CVE-2021-29578: Heap buffer overflow in `FractionalAvgPoolGrad`
First published: Fri May 14 2021(Updated: )
### Impact The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64) out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. ### Patches We have patched the issue in GitHub commit [12c727cee857fa19be717f336943d95fca4ffe4f](https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f). 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/12c727cee857fa19be717f336943d95fca4ffe4f
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf
- https://nvd.nist.gov/vuln/detail/CVE-2021-29578
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-506.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-704.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-215.yaml
- https://github.com/advisories/GHSA-6f89-8j54-29xf (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29578?
CVE-2021-29578 has a high severity due to the potential for a heap buffer overflow, which can lead to arbitrary code execution.
How do I fix CVE-2021-29578?
To fix CVE-2021-29578, upgrade TensorFlow to version 2.4.2 or later.
What versions of TensorFlow are affected by CVE-2021-29578?
CVE-2021-29578 affects TensorFlow versions prior to 2.4.2, specifically versions 2.1.0 through 2.4.1.
Is CVE-2021-29578 a critical vulnerability for TensorFlow?
Yes, CVE-2021-29578 is considered critical due to the risk it poses to application security.
What should I do if I cannot upgrade TensorFlow to fix CVE-2021-29578?
If upgrading is not possible, review your application for vulnerabilities and implement additional security measures to mitigate risks.
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