Security readout for executives and security teams
Plain-English summary
This issue affects TensorFlow Lite in older TensorFlow releases. Malformed or unexpected quantization metadata can cause some TFLite operations to use uninitialized values, creating potential incorrect results or crashes. The business risk is mainly to applications that run affected TensorFlow Lite code on untrusted or externally supplied models.
Executive priority
Treat this as a moderate-priority dependency remediation. Prioritize systems that accept models from outside trusted build pipelines or rely on TensorFlow Lite output for operational decisions. Routine patch management is appropriate unless affected model inputs are exposed to untrusted users.
Technical view
TensorFlow Lite operations using quantization could access quantization.params when quantization.type is kTfLiteNoQuantization. Missing checks across affected kernels can lead to use of uninitialized values. CVSS 3.1 is 4.4 with local access and low privileges required, and low integrity and availability impact.
Likely exposure
Exposure is most likely where products, services, or packaged applications use affected TensorFlow versions with TensorFlow Lite and process quantized models. The listed affected ranges are TensorFlow before 2.3.4, 2.4.0 to before 2.4.3, and 2.5.0 to before 2.5.1.
Exploitation context
The provided sources do not report active exploitation, and the CVE is not listed as KEV. The CVSS vector indicates local access, low attack complexity, low privileges, and no user interaction. Evidence supports a reliability and model-processing integrity concern, not a remote unauthenticated takeover.
Researcher notes
Focus validation on TFLite quantization paths in affected TensorFlow releases. The source bundle identifies CWE-908 and three patch commits, but does not provide evidence of exploitation in the wild. Avoid assuming broader TensorFlow components are affected beyond the described TFLite quantized operations.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or a fixed supported patch release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where those branches apply.
- Review the TensorFlow GitHub advisory for vendor guidance.
- Rebuild and redeploy applications bundling affected TensorFlow Lite code.
- Check lockfiles, containers, and mobile or embedded packages for affected TensorFlow versions.
Validation and detection
- Inventory TensorFlow and TensorFlow Lite versions across builds and runtime artifacts.
- Identify applications that process quantized TFLite models.
- Confirm deployed versions are outside the affected ranges.
- Verify patched builds include the referenced TensorFlow fix commits.
- Run existing model-processing regression tests after upgrading.
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-908: 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-2021-37682 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
- 4.4 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/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:L/PR:L/UI:N/S:U/C:N/I:L/A:L1.82.5Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
4.4MediumVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxwCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5CVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887CVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538CVE 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.
Use of Uninitialized Resource
Use of Uninitialized Resource represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.