AVID-2026-R1074

Description

Segfault in QuantizedAdd in TensorFlow (CVE-2022-35967)

Details

TensorFlow is an open source platform for machine learning. If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Reason for inclusion in AVID: CVE-2022-35967 describes a vulnerability in TensorFlow (an AI framework) where QuantizedAdd can segfault, causing a denial of service. This directly concerns AI software stacks and dependencies used to build/train/deploy AI systems, i.e., the software supply chain for general-purpose AI. The description includes affected versions and a patch/commit, providing clear evidence of a security vulnerability in AI software tooling.

References

• NVD entry
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x
• https://github.com/tensorflow/tensorflow/commit/49b3824d83af706df0ad07e4e677d88659756d89

Affected or Relevant Artifacts

• Developer: tensorflow
• Deployer: tensorflow
• Artifact Details:

Impact

AVID Taxonomy Categorization

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

CVSS

CWE

Other information

• Report Type: Advisory
• Credits:
• Date Reported: 2022-09-16
• Version: 0.3.3
• AVID Entry