Security readout for executives and security teams
Plain-English summary
CVE-2021-37662 is a TensorFlow bug in boosted-trees calculations. A low-privileged local attacker who can supply crafted inputs to affected operations could trigger undefined behavior, risking data integrity and service availability. It is important for ML platforms using affected TensorFlow versions, but the sources do not show active exploitation.
Executive priority
Patch on the normal high-severity track, faster for shared ML infrastructure or environments processing user-controlled jobs. The business concern is disruption or incorrect computation in affected TensorFlow workloads, not confirmed widespread exploitation.
Technical view
Affected TensorFlow versions do not validate input values in BoostedTreesCalculateBestGainsPerFeature and BoostedTreesCalculateBestFeatureSplitV2, allowing reference binding to nullptr. The issue is CWE-824 with CVSS 3.1 score 7.1, local attack vector, low complexity, low privileges, no user interaction, high integrity and availability impact.
Likely exposure
Exposure is most likely in systems running TensorFlow versions before 2.3.4, 2.4.3, 2.5.1, or 2.6.0 where boosted-trees operations may process inputs controlled by local users, tenants, jobs, or pipelines. Remote-only web exposure is not established by the provided sources.
Exploitation context
The source bundle reports no KEV listing and provides no cited evidence of exploitation in the wild. The CVSS vector indicates local access with low privileges is required, with no user interaction. Treat shared ML notebooks, batch workers, and multi-tenant training infrastructure as higher-risk contexts.
Researcher notes
The public advisory ties the flaw to missing input validation and reference binding to nullptr in boosted-trees stats operations. Analysis should stay focused on affected TensorFlow branches and the two named kernels. The provided sources do not establish remote exploitability, public exploit availability, or exploitation in the wild.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a patched supported release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where branch pinning is required.
- Restrict untrusted local users or jobs from invoking affected TensorFlow workloads until patched.
- Check TensorFlow vendor guidance before applying compensating controls.
- Prioritize patching shared ML and batch-processing environments.
Validation and detection
- Inventory TensorFlow versions across applications, notebooks, containers, and training images.
- Flag versions before 2.3.4, 2.4.3, 2.5.1, or 2.6.0.
- Identify workloads using TensorFlow boosted-trees operations.
- Confirm patched package versions are deployed in runtime environments.
- Review whether untrusted users or jobs can submit TensorFlow inputs locally.
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
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ATT&CK lookup starting points
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CWE-824: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37662 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.1 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/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:H/A:H1.85.2Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
7.1HighVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f5cx-5wr3-5qrcCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7CVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5adCVE 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.
Access of Uninitialized Pointer
Access of Uninitialized Pointer represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.