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 |
---|---|---|
Google TensorFlow | =2.7.0 |
Sign up to SecAlerts for real-time vulnerability data matched to your software, aggregated from hundreds of sources.
CVE-2022-23594 has a medium severity level due to the potential for denial of service attacks through specific input modifications.
To fix CVE-2022-23594, upgrade TensorFlow to version 2.7.1 or later where the issue has been addressed.
CVE-2022-23594 affects TensorFlow version 2.7.0.
CVE-2022-23594 is categorized as a security vulnerability that can lead to application instability or denial of service.
Users and developers utilizing TensorFlow version 2.7.0 are impacted by CVE-2022-23594.