OOB read in `MatrixTriangularSolve`

### Impact 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. ### Patches We have patched the issue in GitHub commit [480641e3599775a8895254ffbc0fc45621334f68](https://github.com/tensorflow/tensorflow/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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ye Zhang and Yakun Zhang of Baidu X-Team.

CVSS 3.1

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Severity from

GitHub (reviewed advisory)

Weakness

CWE-125

Also known as

BIT-tensorflow-2021-29551, CVE-2021-29551, PYSEC-2021-188, PYSEC-2021-479, PYSEC-2021-677

• github.com/tensorflow/tensorflow/security/advisories/GHSA-vqw6-72r7-fgw7
• nvd.nist.gov/vuln/detail/CVE-2021-29551
• github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-479.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-677.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-188.yaml
• github.com/tensorflow/tensorflow