PYSEC-2026-3214: Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`

Q: What access does an attacker need to trigger the denial of service?

The issue has local attack vector and low privileges required. An attacker must be able to invoke tf.raw_ops.SparseTensorToCSRSparseMatrix with malformed sparse-tensor inputs.

Q: Which inputs cause the failure?

The operation does not validate that dense_shape is a vector or that indices is a matrix. Supplying inputs that violate these expected shapes can trigger a CHECK failure and terminate the affected process.

Q: Which TensorFlow releases fix the issue?

The issue is fixed in TensorFlow 2.9.0, 2.8.1, 2.7.2, and 2.6.4. Versions earlier than the applicable fixed release are affected.

Published May 20, 2022

Updated

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.rawops.SparseTensorToCSRSparseMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes denseshape is a vector and indices is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Affected Software

1 affected component

Google TensorFlow<2.9.0, <2.8.1, <2.7.2, <2.6.4

Remediation

Recommended actions to resolve this vulnerability, in priority order.

Upgrade
Upgrade TensorFlow to a version that resolves this vulnerability.
Fixed in 2.9.0

Event History

May 20, 2022

CVE Published

via OSV·09:50 PM

Data Sourced

via OSV·09:50 PM

DescriptionSeverityWeakness

Frequently Asked Questions

What access does an attacker need to trigger the denial of service?

The issue has local attack vector and low privileges required. An attacker must be able to invoke tf.raw_ops.SparseTensorToCSRSparseMatrix with malformed sparse-tensor inputs.

Which inputs cause the failure?

The operation does not validate that dense_shape is a vector or that indices is a matrix. Supplying inputs that violate these expected shapes can trigger a CHECK failure and terminate the affected process.

Which TensorFlow releases fix the issue?

The issue is fixed in TensorFlow 2.9.0, 2.8.1, 2.7.2, and 2.6.4. Versions earlier than the applicable fixed release are affected.

PYSEC-2026-3214: Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`

Affected Software

Remediation

Event History

Frequently Asked Questions

What access does an attacker need to trigger the denial of service?

Which inputs cause the failure?

Which TensorFlow releases fix the issue?

Company

Resources

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Frequently Asked Questions

What access does an attacker need to trigger the denial of service?

Which inputs cause the failure?

Which TensorFlow releases fix the issue?