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CVE Record

CVE-2021-29570: Heap out of bounds read in `MaxPoolGradWithArgmax`

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/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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-2021-29570 is a low-severity TensorFlow memory safety issue. A user with local privileges could provide crafted inputs to a specific max-pooling gradient operation and trigger an out-of-bounds heap read, mainly affecting availability rather than data confidentiality or integrity.

Executive priority

Treat this as routine patch management unless TensorFlow is exposed through shared or user-programmable ML platforms. It is low severity, but affected supported branches have vendor fixes and should be updated during normal maintenance.

Technical view

TensorFlow's tf.raw_ops.MaxPoolGradWithArgmax used one value to index two arrays without proving both arrays had matching sizes. This can cause a heap out-of-bounds read. The issue is classified as CWE-125 with CVSS 3.1 score 2.5, requiring local access, low privileges, and high attack complexity.

Likely exposure

Exposure is most relevant where affected TensorFlow versions run workloads that let untrusted or semi-trusted users influence operation inputs. Shared ML notebooks, training platforms, or model execution services are higher concern than isolated trusted research environments.

Exploitation context

The provided sources do not show active exploitation, and the CVE is not listed as KEV. Exploitation requires local access, low privileges, and specially crafted inputs. The documented impact is low availability impact, with no stated confidentiality or integrity impact.

Researcher notes

The root issue is mismatched array-size assumptions in MaxPoolGradWithArgmax indexing. The vendor advisory names affected TensorFlow ranges and the planned fixed releases. Evidence is limited to TensorFlow; the provided sources do not establish downstream product impact or exploitation in the wild.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or later where feasible.
  • Use patched supported branches: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Prioritize systems executing TensorFlow workloads from untrusted users or tenants.
  • Restrict who can submit arbitrary TensorFlow graphs, models, or tensor inputs.
  • If upgrade is blocked, monitor TensorFlow vendor guidance for supported mitigations.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, containers, and training images.
  • Check dependency lockfiles for affected TensorFlow version ranges.
  • Review whether tf.raw_ops.MaxPoolGradWithArgmax is reachable from user-controlled workflows.
  • Confirm production images use patched TensorFlow builds.
  • Run regression 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

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-2021-29570 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-2021-29570Attack 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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