Improper input validation in Google Tensorflow
CVE-2022-35973
TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` 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.
Vulnerability class: Drupalgeddon 2 (CVE-2018-7600)
EPSS: 0.004 (36.0th 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, Patch, Third Party Advisory)
- security-advisories@github.com (Patch, Third Party Advisory, x_refsource_MISC)
Frequently asked questions
- What is CVE-2022-35973?
- CVE-2022-35973 is a medium-severity vulnerability in Google Tensorflow, classified under Improper Input Validation. CVSS score: 5.9/10. Published 2022-09-16.
- How severe is CVE-2022-35973?
- Medium severity. CVSS v3 base score is 5.9 out of 10.
- Is CVE-2022-35973 known to be exploited?
- 1 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.