Attacker Value
Unknown
(0 users assessed)
Exploitability
Unknown
(0 users assessed)
User Interaction
None
Privileges Required
Low
Attack Vector
Local
0

CVE-2021-37677

Disclosure Date: August 12, 2021
Add MITRE ATT&CK tactics and techniques that apply to this CVE.

Description

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.

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CVSS V3 Severity and Metrics
Base Score:
5.5 Medium
Impact Score:
3.6
Exploitability Score:
1.8
Vector:
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Attack Vector (AV):
Local
Attack Complexity (AC):
Low
Privileges Required (PR):
Low
User Interaction (UI):
None
Scope (S):
Unchanged
Confidentiality (C):
None
Integrity (I):
None
Availability (A):
High

General Information

Vendors

  • google

Products

  • tensorflow,
  • tensorflow 2.5.0,
  • tensorflow 2.6.0

Additional Info

Technical Analysis