Advisoriesby IbisHeap buffer overflow in `AvgPool3DGrad`
Impact
The implementation of tf.raw_ops.AvgPool3DGrad is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)
grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.AvgPool3DGrad(
orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,
padding=padding)
`
The [implementation](https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the orig_input_shape and grad tensors have similar first and last dimensions but does not check that this assumption is validated.
Patches
We have patched the issue in GitHub commit [6fc9141f42f6a72180ecd24021c3e6b36165fe0d](https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d).
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...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.1.4 | 2.1.4 |
| >= 2.2.0, < 2.2.3 | 2.2.3 | |
| >= 2.3.0, < 2.3.3 | 2.3.3 | |
| >= 2.4.0, < 2.4.2 | 2.4.2 |
Details and references
### Impact The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32) grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" tf.raw_ops.AvgPool3DGrad( orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. ### Patches We have patched the issue in GitHub commit [6fc9141f42f6a72180ecd24021c3e6b36165fe0d](https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d). 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.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-119, CWE-787
- Also known as
- BIT-tensorflow-2021-29577, CVE-2021-29577, PYSEC-2021-214, PYSEC-2021-505, PYSEC-2021-703
- github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm
- nvd.nist.gov/vuln/detail/CVE-2021-29577
- github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-505.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-703.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-214.yaml
- github.com/tensorflow/tensorflow
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