PYSEC-2026-3358: Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow
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.QuantizedConv2D does not fully validate the input arguments. In this case, references get bound to nullptr for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
Affected Software
Remediation
Recommended actions to resolve this vulnerability, in priority order.
- Upgrade
Upgrade
TensorFlowto a version that resolves this vulnerability.Fixed in 2.9.0 - Upgrade
Upgrade
TensorFlowto a version that resolves this vulnerability.Fixed in 2.8.1 - Upgrade
Upgrade
TensorFlowto a version that resolves this vulnerability.Fixed in 2.7.2 - Upgrade
Upgrade
TensorFlowto a version that resolves this vulnerability.Fixed in 2.6.4
Event History
Frequently Asked Questions
Which TensorFlow releases are affected?
TensorFlow versions before 2.6.4, 2.7.2, 2.8.1, and 2.9.0 are affected. The issue is patched in those respective releases.
What does an attacker need to trigger the issue?
An attacker needs the ability to invoke tf.raw_ops.QuantizedConv2D with empty input arguments. Empty arguments can cause references to bind to nullptr, leading to undefined behavior.
What is the likely impact?
The reported impact is availability loss: the vulnerability is associated with null pointer dereference and undefined behavior. The supplied severity vector indicates local access and low privileges are required, with no confidentiality or integrity impact listed.