GHSA-mv78-g7wq-mhp4

Dashboard / Vulnerabilities / GHSA-mv78-g7wq-mhp4

GHSA-mv78-g7wq-mhp4

Summary: Division by zero in padding computation in TFLite

Details: ### Impact The TFLite computation for size of output after padding, [`ComputeOutSize`](https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division. ```cc inline int ComputeOutSize(TfLitePadding padding, int image_size, int filter_size, int stride, int dilation_rate = 1) { int effective_filter_size = (filter_size - 1) * dilation_rate + 1; switch (padding) { case kTfLitePaddingSame: return (image_size + stride - 1) / stride; case kTfLitePaddingValid: return (image_size + stride - effective_filter_size) / stride; default: return 0; } } ``` Users can craft special models such that `ComputeOutSize` is called with `stride` set to 0. ### Patches We have patched the issue in GitHub commit [49847ae69a4e1a97ae7f2db5e217c77721e37948](https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948). 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 members of the Aivul Team from Qihoo 360.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv78-g7wq-mhp4, https://nvd.nist.gov/vuln/detail/CVE-2021-29585, https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-513.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-711.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-222.yaml, https://github.com/tensorflow/tensorflow, https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55