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CVE Record

CVE-2021-29569: Heap out of bounds read in `RequantizationRange`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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-2021-29569 is a low-severity TensorFlow flaw where specially crafted local inputs can make TensorFlow read outside heap-allocated memory. The documented CVSS impact is limited availability loss, with no confidentiality or integrity impact. Business urgency is higher only where untrusted users can run TensorFlow workloads.

Executive priority

Low priority for most organizations, but schedule remediation through normal dependency maintenance. Prioritize faster where shared ML platforms allow low-privileged users to run TensorFlow workloads, because the issue could affect service stability.

Technical view

The issue is CWE-125 in TensorFlow RequantizationRange logic: code assumes input_min and input_max have at least one element and reads element zero. Empty tensors make that read out of bounds. Sources also mention MaxPoolGradWithArgmax, so the public description contains an operation-name inconsistency.

Likely exposure

Affected installations are TensorFlow versions <2.1.4, >=2.2.0 <2.2.3, >=2.3.0 <2.3.3, and >=2.4.0 <2.4.2. Exposure is most relevant in ML notebooks, batch jobs, or services accepting untrusted tensors or user-submitted workloads.

Exploitation context

The source bundle does not show CISA KEV listing or active exploitation evidence. CVSS requires local access, low privileges, high attack complexity, and no user interaction. Treat this as a hardening and dependency-update issue unless untrusted local users share TensorFlow execution environments.

Researcher notes

Evidence supports an out-of-bounds heap read caused by empty input_min or input_max tensors. The bundle’s description names MaxPoolGradWithArgmax while the title and linked code reference RequantizationRange; preserve that uncertainty when tracking affected call paths.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
  • Restrict untrusted users from submitting arbitrary TensorFlow operations to shared runtimes.
  • Check TensorFlow advisory guidance before relying on local workarounds.

Validation and detection

  • Inventory TensorFlow package versions across production, notebooks, CI, and ML images.
  • Confirm affected version ranges are absent from dependency lockfiles and containers.
  • Verify workloads accepting user tensors are isolated from sensitive shared services.
  • Review update evidence against the referenced TensorFlow advisory and commit.
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

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-125: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29569 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-2021-29569Attack 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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