AVID-2026-R0734

Description

Division by 0 in Conv3DBackprop* (CVE-2021-29522)

Details

TensorFlow is an end-to-end open source platform for machine learning. The tf.raw_ops.Conv3DBackprop* operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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.

Reason for inclusion in AVID: CVE-2021-29522 describes a division-by-zero vulnerability in TensorFlow’s Conv3DBackprop operations that can cause denial of service when an attacker controls input sizes. TensorFlow is a core ML framework frequently used to build, train, deploy, and run AI systems, making this a software supply-chain issue within AI software stacks. The vulnerability is in a widely-used AI software component, with explicit CVE details and advisory references, providing sufficient evidence for classification.

References

• NVD entry
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv
• https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa

Affected or Relevant Artifacts

• Developer: tensorflow
• Deployer: tensorflow
• Artifact Details:

Impact

AVID Taxonomy Categorization

• Risk domains: Security
• SEP subcategories: S0100: Software Vulnerability
• Lifecycle stages: L06: Deployment

CVSS

CWE

Other information

• Report Type: Advisory
• Credits:
• Date Reported: 2021-05-14
• Version: 0.3.3
• AVID Entry