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.
This vulnerability carries a LOW severity rating with a CVSS v3.1 score of 2.5, requiring local system access to exploit but requires specific conditions to be met without requiring user interaction requiring only low-level privileges . The vulnerability impacts and limited availability for affected systems. Impacting 1 product from google organizations running these solutions should prioritize assessment and patching.
Reported in 2021, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2021-05-14T20:15:14.293
2026-06-17T03:47:57.340
Modified
CVSSv3.1: 2.5 (LOW)
AV:L/AC:L/Au:N/C:N/I:N/A:P
3.9
2.9
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | tensorflow | < 2.1.4 | Yes | |
| Application | tensorflow | < 2.2.3 | Yes | |
| Application | tensorflow | < 2.3.3 | Yes | |
| Application | tensorflow | < 2.4.2 | Yes |
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