CVE-2021-29588: Division by zero in TFLite's implementation of `TransposeConv`
First published: Fri May 14 2021(Updated: )
### Impact The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222): ```cc int height_col = (height + pad_t + pad_b - filter_h) / stride_h + 1; int width_col = (width + pad_l + pad_r - filter_w) / stride_w + 1; ``` An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. ### Patches We have patched the issue in GitHub commit [801c1c6be5324219689c98e1bd3e0ca365ee834d](https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d). 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/801c1c6be5324219689c98e1bd3e0ca365ee834d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9
- https://nvd.nist.gov/vuln/detail/CVE-2021-29588
- https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222
- https://github.com/advisories/GHSA-vfr4-x8j2-3rf9 (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-516.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-714.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-225.yaml
Frequently Asked Questions
What is the impact of CVE-2021-29588?
CVE-2021-29588 is a vulnerability in the TransposeConv TFLite operator that can lead to a division by zero error.
What versions are affected by CVE-2021-29588?
CVE-2021-29588 affects TensorFlow versions 2.4.0 to 2.4.2, 2.3.0 to 2.3.3, 2.2.0 to 2.2.3, and 2.1.0 to 2.1.4 for both tensorflow-gpu and tensorflow-cpu.
How do I fix CVE-2021-29588?
To mitigate CVE-2021-29588, upgrade TensorFlow to version 2.4.2 or later.
What is the severity of CVE-2021-29588?
CVE-2021-29588 is classified as a moderate severity vulnerability due to the potential for runtime errors but limited impact.
Is there a workaround for CVE-2021-29588?
There are no official workarounds for CVE-2021-29588, so the best course of action is to upgrade to a patched version.
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