CVE-2021-37663 Incomplete validation in `QuantizeV2` in TensorFlow

2021-08-1222:45:18

CWE-20

GitHub_M

www.cve.org

7.8 High

CVSS3

Attack Vector

LOCAL

Attack Complexity

LOW

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

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

0.0004 Low

EPSS

Percentile

12.8%

JSON

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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"
      }
    ]
  }
]