Impact
The shape inference code for QuantizeV2
can trigger a read outside of bounds of heap allocated array:
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeV2(
input=[1.0,1.0],
min_range=[1.0,10.0],
max_range=[1.0,10.0],
T=tf.qint32,
mode='MIN_COMBINED',
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100,
ensure_minimum_range=10)
return data
test()
This occurs whenever axis
is a negative value less than -1
. In this case, we are accessing data before the start of a heap buffer:
int axis = -1;
Status s = c->GetAttr("axis", &axis);
if (!s.ok() && s.code() != error::NOT_FOUND) {
return s;
}
...
if (axis != -1) {
...
TF_RETURN_IF_ERROR(
c->Merge(c->Dim(minmax, 0), c->Dim(input, axis), &depth));
}
The code allows axis
to be an optional argument (s
would contain an error::NOT_FOUND
error code). Otherwise, it assumes that axis
is a valid index into the dimensions of the input
tensor. If axis
is less than -1
then this results in a heap OOB read.
Patches
We have patched the issue in GitHub commit a0d64445116c43cf46a5666bd4eee28e7a82f244.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
References
Impact
The shape inference code for
QuantizeV2
can trigger a read outside of bounds of heap allocated array:This occurs whenever
axis
is a negative value less than-1
. In this case, we are accessing data before the start of a heap buffer:The code allows
axis
to be an optional argument (s
would contain anerror::NOT_FOUND
error code). Otherwise, it assumes thataxis
is a valid index into the dimensions of theinput
tensor. Ifaxis
is less than-1
then this results in a heap OOB read.Patches
We have patched the issue in GitHub commit a0d64445116c43cf46a5666bd4eee28e7a82f244.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
References