Security readout for executives and security teams
Plain-English summary
CVE-2021-29558 is a low-severity TensorFlow flaw where crafted use of SparseSplit can trigger a heap buffer overflow. The documented impact is limited availability loss, not data theft or integrity compromise. It matters mainly where TensorFlow processes untrusted local inputs, models, or workloads on older affected releases.
Executive priority
Handle through normal dependency hygiene unless the organization runs shared or multi-tenant TensorFlow workloads with untrusted inputs. The documented impact is low and availability-focused, but upgrading is straightforward and should be included in routine ML platform maintenance.
Technical view
The vulnerable tf.raw_ops.SparseSplit path accessed an array element using a user-controlled offset, causing a heap buffer overflow. CVSS 3.1 is 2.5 with local attack vector, high complexity, low privileges required, no user interaction, and low availability impact only.
Likely exposure
Exposure is most likely in systems running TensorFlow versions before the fixed releases: 2.1.4, 2.2.3, 2.3.3, 2.4.2, or 2.5.0. Risk is higher when untrusted users, jobs, or ML inputs can reach TensorFlow SparseSplit behavior locally.
Exploitation context
The source bundle does not show active exploitation, and CISA KEV is false. The CVSS vector requires local access and low privileges with high attack complexity. Treat this as a targeted denial-of-service risk in affected ML runtime environments, not a broadly remote compromise issue.
Researcher notes
Evidence supports CWE-787 heap buffer overflow in TensorFlow SparseSplit caused by user-controlled offset indexing. The advisory names fixed releases and affected supported branches. No provided source establishes public exploitation, remote attackability, confidentiality impact, or integrity impact.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
- Check vendor guidance before applying nonstandard backports or downstream package fixes.
- Limit untrusted local workloads that can execute TensorFlow operations on shared ML infrastructure.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Confirm dependency lockfiles and runtime images use a fixed TensorFlow release.
- Review whether untrusted users or jobs can invoke TensorFlow SparseSplit paths.
- Verify security scanners map TensorFlow packages to CVE-2021-29558 correctly.
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
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-787: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29558 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 2.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/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:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
2.5LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mqh2-9wrp-vx84CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31CVE 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 Write
Out-of-bounds Write represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.