AVID-2026-R0948

Description

Memory exhaustion in Tensorflow (CVE-2022-21732)

Details

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.

Reason for inclusion in AVID: CVE-2022-21732 describes a memory exhaustion vulnerability in TensorFlow’s ThreadPoolHandle that can be exploited to cause denial of service. TensorFlow is a core AI framework; the issue affects software components used to build, train, deploy, and run AI systems, making it relevant to the AI supply chain. The vulnerability is security-related (DoS), with explicit signal and references, satisfying evidence requirements.

References

Affected or Relevant Artifacts

Developer: n/a
Deployer: n/a
Artifact Details:

Type	Name
System	n/a

Impact

AVID Taxonomy Categorization

Risk domains: Security
SEP subcategories: S0100: Software Vulnerability
Lifecycle stages: L06: Deployment

CVSS

Version	3.1
Vector String	CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L
Base Score	4.3
Base Severity	🟠 Medium
Attack Vector	NETWORK
Attack Complexity	🟢 Low
Privileges Required	🟢 Low
User Interaction	NONE
Scope	UNCHANGED
Confidentiality Impact	NONE
Integrity Impact	NONE
Availability Impact	🟢 Low

Other information

Report Type: Advisory
Credits:
Date Reported: 2022-02-03
Version: 0.3.3
AVID Entry