2.1 Low
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
5.5 Medium
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
0.001 Low
EPSS
Percentile
38.5%
The implementation of MatrixTriangularSolve
fails to terminate kernel execution if one validation condition fails:
void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0,
const Tensor& in1) override {
OP_REQUIRES(
ctx, in0.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));
OP_REQUIRES(
ctx, in1.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in1.dims()));
}
void Compute(OpKernelContext* ctx) override {
const Tensor& in0 = ctx->input(0);
const Tensor& in1 = ctx->input(1);
ValidateInputTensors(ctx, in0, in1);
MatMulBCast bcast(in0.shape().dim_sizes(), in1.shape().dim_sizes());
...
}
Since OP_REQUIRES
only sets ctx->status()
to a non-OK value and calls return
, this allows malicious attackers to trigger an out of bounds read:
import tensorflow as tf
import numpy as np
matrix_array = np.array([])
matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(1,0)),dtype=tf.float32)
rhs_array = np.array([])
rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(0,1)),dtype=tf.float32)
tf.raw_ops.MatrixTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor,lower=False,adjoint=False)
As the two input tensors are empty, the OP_REQUIRES
in ValidateInputTensors
should fire and interrupt execution. However, given the implementation of OP_REQUIRES
, after the in0.dims() >= 2
fails, execution moves to the initialization of the bcast
object. This initialization is done with invalid data and results in heap OOB read.
We have patched the issue in GitHub commit 480641e3599775a8895254ffbc0fc45621334f68.
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 Ye Zhang and Yakun Zhang of Baidu X-Team.
2.1 Low
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
5.5 Medium
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
0.001 Low
EPSS
Percentile
38.5%