Most recent entries from all

BIT-tensorflow-2021-29538 — Division by zero in `Conv2DBackpropFilter`

Affected: Bitnami: tensorflow

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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.

cnvd-2021-37616

EUVD-2026-26408

fkie_cve-2021-29538

GHSA-j8qc-5fqr-52fp — Division by zero in `Conv2DBackpropFilter`

Affected: PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu

### Impact An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:

```python import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=False, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):

```cc const size_t size_A = output_image_size * filter_total_size; const size_t size_B = output_image_size * dims.out_depth; const size_t size_C = filter_total_size * dims.out_depth; const size_t work_unit_size = size_A + size_B + size_C; const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size; ```

If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.

### Patches We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21…

gsd-2021-29538

PYSEC-2021-175

Affected: PyPI: tensorflow