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CVE Record

CVE-2021-29568: Reference binding to null in `ParameterizedTruncatedNormal`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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 flaw that can cause undefined behavior when a specific random-number operation receives an empty shape input. The documented impact is limited availability loss, not data theft or privilege escalation. It matters most for systems that let untrusted users influence TensorFlow operations or model inputs.

Executive priority

Treat this as routine patching unless affected TensorFlow workloads process untrusted model inputs or run in shared environments. It does not warrant emergency response based on the provided evidence.

Technical view

`tf.raw_ops.ParameterizedTruncatedNormal` failed to validate `shape` before reading its first element. An empty `shape` makes `shape_tensor.flat<T>()` empty, causing a reference binding to a null pointer. The listed affected TensorFlow branches were fixed in 2.5.0 and supported patch releases.

Likely exposure

Exposure is limited to applications using affected TensorFlow versions and reachable code paths invoking `ParameterizedTruncatedNormal` with attacker-influenced shape data. The CVSS vector requires local access, low privileges, high complexity, and has only low availability impact.

Exploitation context

The source bundle does not identify active exploitation, and KEV status is false. Exploitation requires a crafted condition in a specific TensorFlow operation, with no cited confidentiality or integrity impact.

Researcher notes

The root cause is missing input validation before accessing `shape[0]`. The documented fix is in TensorFlow commit `5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8`; avoid claiming broader impact without additional evidence.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a listed patched supported branch.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Inventory pinned TensorFlow dependencies in applications, notebooks, images, and ML pipelines.
  • Restrict untrusted control over TensorFlow graph construction or tensor shape inputs until patched.
  • Check the TensorFlow advisory before changing unsupported legacy deployments.

Validation and detection

  • Confirm deployed TensorFlow versions are outside the affected ranges.
  • Review dependency lockfiles and container images for vulnerable TensorFlow packages.
  • Identify code paths using `tf.raw_ops.ParameterizedTruncatedNormal` or wrappers around it.
  • Check crash telemetry for failures around this operation before and after upgrade.
  • Run existing ML application tests after upgrading TensorFlow.
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

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cve · low confidence lookup

CVE-2021-29568 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-2021-29568Attack 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-824

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

CWE-824 · source CWE mapping

Access of Uninitialized Pointer

Access of Uninitialized Pointer represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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