CVE-2021-29577: Heap buffer overflow in `AvgPool3DGrad`
First published: Fri May 14 2021(Updated: )
### Impact The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32) grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" tf.raw_ops.AvgPool3DGrad( orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. ### Patches We have patched the issue in GitHub commit [6fc9141f42f6a72180ecd24021c3e6b36165fe0d](https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d). 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. ### 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 Ying Wang and Yakun Zhang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <2.1.4 | |
| Google TensorFlow | >=2.2.0<2.2.3 | |
| Google TensorFlow | >=2.3.0<2.3.3 | |
| Google TensorFlow | >=2.4.0<2.4.2 | |
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm
- https://nvd.nist.gov/vuln/detail/CVE-2021-29577
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-505.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-703.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-214.yaml
- https://github.com/advisories/GHSA-v6r6-84gr-92rm (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29577?
CVE-2021-29577 has been classified with high severity due to the potential for heap buffer overflow exploitation.
What software is affected by CVE-2021-29577?
CVE-2021-29577 affects multiple versions of Google TensorFlow up to and including version 2.4.1.
How do I fix CVE-2021-29577?
To mitigate CVE-2021-29577, upgrade TensorFlow to version 2.4.2 or later.
What type of vulnerability is CVE-2021-29577?
CVE-2021-29577 is a heap buffer overflow vulnerability that can lead to memory corruption.
Can CVE-2021-29577 be exploited remotely?
Yes, CVE-2021-29577 can be exploited remotely if the vulnerable TensorFlow application is exposed to untrusted data.
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