Advisoriesby IbisTensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`
Impact
When tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_4=8
arg_5=False
arg_6=None
tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0,
inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4,
narrow_range=arg_5, name=arg_6)
`
Patches
We have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed).
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...
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 When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_4=8 arg_5=False arg_6=None tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0, inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6) ``` ### Patches We have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed). 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 - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology - 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-2022-35990, CVE-2022-35990, PYSEC-2026-1006, PYSEC-2026-3199, PYSEC-2026-3338
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