Advisoriesby IbisDivision by zero in Tensorflow
Impact
The [estimator for the cost of some convolution operations](https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198) can be made to execute a division by 0:
```python
import tensorflow as tf
@tf.function
def test():
y=tf.raw_ops.AvgPoolGrad(
orig_input_shape=[1,1,1,1],
grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
ksize=[1,1,1,1],
strides=[1,1,1,0],
padding='VALID',
data_format='NCHW')
return y
test()
`
The function fails to check that the stride argument is stricly positive:
```cc
int64_t GetOutputSize(const int64_t input, const int64_t filter,
const int64_t stride, const Padding& padding) {
// Logic for calculating output shape is from GetWindowedOutputSizeVerbose()
// function in third_party/tensorflow/core/framework/common_shape_fns.cc.
if (padding == Padding::VALID) {
return (input - filter + stride) / stride;
} else { // SAME.
return (input + stride - 1) / stride;
}
}
`
Hence, the fix is to add a check for the stride argument to ensure it is valid.
Patches
We have patched the issue in GitHub commit [3218043d6d3a019756607643cf65574fbfef5d7a](https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a).
The fix will be...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.5.3 | 2.5.3 |
| >= 2.6.0, < 2.6.3 | 2.6.3 | |
| >= 2.7.0, < 2.7.1 | 2.7.1 |
Details and references
### Impact The [estimator for the cost of some convolution operations](https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198) can be made to execute a division by 0: ```python import tensorflow as tf @tf.function def test(): y=tf.raw_ops.AvgPoolGrad( orig_input_shape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', data_format='NCHW') return y test() ``` The function fails to check that the stride argument is stricly positive: ```cc int64_t GetOutputSize(const int64_t input, const int64_t filter, const int64_t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() // function in third_party/tensorflow/core/framework/common_shape_fns.cc. if (padding == Padding::VALID) { return (input - filter + stride) / stride; } else { // SAME. return (input + stride - 1) / stride; } } ``` Hence, the fix is to add a check for the stride argument to ensure it is valid. ### Patches We have patched the issue in GitHub commit [3218043d6d3a019756607643cf65574fbfef5d7a](https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
- CVSS 3.1
- CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-369
- Also known as
- BIT-tensorflow-2022-21725, CVE-2022-21725, PYSEC-2022-104, PYSEC-2022-49, PYSEC-2026-3242
- github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f
- nvd.nist.gov/vuln/detail/CVE-2022-21725
- github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-49.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-104.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198
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