CVE-2021-29519

TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

CVSS v3.0 5.5 MEDIUM
CVSS v2.0 2.1 LOW

5.5^/10

CVSS v3.0 : MEDIUM

Vector :

Exploitability : 1.8 / Impact : 3.6

Attack Vector LOCAL

Attack Complexity LOW

Privileges Required LOW

User Interaction NONE

Confidentiality Impact NONE

Integrity Impact NONE

Availability Impact HIGH

Scope UNCHANGED

2.1^/10

CVSS v2.0 : LOW

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

Exploitability : 3.9 / Impact : 2.9

Access Vector LOCAL

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-772j-h9xw-ffp5	Exploit Patch Third Party Advisory
https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025	Patch 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::::::::
	cpe:2.3:a:google:tensorflow::::::::

Information

Published : 2021-05-14 13:15

Updated : 2021-05-20 10:24

NVD link : CVE-2021-29519

Mitre link : CVE-2021-29519

JSON object : View

CWE

CWE-843

Access of Resource Using Incompatible Type ('Type Confusion')

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Products Affected

google

tensorflow

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