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CVE Record

CVE-2021-29619: Segfault in `tf.raw_ops.SparseCountSparseOutput`

TensorFlow is an end-to-end open source platform for machine learning. Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

This is a low-severity TensorFlow crash bug. Invalid arguments to one raw TensorFlow operation can cause a segmentation fault, affecting availability of the local process. It does not indicate data theft or data modification in the provided sources.

Executive priority

Treat as routine patch management unless TensorFlow runs in shared or multi-tenant ML environments. The business risk is localized service disruption, not confirmed compromise or data exposure.

Technical view

CVE-2021-29619 affects tf.raw_ops.SparseCountSparseOutput in TensorFlow. Fuzzing found that invalid arguments can trigger a segfault. CVSS 3.1 is 2.5 with local access, high complexity, low privileges, no user interaction, and low availability impact.

Likely exposure

Exposure is most plausible where vulnerable TensorFlow versions execute user-controlled TensorFlow code, jobs, or operation inputs. Affected ranges include versions before 2.1.4, 2.2.0-2.2.2, 2.3.0-2.3.2, and 2.4.0-2.4.1.

Exploitation context

The provided sources do not show active exploitation, and the CVE is not marked KEV. The issue was discovered by fuzzing and requires local, low-privileged ability to pass invalid arguments to the affected operation.

Researcher notes

The core evidence is the TensorFlow advisory and fix commit. Sources do not provide exploit-in-the-wild evidence or detailed impact beyond segfault availability loss. Keep validation focused on version exposure and whether untrusted users can invoke TensorFlow raw ops.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or later where practical.
  • For supported older branches, use 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Restrict untrusted users from running arbitrary TensorFlow operations on shared systems.
  • Review TensorFlow vendor guidance before applying compensating controls.

Validation and detection

  • Inventory TensorFlow package versions across notebooks, workers, and ML services.
  • Check for vulnerable ranges listed in the advisory and CVE record.
  • Confirm upgraded environments report patched TensorFlow versions.
  • Review shared ML environments for untrusted code execution paths.
Prepared
Confidence
high
Sources
4

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

Conservative CVE-to-ATT&CK context

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ATT&CK lookup starting points

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cwe · low confidence lookup

CWE-755: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29619 mapping review

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Vulnerability profileCVE Program record
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

Official CVE source material

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.

1CVSS vectors
0Timeline events
0ADP providers
3Source links

CVSS vector scores

1 official score

We 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.

ScoreVersionSeverityVectorExploitImpactSource
2.5CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

2.5Low
CVSS 3.1 vector shape for CVE-2021-29619Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
tensorflowtensorflow< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-755

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-755 · source CWE mapping

Improper Handling of Exceptional Conditions

Improper Handling of Exceptional Conditions represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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