CVSS2
Attack Vector
NETWORK
Attack Complexity
LOW
Authentication
SINGLE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:N/AC:L/Au:S/C:P/I:P/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
CHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
AI Score
Confidence
High
EPSS
Percentile
64.8%
In Tensorflow version 2.3.0, the SparseCountSparseOutput
and RaggedCountSparseOutput
implementations don’t validate that the weights
tensor has the same shape as the data. The check exists for DenseCountSparseOutput
, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. 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:*:*:*:-:*:*:* |
[
{
"product": "tensorflow",
"vendor": "tensorflow",
"versions": [
{
"status": "affected",
"version": "= 2.3.0"
}
]
}
]
CVSS2
Attack Vector
NETWORK
Attack Complexity
LOW
Authentication
SINGLE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:N/AC:L/Au:S/C:P/I:P/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
CHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
AI Score
Confidence
High
EPSS
Percentile
64.8%