CVE-2021-29556: Division by 0 in `Reverse`
First published: Fri May 14 2021(Updated: )
### Impact An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`: ```python import tensorflow as tf tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32) dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool) tf.raw_ops.Reverse(tensor=tensor_input, dims=dims) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument: ```cc const int64 N = input.dim_size(0); const int64 cost_per_unit = input.NumElements() / N; ``` Since this is controlled by the user, an attacker can trigger a denial of service. ### Patches We have patched the issue in GitHub commit [4071d8e2f6c45c1955a811fee757ca2adbe462c1](https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1). 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 |
|---|---|---|
| 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/4071d8e2f6c45c1955a811fee757ca2adbe462c1
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93
- https://nvd.nist.gov/vuln/detail/CVE-2021-29556
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-484.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-682.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-193.yaml
- https://github.com/advisories/GHSA-fxqh-cfjm-fp93 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29556?
CVE-2021-29556 has a medium severity rating as it can cause a denial of service.
How do I fix CVE-2021-29556?
To fix CVE-2021-29556, upgrade TensorFlow to version 2.4.2 or later.
What versions of TensorFlow are affected by CVE-2021-29556?
CVE-2021-29556 affects TensorFlow versions prior to 2.4.2, including 2.1.4, and versions between 2.2.0 and 2.2.3, 2.3.0 and 2.3.3.
What type of vulnerability is CVE-2021-29556?
CVE-2021-29556 is a denial of service vulnerability caused by a FPE runtime error in TensorFlow.
Can CVE-2021-29556 be exploited remotely?
Yes, CVE-2021-29556 can be exploited remotely if an attacker sends crafted input to the vulnerable TensorFlow operation.
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