| Package | Affected versions | Patched versions |
|---|---|---|
| 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 |
| tensorflow-cpu | ≥ 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 |
| tensorflow-gpu | ≥ 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 |
| 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 |
A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of ArgMin/ArgMax:
TfLiteIntArray* output_dims = TfLiteIntArrayCreate(NumDimensions(input) - 1);
int j = 0;
for (int i = 0; i < NumDimensions(input); ++i) {
if (i != axis_value) {
output_dims->data[j] = SizeOfDimension(input, i);
++j;
}
}
If axis_value is not a value between 0 and NumDimensions(input), then the condition in the if is never true, so code writes past the last valid element of output_dims->data.
We have patched the issue in GitHub commit c59c37e7b2d563967da813fa50fe20b21f4da683.
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.
Running one of these products? Scan your site to see whether it exposes a vulnerable version.
Scan my site free