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CVE Record

CVE-2021-29560: Heap buffer overflow in `RaggedTensorToTensor`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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

CVE-2021-29560 is a low-severity TensorFlow memory-safety flaw. A user who can run or influence TensorFlow operations could cause a crash or limited availability impact through malformed inputs to `RaggedTensorToTensor`. The provided sources do not show active exploitation.

Executive priority

Treat as routine patch management unless TensorFlow is exposed to untrusted internal users or shared compute workloads. The business impact is expected to be limited availability disruption, not data theft, based on the provided CVSS and advisory evidence.

Technical view

TensorFlow `tf.raw_ops.RaggedTensorToTensor` used one index across two arrays. Because input shapes are user-controlled, `parent_output_index` can be shorter than `row_split`, causing heap out-of-bounds access. CVSS 3.1 is 2.5: local attack, high complexity, low privileges, no confidentiality or integrity impact, low availability impact.

Likely exposure

Exposure is most likely in systems running TensorFlow versions listed as affected: before 2.1.4, 2.2.x before 2.2.3, 2.3.x before 2.3.3, and 2.4.x before 2.4.2. Risk depends on whether untrusted users, jobs, models, or data can reach this TensorFlow operation.

Exploitation context

The source bundle reports no CISA KEV listing and provides no evidence of active exploitation. The CVSS vector indicates exploitation requires local access, low privileges, and high complexity, with only availability impact identified.

Researcher notes

Focus validation on dependency versions and reachable ML execution paths. The root issue is array-index misuse inside `RaggedTensorToTensor`, producing heap out-of-bounds access when related input-derived arrays differ in length. Do not assume remote exploitation from the provided sources.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a fixed supported cherry-pick release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches apply.
  • Check current TensorFlow vendor guidance before relying on older branches.
  • Limit untrusted execution in ML notebooks, batch jobs, and model-serving pipelines.
  • Track SBOMs and container images for vulnerable TensorFlow versions.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, images, and dependency lockfiles.
  • Compare findings against the affected version ranges in the advisory.
  • Confirm production workloads use a fixed TensorFlow release.
  • Review whether untrusted users can submit TensorFlow jobs or inputs.
  • Document any compensating controls if immediate upgrade is not possible.
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-125: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29560 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-2021-29560Attack 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-125

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

CWE-125 · source CWE mapping

Out-of-bounds Read

Out-of-bounds Read represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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