CVE-2022-23580: Abort caused by allocating a vector that is too large in Tensorflow
First published: Fri Feb 04 2022(Updated: )
### Impact During shape inference, TensorFlow can [allocate a large vector](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790) based on a value from a tensor controlled by the user: ```cc const auto num_dims = Value(shape_dim); std::vector<DimensionHandle> dims; dims.reserve(num_dims); ``` ### Patches We have patched the issue in GitHub commit [1361fb7e29449629e1df94d44e0427ebec8c83c7](https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <=2.5.2 | |
| Google TensorFlow | >=2.6.0<=2.6.2 | |
| Google TensorFlow | =2.7.0 | |
| pip/tensorflow-gpu | =2.7.0 | 2.7.1 |
| pip/tensorflow-cpu | =2.7.0 | 2.7.1 |
| pip/tensorflow | =2.7.0 | 2.7.1 |
| pip/tensorflow-gpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-gpu | <2.5.3 | 2.5.3 |
| pip/tensorflow-cpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-cpu | <2.5.3 | 2.5.3 |
| pip/tensorflow | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow | <2.5.3 | 2.5.3 |
| <=2.5.2 | ||
| >=2.6.0<=2.6.2 | ||
| =2.7.0 |
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Reference Links
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790
- https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7
- https://nvd.nist.gov/vuln/detail/CVE-2022-23580
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-89.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-144.yaml
- https://github.com/advisories/GHSA-627q-g293-49q7 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-23580?
CVE-2022-23580 has a severity rating of medium due to its potential for a denial-of-service attack through resource exhaustion.
How do I fix CVE-2022-23580?
To remediate CVE-2022-23580, upgrade TensorFlow to version 2.7.1 or later.
Which versions of TensorFlow are affected by CVE-2022-23580?
Versions of TensorFlow up to and including 2.7.0 are affected by CVE-2022-23580.
What is the impact of CVE-2022-23580?
CVE-2022-23580 can lead to excessive memory allocation during shape inference, causing potential denial of service.
How can I determine if my software is vulnerable to CVE-2022-23580?
Check the version of TensorFlow in use; if it is below 2.7.1, it is vulnerable to CVE-2022-23580.
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