| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow-cpu | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow-gpu | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow | = 2.5.0= 2.6.0≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3 | 2.3.42.4.3 |
The code for tf.raw_ops.SaveV2 does not properly validate the inputs and an attacker can trigger a null pointer dereference:
import tensorflow as tf
tf.raw_ops.SaveV2(
prefix=['tensorflow'],
tensor_name=['v'],
shape_and_slices=[],
tensors=[1,2,3])
The implementation uses ValidateInputs to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above.
However, the validation uses OP_REQUIRES which translates to setting the Status object of the current OpKernelContext to an error status, followed by an empty return statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in Compute that follows the call to ValidateInputs. This is equivalent to lacking the validation.
We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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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