Heap OOB access in `Dilation2DBackpropInput`
Impact
An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to tf.raw_ops.Dilation2DBackpropInput:
```python
import tensorflow as tf
input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32)
out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32)
tf.raw_ops.Dilation2DBackpropInput(
input=input_tensor, filter=filter, out_backprop=out_backprop,
strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID')
`
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array.
```cc
in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d);
`
The values for h_out and w_out are guaranteed to be in range for out_backprop (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating h_in_max/w_in_max and in_backprop.
Patches
We have patched the issue in GitHub commit [3f6fe4dfef6f57e768260b48166c27d148f3015f](https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on...
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 An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`: ```python import tensorflow as tf input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32) filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32) out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32) tf.raw_ops.Dilation2DBackpropInput( input=input_tensor, filter=filter, out_backprop=out_backprop, strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID') ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. ```cc in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d); ``` The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. ### Patches We have patched the issue in GitHub commit [3f6fe4dfef6f57e768260b48166c27d148f3015f](https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f). 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.
- 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-787
- Also known as
- BIT-tensorflow-2021-29566, CVE-2021-29566, PYSEC-2021-203, PYSEC-2021-494, PYSEC-2021-692
- github.com/tensorflow/tensorflow/security/advisories/GHSA-pvrc-hg3f-58r6
- nvd.nist.gov/vuln/detail/CVE-2021-29566
- github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-494.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-692.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-203.yaml
- github.com/tensorflow/tensorflow