AVID-2026-R1111

Description

Segfault in Requantize in TensorFlow (CVE-2022-36017)

Details

TensorFlow is an open source platform for machine learning. If Requantize is given input_min, input_max, requested_output_min, requested_output_max 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 785d67a78a1d533759fcd2f5e8d6ef778de849e0. 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-2022-36017 describes a segfault in TensorFlow’s Requantize, enabling denial of service. TensorFlow is a core AI framework commonly used to build, train, deploy, and run AI systems, so this vulnerability is relevant to AI software stacks and their supply chains. It affects software components (TensorFlow versions) used in general-purpose AI pipelines, not hardware/firmware, and there is a concrete CVE entry with a patch and affected versions, satisfying the evidence requirement.

References

• NVD entry
• https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc

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