CVE-2022-35973: Segfault in `QuantizedMatMul` in TensorFlow
TensorFlow is an open source platform for machine learning. If QuantizedMatMul is given nonscalar input for: mina, maxa, minb, or maxb It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2022-35973?
The severity of CVE-2022-35973 is classified as a denial of service vulnerability.
How do I fix CVE-2022-35973?
To fix CVE-2022-35973, update TensorFlow to a version that includes the patch from GitHub commit aca766ac7693bf29.
What component is affected by CVE-2022-35973?
CVE-2022-35973 affects the `QuantizedMatMul` operation in TensorFlow.
What input causes the issue in CVE-2022-35973?
CVE-2022-35973 is triggered when nonscalar input is provided for the parameters `min_a`, `max_a`, `min_b`, or `max_b`.
Which versions of TensorFlow are vulnerable to CVE-2022-35973?
TensorFlow versions between 2.7.0 and 2.7.2, 2.8.0 and 2.8.1, and 2.9.0 and 2.9.1, as well as specific release candidates of 2.10 are vulnerable.