CVE-2021-37682
Use of unitialized value in TFLite
Impact
### 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.
Affected packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| pip | tensorflow | < 2.3.4 | 2.3.4 |
| pip | tensorflow-cpu | < 2.3.4 | 2.3.4 |
| pip | tensorflow-gpu | < 2.3.4 | 2.3.4 |
Affected products
| Vendor | Product | Affected versions |
|---|---|---|
| Tensorflow | from 2.3.0 before 2.3.4 from 2.4.0 before 2.4.3 Versions 2.5.0, 2.6.0 |
Weakness classification
References
- NVD record
- CVE.org record
- GitHub advisory
- 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://github.com/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/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