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CVE Record

CVE-2021-29525: Division by 0 in `Conv2DBackpropInput`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. 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
Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29525 is a low-severity TensorFlow availability bug. A permitted local user can cause a division-by-zero crash path in Conv2DBackpropInput when caller-controlled values reach that operation. It does not expose data or allow code execution based on the supplied sources.

Executive priority

Treat this as routine patching unless the organization runs multi-tenant or user-submitted ML workloads. It is a denial-of-service concern with no cited confidentiality, integrity, code execution, or active exploitation evidence.

Technical view

TensorFlow’s tf.raw_ops.Conv2DBackpropInput could divide by a caller-controlled quantity in conv_grad_input_ops.h, causing a CWE-369 division-by-zero condition. The CVSS vector is local, high complexity, low privileges, no user interaction, unchanged scope, and low availability impact only.

Likely exposure

Exposure is most likely in systems running affected TensorFlow releases: before 2.1.4, 2.2.x before 2.2.3, 2.3.x before 2.3.3, or 2.4.x before 2.4.2. Risk is higher where users can submit models, tensors, or ML jobs using raw TensorFlow operations.

Exploitation context

The source bundle does not indicate active exploitation, and the CVE is not listed as KEV. Exploitation requires local access, low privileges, high complexity, and a path to invoke the affected operation with controlled values. Expected impact is process disruption, not compromise.

Researcher notes

Focus validation on version exposure and whether untrusted callers can reach tf.raw_ops.Conv2DBackpropInput. The public record identifies the vulnerable division in TensorFlow kernel code and a fixing commit. Avoid assuming broader TensorFlow operations, remote exploitability, or data exposure without additional evidence.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
  • Inventory lockfiles, containers, notebooks, and ML worker images for affected TensorFlow versions.
  • Restrict untrusted users from submitting arbitrary TensorFlow jobs until patched.
  • Check TensorFlow advisory guidance for unsupported or vendor-embedded distributions.

Validation and detection

  • Confirm deployed TensorFlow versions are not in the affected ranges.
  • Review dependency manifests and runtime images for transitive TensorFlow installations.
  • Identify services exposing user-controlled model execution or tensor operation parameters.
  • Check application logs for unexplained TensorFlow worker crashes around convolution gradient input operations.
  • Verify patched versions are present after rebuilds and redeployments.
Prepared
Confidence
high
Sources
4

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-369: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29525 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-2021-29525Attack 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
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 links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-369 · source CWE mapping

CWE mapping pending import

This CVE carries a CWE mapping that will resolve to a full Glexia CWE intelligence page after the official CWE import is complete.