CVE-2021-29573: 
Python vulnerability analysis and mitigation

A division by zero vulnerability was identified in TensorFlow's implementation of tf.raw_ops.MaxPoolGradWithArgmax function. The vulnerability, tracked as CVE-2021-29573, was discovered by Ying Wang and Yakun Zhang of Baidu X-Team and disclosed on May 13, 2021. The issue affects TensorFlow versions prior to 2.5.0, specifically impacting the pip packages tensorflow, tensorflow-cpu, and tensorflow-gpu (GitHub Advisory).

The vulnerability occurs when the implementation fails to validate that the batch dimension of the tensor is non-zero before performing division operations. This can be triggered when processing empty tensors with specific shapes. A proof of concept involves using empty tensors with shape [0, 0, 0, 0] as input parameters to the MaxPoolGradWithArgmax operation (GitHub Advisory).

The vulnerability has been classified as Low severity. When exploited, it causes a division by zero error in the TensorFlow operations, potentially leading to program crashes or unexpected behavior (GitHub Advisory).

The vulnerability can be triggered through the tf.raw_ops.MaxPoolGradWithArgmax function by providing specific empty tensor inputs. A proof of concept exploit has been documented that demonstrates the issue using empty tensors with shape [0, 0, 0, 0] and specific parameters for ksize and strides (GitHub Advisory).

The vulnerability has been patched in TensorFlow version 2.5.0 and backported to versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. The fix involves adding a check to prevent processing when the output tensor is null, implemented in commit 376c352a37ce5a68b721406dc7e77ac4b6cf483d (GitHub Advisory, GitHub Commit).