CVE-2021-29522: Division by 0 in `Conv3DBackprop*`
First published: Fri May 14 2021(Updated: )
### Impact The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0: ```python import tensorflow as tf input_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` ```python import tensorflow as tf input_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32) filter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32) out_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32) tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero: ```cc const int64 size_A = output_image_size * dims.out_depth; const int64 size_B = filter_total_size * dims.out_depth; const int64 size_C = output_image_size * filter_total_size; const int64 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; ``` Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. ### Patches We have patched the issue in GitHub commit [311403edbc9816df80274bd1ea8b3c0c0f22c3fa](https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa). 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.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <2.1.4 | |
| Google TensorFlow | >=2.2.0<2.2.3 | |
| Google TensorFlow | >=2.3.0<2.3.3 | |
| Google TensorFlow | >=2.4.0<2.4.2 | |
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv
- https://nvd.nist.gov/vuln/detail/CVE-2021-29522
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-450.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-648.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-159.yaml
- https://github.com/advisories/GHSA-c968-pq7h-7fxv (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29522?
CVE-2021-29522 has a medium severity due to the potential for division by zero when the input tensors are empty.
How do I fix CVE-2021-29522?
To fix CVE-2021-29522, update TensorFlow to version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29522?
CVE-2021-29522 affects TensorFlow versions prior to 2.1.4, as well as versions 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2.
What operations are impacted by CVE-2021-29522?
The operations impacted by CVE-2021-29522 are the tf.raw_ops.Conv3DBackprop* operations.
What happens if I use an empty input tensor with CVE-2021-29522?
Using an empty input tensor with CVE-2021-29522 will result in a division by zero error.
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