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

2021-08-1222:35:10

CWE-20

GitHub_M

www.cve.org

tensorflow

machine learning

vulnerability

shape inference

denial of service

github

patch

cherrypick

cve-2021-37677

CVSS3

5.5

Attack Vector

LOCAL

Attack Complexity

LOW

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

HIGH

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

AI Score

6.1

Confidence

High

EPSS

Percentile

12.6%

JSON

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.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": ">= 2.5.0, < 2.5.1"
      },
      {
        "status": "affected",
        "version": ">= 2.4.0, < 2.4.3"
      },
      {
        "status": "affected",
        "version": "< 2.3.4"
      }
    ]
  }
]