Security readout for executives and security teams
Plain-English summary
CVE-2021-37687 is a TensorFlow Lite information disclosure flaw. A specially crafted model can trigger out-of-bounds heap reads in Gather and GatherNd handling, potentially exposing sensitive process memory. The known impact is confidentiality only, and the source bundle does not show active exploitation.
Executive priority
Treat as moderate priority. Patch during the normal security cycle, with faster handling for products or services that ingest external ML models. The main business risk is unintended disclosure of process memory from systems running affected TensorFlow Lite code.
Technical view
TFLite GatherNd and Gather did not validate negative indices in affected TensorFlow versions. That missing bounds check can cause heap out-of-bounds reads, classified as CWE-125. CVSS 3.1 is 5.5, with local attack vector, low complexity, low privileges, no user interaction, and high confidentiality impact.
Likely exposure
Exposure is most likely where affected TensorFlow versions process TensorFlow Lite models, especially third-party or user-supplied models. Listed affected ranges include TensorFlow 2.5.0 before 2.5.1, 2.4.x before 2.4.3, and versions before 2.3.4.
Exploitation context
The advisory describes attacker control through a crafted model containing negative index values. The bundle marks CISA KEV as false and provides no cited evidence of active exploitation, public weaponization, or exploitation in the wild.
Researcher notes
Focus review on TFLite Gather and GatherNd negative index handling and fixed-version verification. Evidence supports heap out-of-bounds read only; integrity and availability impact are not described. Do not assume active exploitation without additional sourced evidence.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or fixed supported patch releases.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where staying on older branches.
- Restrict untrusted TFLite model ingestion until affected environments are patched.
- Review vendor advisory and patch commits for implementation-specific guidance.
Validation and detection
- Inventory TensorFlow versions in source, lockfiles, images, and deployed ML services.
- Identify workloads that load TensorFlow Lite models from external or user-controlled sources.
- Confirm deployed versions are outside the affected ranges listed in the advisory.
- Run regression tests for TFLite model loading 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-125: 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-2021-37687 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:H/I:N/A:N
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:H/I:N/A:N1.83.6Primary 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:H/I:N/A:N
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8fCVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179dCVE 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.
Out-of-bounds Read
Out-of-bounds Read represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.