AVID-2026-R1200

Description

TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation (CVE-2023-25668)

Details

TensorFlow is an open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

Reason for inclusion in AVID: CVE-2023-25668 is a security vulnerability in TensorFlow (a core ML framework) that could enable remote code execution or crashes via a heap-out-of-bounds read in QuantizeAndDequantize. TensorFlow is a foundational library used to build, train, deploy, and run AI systems. The issue affects software components in AI pipelines (framework/library level), and the report provides explicit CVE details, affected versions, impact, and remediation, offering sufficient evidence for a supply-chain vulnerability classification in general-purpose AI systems.

References

Affected or Relevant Artifacts

Developer: tensorflow
Deployer: tensorflow
Artifact Details:

Type	Name
System	tensorflow

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:N/UI:N/S:U/C:H/I:H/A:H
Base Score	9.8
Base Severity	🔴 Critical
Attack Vector	NETWORK
Attack Complexity	🟢 Low
Privileges Required	NONE
User Interaction	NONE
Scope	UNCHANGED
Confidentiality Impact	🔴 High
Integrity Impact	🔴 High
Availability Impact	🔴 High

CWE

ID	Description
CWE-122	CWE-122: Heap-based Buffer Overflow
CWE-125	CWE-125: Out-of-bounds Read

Other information

Report Type: Advisory
Credits:
Date Reported: 2023-03-24
Version: 0.3.3
AVID Entry