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AVID-2026-R0867

Description

Missing validation in shape inference for Dequantize in TensorFlow (CVE-2021-37677)

Details

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Reason for inclusion in AVID: CVE-2021-37677 describes a vulnerability in TensorFlow’s shape inference for tf.raw_ops.Dequantize that can cause a denial of service via a segfault when given invalid inputs. This is a software vulnerability in a core AI framework (TensorFlow) used to build/train/deploy AI models, i.e., a component in the AI software stack. The issue is clearly documented with CVE/NVD references and a commit fix, indicating actionable security impact within AI software pipelines. Therefore it satisfies AI-related, GP AI supply chain, and security criteria with sufficient evidence.

References

Affected or Relevant Artifacts

  • Developer: tensorflow
  • Deployer: tensorflow
  • Artifact Details:
TypeName
Systemtensorflow

Impact

AVID Taxonomy Categorization

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

CVSS

Version3.1
Vector StringCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Base Score5.5
Base Severity🟠 Medium
Attack VectorLOCAL
Attack Complexity🟢 Low
Privileges Required🟢 Low
User InteractionNONE
ScopeUNCHANGED
Confidentiality ImpactNONE
Integrity ImpactNONE
Availability Impact🔴 High

CWE

IDDescription
CWE-20CWE-20: Improper Input Validation

Other information

  • Report Type: Advisory
  • Credits:
  • Date Reported: 2021-08-12
  • Version: 0.3.3
  • AVID Entry