CVE-2022-23594: Out of bounds read in Tensorflow
First published: Fri Feb 04 2022(Updated: )
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Credit: security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| TensorFlow Keras | =2.7.0 |
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Reference Links
Frequently Asked Questions
What is the severity of CVE-2022-23594?
CVE-2022-23594 has a medium severity level due to the potential for denial of service attacks through specific input modifications.
How do I fix CVE-2022-23594?
To fix CVE-2022-23594, upgrade TensorFlow to version 2.7.1 or later where the issue has been addressed.
What versions of TensorFlow are affected by CVE-2022-23594?
CVE-2022-23594 affects TensorFlow version 2.7.0.
What type of vulnerability is CVE-2022-23594?
CVE-2022-23594 is categorized as a security vulnerability that can lead to application instability or denial of service.
Who is impacted by CVE-2022-23594?
Users and developers utilizing TensorFlow version 2.7.0 are impacted by CVE-2022-23594.
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