Security readout for executives and security teams
Plain-English summary
This TensorFlow issue can let a user who can run TensorFlow operations crash a process through an internal assertion in a sparse tensor operation. The sourced impact is denial of service only; no confidentiality, integrity, privilege escalation, or active exploitation evidence is provided.
Executive priority
Handle through normal vulnerability remediation unless this runs in shared or customer-controlled ML execution environments. The business risk is service disruption, not data compromise, but exposed multi-tenant notebooks or inference systems deserve faster scheduling.
Technical view
CVE-2021-29523 is a CWE-190 overflow-related CHECK failure in tf.raw_ops.AddManySparseToTensorsMap. The implementation used sparse_shape values as output dimensions, and TensorShape construction could abort when dimension initialization failed. TensorFlow planned fixes in 2.5.0 and supported backports.
Likely exposure
Exposure is likely limited to TensorFlow deployments on affected versions: before 2.1.4, 2.2.0-2.2.2, 2.3.0-2.3.2, and 2.4.0-2.4.1. Risk is higher where untrusted users, notebooks, model code, or tensor inputs can reach raw TensorFlow operations.
Exploitation context
The CVSS vector is local, high complexity, low privileges required, no user interaction, and low availability impact. The source bundle says KEV is false, and the cited sources do not report active exploitation. Treat this as a process crash risk, not a remote takeover issue.
Researcher notes
The key condition is TensorShape construction from sparse_shape dimensions in AddManySparseToTensorsMap. The advisory attributes the failure to legacy CHECK-based construction and recommends safer shape-building APIs. Public evidence here is sufficient for version triage, but not for claiming exploitation in the wild.
Mitigation direction
- Inventory TensorFlow versions in applications, notebooks, workers, containers, and ML pipelines.
- Upgrade affected branches to TensorFlow 2.5.0 or patched supported backports.
- Restrict untrusted users from executing arbitrary TensorFlow operations in shared environments.
- Check TensorFlow vendor guidance before applying alternate mitigations.
Validation and detection
- Confirm installed TensorFlow versions are not in the affected version ranges.
- Review environments where tenant-controlled code or inputs can reach TensorFlow raw ops.
- Verify dependency manifests and container images resolve to patched TensorFlow builds.
- Confirm operational monitoring would detect TensorFlow worker crashes or restart loops.
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-190: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29523 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/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7cCVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2cpx-427x-q2c6CVE reference · x_refsource_CONFIRM
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.
Integer Overflow or Wraparound
Integer Overflow or Wraparound represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.