CVE-2021-41212 (published 2021-11-05T21:15Z)

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Severity: LOW
CVSS v2 base score: 3.6 (AV:L/AC:L/Au:N/C:P/I:N/A:P)
CVSS v3 base score: 7.1, HIGH (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H)

Weakness information:
cwe_id: CWE-125
cwe_name: Out-of-bounds Read
description: The product reads data past the end, or before the beginning, of the intended buffer.
extended_description: 
            
            
             
			 
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Edward Loper
author_date: 2021-09-30 15:52:09-07:00
msg: Fix null pointer exception in shape inference function when tf.ragged.cross() is called with invalid inputs.

PiperOrigin-RevId: 400045848
Change-Id: Ia65501583b85cf1ec14a252d83fbdd716817a516
repository: repo_url: https://github.com/tensorflow/tensorflow
repo_name: tensorflow/tensorflow
description: An Open Source Machine Learning Framework for Everyone
repo_language: C++
owner: tensorflow
file_changes: filename: ragged_array_ops.cc
change_type: ModificationType.MODIFY
diff: @@ -99,6 +99,13 @@ REGISTER_OP("RaggedCross")
       int dense_start = num_ragged * 2 + num_sparse * 3;
       for (int i = 0; i < dense_types.size(); ++i) {
         ShapeHandle dense_input = c->input(i + dense_start);
+        int32 rank = c->Rank(dense_input);
+        if (rank == InferenceContext::kUnknownRank) {
+          continue;
+        } else if (rank != 2) {
+          return errors::InvalidArgument(
+              "tf.ragged.cross only supports inputs with rank=2");
+        }
         int64_t batch_size = c->Value(c->Dim(dense_input, 0));
         if (batch_size != InferenceContext::kUnknownDim) {
           ShapeHandle row_splits = c->Vector(batch_size + 1);

programming_language: C++
filename: ragged_cross_op_test.py
change_type: ModificationType.MODIFY
diff: @@ -18,10 +18,12 @@ from absl.testing import parameterized
 
 import numpy as np
 
+from tensorflow.python.eager import def_function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_spec
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops.ragged import ragged_array_ops
@@ -358,6 +360,16 @@ class RaggedCrossOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
                   dense_const([[2], [3]])],
           exception=(ValueError, errors.InvalidArgumentError),
           message='inputs must all have the same batch dimension size'),
+      dict(
+          testcase_name='3DDenseTensor',
+          inputs=[dense_const([[[1]]])],
+          exception=(ValueError, errors.InvalidArgumentError),
+          message='tf.ragged.cross only supports inputs with rank=2'),
+      dict(
+          testcase_name='0DDenseTensor',
+          inputs=[dense_const(1)],
+          exception=(ValueError, errors.InvalidArgumentError),
+          message='tf.ragged.cross only supports inputs with rank=2'),
   ])
   def testStaticError(self, inputs, exception=ValueError, message=None):
     with self.assertRaisesRegex(exception, message):
@@ -368,17 +380,36 @@ class RaggedCrossOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
           testcase_name='3DRaggedTensor',
           inputs=[ragged_const([[[1]]], ragged_rank=1)],
           message='tf.ragged.cross only supports inputs with rank=2'),
+      dict(
+          testcase_name='0DDenseTensor',
+          inputs=[dense_const(1)],
+          signature=[[tensor_spec.TensorSpec(None, dtypes.int32)]],
+          exception=(ValueError, errors.InvalidArgumentError),
+          message='tf.ragged.cross only supports inputs with rank=2'),
+      dict(
+          testcase_name='1DDenseTensor',
+          inputs=[dense_const([1])],
+          signature=[[tensor_spec.TensorSpec(None, dtypes.int32)]],
+          exception=(ValueError, errors.InvalidArgumentError),
+          message='tf.ragged.cross only supports inputs with rank=2'),
       dict(
           testcase_name='3DDenseTensor',
           inputs=[dense_const([[[1]]])],
+          signature=[[tensor_spec.TensorSpec(None, dtypes.int32)]],
+          exception=(ValueError, errors.InvalidArgumentError),
           message='tf.ragged.cross only supports inputs with rank=2'),
   ])
   def testRuntimeError(self,
                        inputs,
                        exception=errors.InvalidArgumentError,
-                       message=None):
+                       message=None,
+                       signature=None):
+    @def_function.function(input_signature=signature)
+    def fn(x):
+      return ragged_array_ops.cross(x)
+
     with self.assertRaisesRegex(exception, message):
-      self.evaluate(ragged_array_ops.cross(inputs))
+      self.evaluate(fn(inputs))
 
   def _ragged_to_sparse(self, t):
     if ragged_tensor.is_ragged(t):

programming_language: Python