CVE-2022-23575: Integer overflow in Tensorflow
First published: Fri Feb 04 2022(Updated: )
### Impact The [implementation of `OpLevelCostEstimator::CalculateTensorSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558) is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements: ```cc int64_t OpLevelCostEstimator::CalculateTensorSize( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) { int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes); int size = DataTypeSize(BaseType(tensor.dtype())); VLOG(2) << "Count: " << count << " DataTypeSize: " << size; return count * size; } ``` Here, `count` and `size` can be large enough to cause `count * size` to overflow. ### Patches We have patched the issue in GitHub commit [fcd18ce3101f245b083b30655c27b239dc72221e](https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e). 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 |
|---|---|---|
| pip/tensorflow-gpu | =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 | =2.7.0 | 2.7.1 |
| pip/tensorflow-cpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-cpu | <2.5.3 | 2.5.3 |
| pip/tensorflow | =2.7.0 | 2.7.1 |
| pip/tensorflow | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow | <2.5.3 | 2.5.3 |
| TensorFlow Keras | <=2.5.2 | |
| TensorFlow Keras | >=2.6.0<=2.6.2 | |
| TensorFlow Keras | =2.7.0 |
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Reference Links
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558
- https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c94w-c95p-phf8
- https://nvd.nist.gov/vuln/detail/CVE-2022-23575
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-84.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-139.yaml
- https://github.com/advisories/GHSA-c94w-c95p-phf8 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-23575?
CVE-2022-23575 is classified as a high severity vulnerability due to its potential impact on applications using TensorFlow.
How do I fix CVE-2022-23575?
To fix CVE-2022-23575, update TensorFlow to version 2.5.3, 2.6.3, or 2.7.1 or later, depending on your current version.
Which versions of TensorFlow are affected by CVE-2022-23575?
CVE-2022-23575 affects TensorFlow versions up to 2.5.2 and versions between 2.6.0 and 2.6.2, as well as version 2.7.0.
What type of vulnerability is CVE-2022-23575?
CVE-2022-23575 is an integer overflow vulnerability found in the CalculateTensorSize function of TensorFlow.
Can CVE-2022-23575 be exploited remotely?
Yes, CVE-2022-23575 can potentially be exploited remotely if an attacker can send specially crafted inputs to the affected TensorFlow applications.
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