| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-cpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-gpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
The TFLite computation for size of output after padding, ComputeOutSize, does not check that the stride argument is not 0 before doing the division.
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.
We have patched the issue in GitHub 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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
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