CVE-2021-29576: Heap buffer overflow in `MaxPool3DGradGrad`
First published: Fri May 14 2021(Updated: )
### Impact The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf values = [0.01] * 11 orig_input = tf.constant(values, shape=[11, 1, 1, 1, 1], dtype=tf.float32) orig_output = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32) grad = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" tf.raw_ops.MaxPool3DGradGrad( orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize, strides=strides, padding=padding) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully: ```cc Pool3dParameters params{context, ksize_, stride_, padding_, data_format_, tensor_in.shape()}; ``` Since [the constructor](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. ### Patches We have patched the issue in GitHub commit [63c6a29d0f2d692b247f7bf81f8732d6442fad09](https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09). 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/63c6a29d0f2d692b247f7bf81f8732d6442fad09
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh
- https://nvd.nist.gov/vuln/detail/CVE-2021-29576
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-504.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-702.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-213.yaml
- https://github.com/advisories/GHSA-7cqx-92hp-x6wh (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29576?
CVE-2021-29576 has a medium severity rating due to the potential for a heap buffer overflow.
How do I fix CVE-2021-29576?
To fix CVE-2021-29576, update your TensorFlow installation to version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29576?
CVE-2021-29576 affects TensorFlow versions from 2.1.0 up to, but not including, 2.4.2.
What component of TensorFlow is impacted by CVE-2021-29576?
The MaxPool3DGradGrad operation is the specific component in TensorFlow impacted by CVE-2021-29576.
Is CVE-2021-29576 a remotely exploitable vulnerability?
CVE-2021-29576 may not be remotely exploitable as it typically requires local execution of specific TensorFlow operations.
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