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CVE Record

CVE-2021-29618: Crash in `tf.transpose` with complex inputs

TensorFlow is an end-to-end open source platform for machine learning. Passing a complex argument to `tf.transpose` at the same time as passing `conjugate=True` argument results in a crash. 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

TensorFlow can crash when transposing complex-number data while conjugation is enabled. The documented impact is limited availability loss, not data theft or modification. Business urgency is low unless vulnerable TensorFlow is exposed to users who can run or influence ML code paths.

Executive priority

Treat this as routine dependency hygiene unless vulnerable TensorFlow is used in shared or user-controlled ML execution. It should not displace higher-severity patching, but fixed versions are available and should be adopted during normal maintenance.

Technical view

CVE-2021-29618 is a CWE-755 improper exceptional-condition handling issue in tf.transpose. Passing a complex argument with conjugate=True can crash TensorFlow. The CVSS 3.1 score is 2.5, with local attack vector, high complexity, low privileges, and low availability impact only.

Likely exposure

Exposure is most likely in environments running affected TensorFlow versions and allowing users, jobs, or model code to exercise TensorFlow tensor operations. The affected ranges are below 2.1.4, 2.2.0 to before 2.2.3, 2.3.0 to before 2.3.3, and 2.4.0 to before 2.4.2.

Exploitation context

The provided sources do not show active exploitation, and KEV status is false. The issue requires local ability or equivalent application-level influence over TensorFlow execution. The known outcome is a crash, so the credible risk is denial of service in affected ML workloads.

Researcher notes

The public record describes a crash condition, not memory disclosure or code execution. Evidence is sufficient for affected-version triage and remediation planning, but the bundle does not provide exploitation in the wild or broader product impact beyond TensorFlow itself.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
  • For older branches, use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 as applicable.
  • Prioritize systems where untrusted users can run notebooks, jobs, or model code.
  • Check current TensorFlow vendor guidance before relying on unsupported versions.
  • Restrict who can submit or execute TensorFlow workloads in shared environments.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, images, and ML pipelines.
  • Compare installed versions against the affected ranges listed in the advisory.
  • Review code paths using tf.transpose with complex tensors and conjugation enabled.
  • Confirm upgraded environments report a fixed TensorFlow release.
  • Record whether vulnerable workloads are user-accessible or only internally controlled.
Prepared
Confidence
high
Sources
6

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

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

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cwe · low confidence lookup

CWE-755: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29618 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
5Source 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-2021-29618Attack 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.

Open CWE intelligenceSame-CWE CVEs