CVE-2022-23581

Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `IsSimplifiableReshape` would trigger `CHECK` failures. 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.

CVSS v3.0 6.5 MEDIUM
CVSS v2.0 5.0 MEDIUM

6.5^/10

CVSS v3.0 : MEDIUM

Vector :

Exploitability : 2.8 / Impact : 3.6

Attack Vector NETWORK

Attack Complexity LOW

Privileges Required LOW

User Interaction NONE

Confidentiality Impact NONE

Integrity Impact NONE

Availability Impact HIGH

Scope UNCHANGED

5.0^/10

CVSS v2.0 : MEDIUM

Vector : AV:N/AC:L/Au:N/C:N/I:N/A:P

Exploitability : 10.0 / Impact : 2.9

Access Vector NETWORK

Access Complexity LOW

Authentication NONE

Confidentiality Impact NONE

Integrity Impact NONE

Availability Impact PARTIAL

References

Link	Resource
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c	Patch Third Party Advisory
https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082	Patch Third Party Advisory
https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6	Patch Third Party Advisory
https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1	Patch Third Party Advisory
https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1687-L1742	Exploit Third Party Advisory

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Configurations

Configuration 1 (hide)

OR	cpe:2.3:a:google:tensorflow::::::::
	cpe:2.3:a:google:tensorflow::::::::
	cpe:2.3:a:google:tensorflow:2.7.0:::::::*

Information

Published : 2022-02-04 15:15

Updated : 2022-02-09 17:34

NVD link : CVE-2022-23581

Mitre link : CVE-2022-23581

JSON object : View

CWE

CWE-617

Reachable Assertion

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Products Affected

google

tensorflow

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