CVE-2021-29580
Description
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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.
How to fix
- google tensorflow2.1.4, 2.2.3, 2.3.3, 2.4.2 or later
- tensorflow2.1.4, 2.2.3, 2.3.3, 2.4.2 or later
- tensorflow-cpu2.1.4, 2.2.3, 2.3.3, 2.4.2 or later
- tensorflow-gpu2.1.4, 2.2.3, 2.3.3, 2.4.2 or later
Affected products
| Product | Affected versions | Fixed in |
|---|---|---|
| google tensorflow | v < 2.1.4, 2.2.0 ≤ v < 2.2.3, 2.3.0 ≤ v < 2.3.3, 2.4.0 ≤ v < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| tensorflowPyPI | v < 2.1.4, 2.2.0 ≤ v < 2.2.3, 2.3.0 ≤ v < 2.3.3, 2.4.0 ≤ v < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| tensorflow-cpuPyPI | v < 2.1.4, 2.2.0 ≤ v < 2.2.3, 2.3.0 ≤ v < 2.3.3, 2.4.0 ≤ v < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| tensorflow-gpuPyPI | v < 2.1.4, 2.2.0 ≤ v < 2.2.3, 2.3.0 ≤ v < 2.3.3, 2.4.0 ≤ v < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
Check your version
Choose the product and enter the version you run to see whether it falls in the affected range.
Attack Surface Management
See the assets tied to your domains, the technologies they run and known vulnerabilities in one list.