Vulnerability in Google Tensorflow
CVE-2022-35990
TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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 (32.5th 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-2022-35990?
- CVE-2022-35990 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-2022-35990?
- Medium severity. CVSS v3 base score is 5.9 out of 10.
- Is CVE-2022-35990 known to be exploited?
- 1 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.