Vulnerability in Google Tensorflow
CVE-2022-35985
TensorFlow is an open source platform for machine learning. If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. 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.
EPSS: 0.004 (33.7th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 5.9 (Medium). Vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H.
Affected products
- Google Tensorflow — versions 2.10
- Tensorflow — versions < 2.7.2, >= 2.8.0, < 2.8.1, >= 2.9.0, < 2.9.1
Weakness classification (CWE)
Public proof-of-concept exploits
References
- security-advisories@github.com (x_refsource_CONFIRM, Third Party Advisory)
- security-advisories@github.com (Patch, Third Party Advisory, x_refsource_MISC)
Frequently asked questions
- What is CVE-2022-35985?
- CVE-2022-35985 is a medium-severity vulnerability in Google Tensorflow, classified under Reachable Assertion. CVSS score: 5.9/10. Published 2022-09-16.
- How severe is CVE-2022-35985?
- Medium severity. CVSS v3 base score is 5.9 out of 10.
- Is CVE-2022-35985 known to be exploited?
- 1 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.