CVE Vulnerabilities

CVE-2022-23591

Uncontrolled Resource Consumption

Published: Feb 04, 2022 | Modified: Nov 21, 2024
CVSS 3.x
7.5
HIGH
Source:
NVD
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
CVSS 2.x
5 MEDIUM
AV:N/AC:L/Au:N/C:N/I:N/A:P
RedHat/V2
RedHat/V3
Ubuntu

Tensorflow is an Open Source Machine Learning Framework. The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel. This would result in a stack overflow during execution as resolving each NodeDef means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Weakness

The product does not properly control the allocation and maintenance of a limited resource.

Affected Software

Name Vendor Start Version End Version
Tensorflow Google * 2.5.2 (including)
Tensorflow Google 2.6.0 (including) 2.6.2 (including)
Tensorflow Google 2.7.0 (including) 2.7.0 (including)

Potential Mitigations

  • Mitigation of resource exhaustion attacks requires that the target system either:

  • The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.

  • The second solution is simply difficult to effectively institute – and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.

References