4.6 Medium
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:P/A:P
7.8 High
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
0.0005 Low
EPSS
Percentile
17.9%
The implementation of the OneHot
TFLite operator is vulnerable to a division by zero error:
int prefix_dim_size = 1;
for (int i = 0; i < op_context.axis; ++i) {
prefix_dim_size *= op_context.indices->dims->data[i];
}
const int suffix_dim_size = NumElements(op_context.indices) / prefix_dim_size;
An attacker can craft a model such that at least one of the dimensions of indices
would be 0. In turn, the prefix_dim_size
value would become 0.
We have patched the issue in GitHub commit 3ebedd7e345453d68e279cfc3e4072648e5e12e5.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
CPE | Name | Operator | Version |
---|---|---|---|
tensorflow | lt | 2.4.2 | |
tensorflow | lt | 2.3.3 | |
tensorflow | lt | 2.2.3 | |
tensorflow | lt | 2.1.4 |
4.6 Medium
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:P/A:P
7.8 High
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
0.0005 Low
EPSS
Percentile
17.9%