CVSS2
Attack Vector
NETWORK
Attack Complexity
LOW
Authentication
SINGLE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:N/AC:L/Au:S/C:N/I:N/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS
Percentile
54.5%
The implementation of shape inference for ConcatV2
can be used to trigger a denial of service attack via a segfault caused by a type confusion:
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.ConcatV2(
values=[[1,2,3],[4,5,6]],
axis = 0xb500005b)
return y
test()
The axis
argument is translated into concat_dim
in the ConcatShapeHelper
helper function. Then, a value for min_rank
is computed based on concat_dim
. This is then used to validate that the values
tensor has at least the required rank:
int64_t concat_dim;
if (concat_dim_t->dtype() == DT_INT32) {
concat_dim = static_cast<int64_t>(concat_dim_t->flat<int32>()(0));
} else {
concat_dim = concat_dim_t->flat<int64_t>()(0);
}
// Minimum required number of dimensions.
const int min_rank = concat_dim < 0 ? -concat_dim : concat_dim + 1;
// ...
ShapeHandle input = c->input(end_value_index - 1);
TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, min_rank, &input));
However, WithRankAtLeast
receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented:
Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int64_t rank,
ShapeHandle* out) {
if (rank > kint32max) {
return errors::InvalidArgument("Rank cannot exceed kint32max");
}
// ...
}
Due to the fact that min_rank
is a 32-bits value and the value of axis
, the rank
argument is a negative value, so the error check is bypassed.
We have patched the issue in GitHub commit 08d7b00c0a5a20926363849f611729f53f3ec022.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
Vendor | Product | Version | CPE |
---|---|---|---|
tensorflow | gpu | 2.7.0 | cpe:2.3:a:tensorflow:gpu:2.7.0:*:*:*:*:*:*:* |
tensorflow | gpu | * | cpe:2.3:a:tensorflow:gpu:*:*:*:*:*:*:*:* |
tensorflow | cpu | 2.7.0 | cpe:2.3:a:tensorflow:cpu:2.7.0:*:*:*:*:*:*:* |
tensorflow | cpu | * | cpe:2.3:a:tensorflow:cpu:*:*:*:*:*:*:*:* |
tensorflow | tensorflow | 2.7.0 | cpe:2.3:a:tensorflow:tensorflow:2.7.0:*:*:*:*:*:*:* |
tensorflow | tensorflow | * | cpe:2.3:a:tensorflow:tensorflow:*:*:*:*:*:*:*:* |
github.com/advisories/GHSA-m4hf-j54p-p353
github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc#L1961-L2059
github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc#L345-L358
github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022
github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353
nvd.nist.gov/vuln/detail/CVE-2022-21731
CVSS2
Attack Vector
NETWORK
Attack Complexity
LOW
Authentication
SINGLE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:N/AC:L/Au:S/C:N/I:N/A:P
CVSS3
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS
Percentile
54.5%