CVE-2022-35967: Segfault in `QuantizedAdd` in TensorFlow
First published: Fri Sep 16 2022(Updated: )
TensorFlow is an open source platform for machine learning. If `QuantizedAdd` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. 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.
Credit: security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| TensorFlow Keras | >=2.7.0<2.7.2 | |
| TensorFlow Keras | >=2.8.0<2.8.1 | |
| TensorFlow Keras | >=2.9.0<2.9.1 | |
| TensorFlow Keras | =2.10-rc0 | |
| TensorFlow Keras | =2.10-rc1 | |
| TensorFlow Keras | =2.10-rc2 | |
| TensorFlow Keras | =2.10-rc3 |
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Reference Links
Frequently Asked Questions
What is the severity of CVE-2022-35967?
CVE-2022-35967 has been identified as a vulnerability that can lead to denial of service due to a segmentation fault.
How do I fix CVE-2022-35967?
To fix CVE-2022-35967, upgrade TensorFlow to a version that includes the patch for this vulnerability.
Which versions of TensorFlow are affected by CVE-2022-35967?
CVE-2022-35967 affects TensorFlow versions from 2.7.0 to 2.9.1, and also includes the 2.10 release candidates.
What is the impact of exploiting CVE-2022-35967?
Exploiting CVE-2022-35967 can result in a segmentation fault, effectively causing a denial of service.
Can CVE-2022-35967 be exploited in production systems?
Yes, if unpatched, CVE-2022-35967 could be exploited in production systems that utilize affected versions of TensorFlow.
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