CVE-2021-29594: Division by zero in TFLite's convolution code
First published: Fri May 14 2021(Updated: )
### Impact TFLite's [convolution code](https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-zero. For example: ```cc const int input_size = NumElements(input) / SizeOfDimension(input, 0); ``` ### Patches We have patched the issue in GitHub commit [ff489d95a9006be080ad14feb378f2b4dac35552](https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552). 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/ff489d95a9006be080ad14feb378f2b4dac35552
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qgw-p4fm-x7gf
- https://nvd.nist.gov/vuln/detail/CVE-2021-29594
- https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc
- https://github.com/advisories/GHSA-3qgw-p4fm-x7gf (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-522.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-720.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-231.yaml
Frequently Asked Questions
What is the severity of CVE-2021-29594?
The severity of CVE-2021-29594 is classified as medium.
How do I fix CVE-2021-29594?
To fix CVE-2021-29594, update TensorFlow to version 2.4.2 or later.
What versions of TensorFlow are affected by CVE-2021-29594?
CVE-2021-29594 affects TensorFlow versions prior to 2.1.4 and between 2.2.0 and 2.4.2.
What is the nature of the vulnerability in CVE-2021-29594?
CVE-2021-29594 is related to improper checks in the convolution code allowing division by zero.
Can CVE-2021-29594 be exploited remotely?
Yes, CVE-2021-29594 can potentially be exploited remotely through specially crafted inputs.
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