Headline
GHSA-h48f-q7rw-hvr7: Missing validation causes denial of service via `StagePeek`
Impact
The implementation of tf.raw_ops.StagePeek does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
index = tf.constant([], shape=[0], dtype=tf.int32)
tf.raw_ops.StagePeek(index=index, dtypes=[tf.int32])
The code assumes index is a scalar but there is no validation for this before accessing its value:
std::size_t index = ctx->input(0).scalar<int>()();
Patches
We have patched the issue in GitHub commit cebe3c45d76357d201c65bdbbf0dbe6e8a63bbdb.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
Impact
The implementation of tf.raw_ops.StagePeek does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
index = tf.constant([], shape=[0], dtype=tf.int32) tf.raw_ops.StagePeek(index=index, dtypes=[tf.int32])
The code assumes index is a scalar but there is no validation for this before accessing its value:
std::size_t index = ctx->input(0).scalar<int>()();
Patches
We have patched the issue in GitHub commit cebe3c45d76357d201c65bdbbf0dbe6e8a63bbdb.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
References
- GHSA-h48f-q7rw-hvr7
- https://nvd.nist.gov/vuln/detail/CVE-2022-29195
- tensorflow/tensorflow@cebe3c4
- https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/stage_op.cc#L26
- https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0
Related news
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.StagePeek` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `index` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.