LiveActive security incident?Get immediate response
CVE Record

CVE-2021-29538: Division by zero in `Conv2DBackpropFilter`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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
Discuss exposureBack to CVEs
Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

This TensorFlow flaw can crash affected ML workloads when specially shaped inputs reach Conv2DBackpropFilter. The business impact is limited to availability: no data theft or integrity change is described. It matters most for shared ML environments or services that accept user-controlled tensors, models, or training jobs.

Executive priority

Treat this as routine patching unless TensorFlow is exposed through shared or user-programmable ML infrastructure. It is not described as remotely exploitable or actively exploited, but it can still disrupt vulnerable ML jobs where untrusted users control workload inputs.

Technical view

CVE-2021-29538 is a CWE-369 division-by-zero in TensorFlow Conv2DBackpropFilter. The divisor is derived from tensor shapes; if all shapes are empty, work_unit_size becomes zero and a runtime exception can occur. CVSS 3.1 is 2.5, with local access, low privileges, high complexity, and low availability impact.

Likely exposure

Exposure is likely limited to TensorFlow deployments on affected versions where users or application paths can influence tensor shapes reaching this kernel. Higher-risk contexts include shared notebooks, managed ML platforms, or user-submitted training workloads. Closed systems using trusted models and inputs are less exposed.

Exploitation context

The source bundle marks KEV false and provides no evidence of active exploitation. CVSS indicates local access, low privileges, high attack complexity, and no user interaction. Practical abuse is limited to denial of service through inputs that trigger the vulnerable runtime path.

Researcher notes

The evidence supports a narrow denial-of-service issue in TensorFlow’s kernel implementation. Do not infer broader product impact beyond TensorFlow versions listed in the advisory. The fix is tied to the referenced commit and release/cherrypick versions named by TensorFlow.

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.
  • Check the TensorFlow advisory before selecting a remediation version.
  • Restrict untrusted model, notebook, and tensor submission paths until patched.
  • Monitor ML worker crashes consistent with TensorFlow runtime exceptions.

Validation and detection

  • Inventory deployed TensorFlow versions across services, notebooks, images, and training workers.
  • Flag versions before 2.1.4, 2.2.3, 2.3.3, and 2.4.2 as affected.
  • Confirm whether Conv2DBackpropFilter is reachable from user-controlled workloads.
  • Verify patched images and environments are actually running after upgrade.
  • Review crash logs for recurring Conv2DBackpropFilter runtime exceptions.
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

Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.

cwe · low confidence lookup

CWE-369: Exact CWE lookup

Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.

Open ATT&CK lookup
cve · low confidence lookup

CVE-2021-29538 mapping review

Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.

Open ATT&CK lookup
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-2021-29538Attack 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-369

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

CWE-369 · source CWE mapping

Divide By Zero

Divide By Zero represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

Open CWE intelligenceSame-CWE CVEs