CVE-2021-29595: Division by zero in TFLite's implementation of `DepthToSpace`
First published: Fri May 14 2021(Updated: )
### Impact The implementation of the `DepthToSpace` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69): ```cc const int block_size = params->block_size; ... const int input_channels = input->dims->data[3]; ... int output_channels = input_channels / block_size / block_size; ``` An attacker can craft a model such that `params->block_size` is 0. ### Patches We have patched the issue in GitHub commit [106d8f4fb89335a2c52d7c895b7a7485465ca8d9](https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9). 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 members of the Aivul Team from Qihoo 360.
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/106d8f4fb89335a2c52d7c895b7a7485465ca8d9
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vf94-36g5-69v8
- https://nvd.nist.gov/vuln/detail/CVE-2021-29595
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-523.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-721.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-232.yaml
- https://github.com/advisories/GHSA-vf94-36g5-69v8 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29595?
CVE-2021-29595 has a severity rating of Medium due to its potential for causing a division by zero error.
How do I fix CVE-2021-29595?
You can address CVE-2021-29595 by upgrading TensorFlow to version 2.4.2 or higher.
Which versions of TensorFlow are affected by CVE-2021-29595?
CVE-2021-29595 affects TensorFlow versions prior to 2.1.4, and between 2.2.0 and 2.2.3, 2.3.0 and 2.3.3, and 2.4.0 and 2.4.2.
Is CVE-2021-29595 present in TensorFlow GPU installations?
Yes, CVE-2021-29595 affects both TensorFlow CPU and GPU installations.
What should developers do about CVE-2021-29595?
Developers should review their applications for dependencies on vulnerable TensorFlow versions and consider upgrading.
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