CVE-2021-29556

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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.

Impact

An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse:

import tensorflow as tf

tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)
dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)

tf.raw_ops.Reverse(tensor=tensor_input, dims=dims)

This is because the implementation performs a division based on the first dimension of the tensor argument:

const int64 N = input.dim_size(0);
const int64 cost_per_unit = input.NumElements() / N;

Since this is controlled by the user, an attacker can trigger a denial of service.

Patches

We have patched the issue in GitHub commit 4071d8e2f6c45c1955a811fee757ca2adbe462c1.

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 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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.

Impact

An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse:

import tensorflow as tf

tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)
dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)

tf.raw_ops.Reverse(tensor=tensor_input, dims=dims)

This is because the implementation performs a division based on the first dimension of the tensor argument:

const int64 N = input.dim_size(0);
const int64 cost_per_unit = input.NumElements() / N;

Since this is controlled by the user, an attacker can trigger a denial of service.

Patches

We have patched the issue in GitHub commit 4071d8e2f6c45c1955a811fee757ca2adbe462c1.

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 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.