PyPI: tensorflow
CVE-2022-23583
Safety vulnerability ID: SFTY-20220204-19581
Safety legacy ID: pyup.io-44868
Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. 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.
Overview
`CHECK`-failures in binary ops in Tensorflow
Advisory
Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. 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.
Affected Package
Also affects
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How to Fix
Mitigation and Workarounds
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Vulnerable Functions
Functions linked to known vulnerabilities.
References
- https://getsafety.com/vulnerabilities/SFTY-20220204-19581/CVE-2022-23583
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gjqc-q9g6-q2j3
- https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137
- https://nvd.nist.gov/vuln/detail/CVE-2022-23583
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-92.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-147.yaml
- https://github.com/advisories/GHSA-gjqc-q9g6-q2j3
Verified by Safety
Our Cybersecurity Intelligence Team reviewed this vulnerability. We combine public data with our own research to find issues not yet reported to public sources.
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