AVID-2026-R0943

Description

Integer overflow in Tensorflow (CVE-2022-21727)

Details

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness. The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes axis + 1, an attacker can trigger an integer overflow. 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: The CVE describes an integer overflow vulnerability in TensorFlow’s shape inference for the Dequantize operation. TensorFlow is a core AI framework used to build, train, and deploy AI systems, and this issue affects the software components (not hardware/firmware) relied upon in general-purpose AI stacks. It is a legitimate security vulnerability with evidence in CVE/NVD and TensorFlow advisories, affecting software supply chains (dependencies/frameworks) rather than firmware. Therefore it should be kept for AVID curation.

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:L/I:L/A:H
Base Score	7.6
Base Severity	🔴 High
Attack Vector	NETWORK
Attack Complexity	🟢 Low
Privileges Required	🟢 Low
User Interaction	NONE
Scope	UNCHANGED
Confidentiality Impact	🟢 Low
Integrity Impact	🟢 Low
Availability Impact	🔴 High

Other information

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