CVE-2021-29612: Heap buffer overflow in `BandedTriangularSolve`

Impact An attacker can trigger a heap buffer overflow in Eigen implementation of tf.rawops.BandedTriangularSolve:

python import tensorflow as tf import numpy as np matrixarray = np.array([]) matrixtensor = tf.converttotensor(np.reshape(matrixarray,(0,1)),dtype=tf.float32) rhsarray = np.array([1,1]) rhstensor = tf.converttotensor(np.reshape(rhsarray,(1,2)),dtype=tf.float32) tf.rawops.BandedTriangularSolve(matrix=matrixtensor,rhs=rhstensor)

The implementation calls ValidateInputTensors for input validation but fails to validate that the two tensors are not empty: cc void ValidateInputTensors(OpKernelContext ctx, const Tensor& in0, const Tensor& in1) { OPREQUIRES( ctx, in0.dims() >= 2, errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));

OPREQUIRES( ctx, in1.dims() >= 2, errors::InvalidArgument("In[1] ndims must be >= 2: ", in1.dims())); }

Furthermore, since OPREQUIRES macro only stops execution of current function after setting ctx->status() to a non-OK value, callers of helper functions that use OPREQUIRES must check value of ctx->status() before continuing. This doesn't happen in this op's implementation, hence the validation that is present is also not effective.

Patches We have patched the issue in GitHub commit ba6822bd7b7324ba201a28b2f278c29a98edbef2 followed by GitHub commit 0ab290774f91a23bebe30a358fde4e53ab4876a0.

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.

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Attribution This vulnerability has been reported by Ye Zhang and Yakun Zhang of Baidu X-Team.