Resource exhaustion in Google Tensorflow
CVE-2022-21732
Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
EPSS: 0.008 (51.7th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 4.3 (Medium). Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L.
Affected products
- Google Tensorflow — versions 2.7.0
- N/a — versions n/a
Weakness classification (CWE)
References
- security-advisories@github.com (x_refsource_CONFIRM, Patch, Third Party Advisory)
- security-advisories@github.com (Patch, Third Party Advisory, x_refsource_MISC)
- security-advisories@github.com (Exploit, Third Party Advisory, x_refsource_MISC)
Frequently asked questions
- What is CVE-2022-21732?
- CVE-2022-21732 is a medium-severity vulnerability in Google Tensorflow, classified under Allocation of Resources Without Limits or Throttling. CVSS score: 4.3/10. Published 2022-02-03.
- How severe is CVE-2022-21732?
- Medium severity. CVSS v3 base score is 4.3 out of 10.