CVE-2021-37682: Use of unitialized value in TensorFlow Lite
First published: Thu Aug 12 2021(Updated: )
### Impact All TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200): ```cc const auto* affine_quantization = reinterpret_cast<TfLiteAffineQuantization*>( filter->quantization.params); ``` The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. ### Patches We have patched the issue in GitHub commits [537bc7c723439b9194a358f64d871dd326c18887](https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887), [4a91f2069f7145aab6ba2d8cfe41be8a110c18a5](https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5) and [8933b8a21280696ab119b63263babdb54c298538](https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
| >=2.3.0<2.3.4 | ||
| >=2.4.0<2.4.3 | ||
| =2.5.0 | ||
| =2.6.0-rc0 | ||
| =2.6.0-rc1 | ||
| =2.6.0-rc2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5
- https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887
- https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw
- https://nvd.nist.gov/vuln/detail/CVE-2021-37682
- https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200
- https://github.com/advisories/GHSA-4c4g-crqm-xrxw (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-595.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-793.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-304.yaml
Frequently Asked Questions
What is the severity of CVE-2021-37682?
CVE-2021-37682 is classified as a high severity vulnerability due to its potential to expose uninitialized values in TFLite operations using quantization.
How do I fix CVE-2021-37682?
To fix CVE-2021-37682, upgrade TensorFlow to version 2.5.1, 2.4.3, or 2.3.4 depending on the version you are currently using.
Which versions of TensorFlow are affected by CVE-2021-37682?
CVE-2021-37682 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, and various release candidates of 2.6.0.
What kind of applications are impacted by CVE-2021-37682?
Applications using TensorFlow for machine learning operations that implement quantization techniques may be impacted by CVE-2021-37682.
Is there a workaround for CVE-2021-37682 if I cannot update TensorFlow immediately?
Currently, no official workarounds are provided for CVE-2021-37682, and it is recommended to upgrade to a patched version as soon as possible.
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