Security readout for executives and security teams
Plain-English summary
A TensorFlow SparseCross bug can let a low-privileged local user or local code path crash a process by triggering a type-confusion CHECK failure. The disclosed impact is limited availability loss, not data theft or code execution. Business urgency is low unless vulnerable TensorFlow is exposed in production pipelines that process untrusted inputs.
Executive priority
Treat as a low-priority reliability fix unless TensorFlow workloads are multi-tenant or process untrusted local inputs. Patch through normal dependency maintenance, with faster action for shared ML platforms where one user can disrupt jobs for others.
Technical view
tf.raw_ops.SparseCross allowed invalid DT_STRING and DT_INT64 type mixing, causing the kernel to mis-handle tensor contents and fail a CHECK. The issue is CWE-843 with CVSS 3.1 score 2.5. Fixed releases were planned for TensorFlow 2.5.0 and cherrypicked to 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
Likely exposure
Exposure is limited to systems running affected TensorFlow versions and invoking tf.raw_ops.SparseCross, especially where local users, jobs, notebooks, or pipelines can influence tensor inputs. The source bundle does not indicate remote unauthenticated exposure.
Exploitation context
The CVSS vector indicates local access, high attack complexity, low privileges required, no user interaction, and low availability impact only. KEV is false, and the provided sources do not report active exploitation.
Researcher notes
The root issue is type confusion in TensorFlow SparseCross. The advisory states preventing DT_STRING and DT_INT64 mixing resolves it. Evidence supports denial of service only; there is no cited confidentiality, integrity, remote exploitation, or active exploitation evidence.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or an applicable fixed cherrypick release.
- Prioritize 2.4.2, 2.3.3, 2.2.3, or 2.1.4 for supported affected branches.
- Review vendor advisory before relying on unsupported TensorFlow versions.
- Restrict who can run untrusted TensorFlow jobs in shared environments.
- Monitor ML job failures for repeated SparseCross-related crashes.
Validation and detection
- Inventory TensorFlow versions across production, CI, notebooks, and model-serving images.
- Check dependency manifests and runtime environments for affected version ranges.
- Identify code paths that invoke tf.raw_ops.SparseCross or SparseCross wrappers.
- Confirm deployed images use a fixed TensorFlow release.
- Review crash logs for TensorFlow CHECK failures involving SparseCross.
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-843: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29519 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-772j-h9xw-ffp5CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025CVE 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 Resource Using Incompatible Type ('Type Confusion')
Access of Resource Using Incompatible Type ('Type Confusion') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.