First published: Thu Aug 12 2021(Updated: )
### Impact An attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`: ```python import tensorflow as tf v = tf.Variable([b'vvv']) tf.raw_ops.ResourceScatterUpdate( resource=v.handle, indices=[0], updates=['1', '2', '3', '4', '5']) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. ### Patches We have patched the issue in GitHub commit [01cff3f986259d661103412a20745928c727326f](https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f). 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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Credit: security-advisories@github.com security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
pip/tensorflow | =2.5.0 | 2.5.1 |
pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
pip/tensorflow | <2.3.4 | 2.3.4 |
TensorFlow Keras | >=2.3.0<2.3.4 | |
TensorFlow Keras | >=2.4.0<2.4.3 | |
TensorFlow Keras | =2.5.0 | |
TensorFlow Keras | =2.6.0-rc0 | |
TensorFlow Keras | =2.6.0-rc1 | |
TensorFlow Keras | =2.6.0-rc2 |
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CVE-2021-37655 is classified as a high severity vulnerability due to its potential to allow attackers to read beyond the bounds of heap allocated data.
To fix CVE-2021-37655, you should upgrade to TensorFlow version 2.5.1 or later, or 2.4.3 if using an earlier version.
CVE-2021-37655 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0.
CVE-2021-37655 is a heap data exposure vulnerability that can be exploited by providing invalid arguments to the ResourceScatterUpdate function.
As of now, there is no publicly known exploit for CVE-2021-37655, but its high severity indicates a significant risk if left unpatched.