the supplied sources.
Technical view
TensorFlow Lite div.cc lacked a zero-divisor validation path for division kernels, causing CWE-369 division by zero. The CVSS vector is local, low-complexity, low-privilege, no user interaction, unchanged scope, with high availability impact only.
Likely exposure
Exposure is limited to systems using affected TensorFlow or TensorFlow Lite versions: before 2.3.4, 2.4.0 to before 2.4.3, and 2.5.0 to before 2.5.1. The source bundle does not indicate remote network exposure.
Exploitation context
The CVSS vector requires local access and low privileges. The source bundle does not identify active exploitation, public weaponization, or CISA KEV listing. Treat this mainly as a crash or service interruption risk in local TensorFlow Lite workloads.
Researcher notes
The useful validation point is version and patch provenance, not exploit reproduction. Sources name the vulnerable kernel implementation and fixed commit, but do not provide broader affected product details beyond TensorFlow version ranges.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or later where feasible.
- Use fixed supported releases 2.5.1, 2.4.3, or 2.3.4.
- Confirm downstream packages include commit 1e206baedf8bef0334cca3eb92bab134ef525a28.
- Check TensorFlow advisory guidance for unsupported or vendor-managed builds.
Validation and detection
- Inventory applications and containers using TensorFlow or TensorFlow Lite.
- Compare installed versions against the affected version ranges.
- Confirm fixed versions or the referenced patch commit are present.
- Review crash telemetry for TensorFlow Lite division-operation failures.
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-2021-37683 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-rhrq-64mq-hf9hCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28CVE 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.