Resource exhaustion in Google Tensorflow
CVE-2022-23591
Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. 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.
Vulnerability class: DoS (Denial of Service)
EPSS: 0.008 (52.5th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 7.5 (High). Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H.
Affected products
- Google Tensorflow — versions 2.7.0
- Tensorflow — versions >= 2.7.0, < 2.7.1, >= 2.6.0, < 2.6.3, < 2.5.3
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-2022-23591?
- CVE-2022-23591 is a high-severity vulnerability in Google Tensorflow, classified under Uncontrolled Resource Consumption. CVSS score: 7.5/10. Published 2022-02-04.
- How severe is CVE-2022-23591?
- High severity. CVSS v3 base score is 7.5 out of 10.
- Is CVE-2022-23591 known to be exploited?
- 2 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.