AVID-2026-R0784

Description

Undefined behavior and CHECK-fail in FractionalMaxPoolGrad (CVE-2021-29580)

Details

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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: The CVE describes a vulnerability in TensorFlow (an ML framework) related to FractionalMaxPoolGrad causing undefined behavior and potential denial of service. TensorFlow is a core component used to build, train, deploy, and run AI systems, so this is a software supply-chain issue affecting AI software stacks. The report includes specifics about vulnerable code paths and impacts, satisfying the security/signature criteria for AVID curation.

References

• NVD entry
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp
• https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925

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