CVSS2
Attack Vector
NETWORK
Attack Complexity
MEDIUM
Authentication
SINGLE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:N/AC:M/Au:S/C:N/I:N/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
NONE
Scope
CHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
EPSS
Percentile
55.6%
In Tensorflow before version 2.3.1, the SparseCountSparseOutput
implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices
tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a CHECK
assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Vendor | Product | Version | CPE |
---|---|---|---|
tensorflow | 2.3.0 | cpe:2.3:a:google:tensorflow:2.3.0:*:*:*:-:*:*:* |
CVSS2
Attack Vector
NETWORK
Attack Complexity
MEDIUM
Authentication
SINGLE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:N/AC:M/Au:S/C:N/I:N/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
NONE
Scope
CHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
EPSS
Percentile
55.6%