GHSA-gwcx-jrx4-92w2
Segfault in `simplifyBroadcast` in Tensorflow
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | tensorflow | >= 2.8.0-rc0, < 2.8.0 | 2.8.0 |
| PyPI | tensorflow-cpu | >= 2.8.0-rc0, < 2.8.0 | 2.8.0 |
| PyPI | tensorflow-gpu | >= 2.8.0-rc0, < 2.8.0 | 2.8.0 |
Details
As written in the advisory (Markdown source, shown as plain text).
### Impact
The [`simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205) is vulnerable to a segfault (hence, denial of service), if called with scalar shapes.
```cc
size_t maxRank = 0;
for (auto shape : llvm::enumerate(shapes)) {
auto found_shape = analysis.dimensionsForShapeTensor(shape.value());
if (!found_shape) return {};
shapes_found.push_back(*found_shape);
maxRank = std::max(maxRank, found_shape->size());
}
SmallVector<const ShapeComponentAnalysis::SymbolicDimension*>
joined_dimensions(maxRank);
```
If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`.
### Patches
We have patched the issue in GitHub commit [35f0fabb4c178253a964d7aabdbb15c6a398b69a](https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a).
The fix will be included in TensorFlow 2.8.0. This is the only affected version.
### 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.
References
External links from the advisory. They lead to third-party sites.
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2
- https://nvd.nist.gov/vuln/detail/CVE-2022-23593
- https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-102.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-157.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt…