Описание
Integer overflow in Tensorflow
Impact
The implementation of OpLevelCostEstimator::CalculateOutputSize is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements:
Here, we can have a large enough number of dimensions in output_shape.dim() or just a small number of dimensions being large enough to cause an overflow in the multiplication.
Patches
We have patched the issue in GitHub commit b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae.
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 for more information regarding the security model and how to contact us with issues and questions.
Ссылки
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wm93-f238-7v37
- https://nvd.nist.gov/vuln/detail/CVE-2022-23576
- https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-85.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-140.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617
Пакеты
tensorflow
< 2.5.3
2.5.3
tensorflow
>= 2.6.0, < 2.6.3
2.6.3
tensorflow
= 2.7.0
2.7.1
tensorflow-cpu
< 2.5.3
2.5.3
tensorflow-cpu
>= 2.6.0, < 2.6.3
2.6.3
tensorflow-cpu
= 2.7.0
2.7.1
tensorflow-gpu
< 2.5.3
2.5.3
tensorflow-gpu
>= 2.6.0, < 2.6.3
2.6.3
tensorflow-gpu
= 2.7.0
2.7.1
Связанные уязвимости
Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. 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.
Tensorflow is an Open Source Machine Learning Framework. The implement ...