CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
AI Score
Confidence
High
EPSS
Percentile
12.6%
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for tf.raw_ops.Dequantize
has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses axis
to select between two different values for minmax_rank
which is then used to retrieve tensor dimensions. However, code assumes that axis
can be either -1
or a value greater than -1
, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
[
{
"product": "tensorflow",
"vendor": "tensorflow",
"versions": [
{
"status": "affected",
"version": ">= 2.5.0, < 2.5.1"
},
{
"status": "affected",
"version": ">= 2.4.0, < 2.4.3"
},
{
"status": "affected",
"version": "< 2.3.4"
}
]
}
]
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
AI Score
Confidence
High
EPSS
Percentile
12.6%