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
QuantizeV2can trigger a read outside of bounds of heap allocated array:This occurs whenever
axisis a negative value less than-1. In this case, we are accessing data before the start of a heap buffer:The code allows
axisto be an optional argument (swould contain anerror::NOT_FOUNDerror code). Otherwise, it assumes thataxisis a valid index into the dimensions of theinputtensor. Ifaxisis less than-1then 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