CVE-2020-15194 (published 2020-09-25T19:15Z)

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Severity: MEDIUM
CVSS v2 base score: 5.0 (AV:N/AC:L/Au:N/C:N/I:N/A:P)
CVSS v3 base score: 5.3, MEDIUM (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L)

Weakness information:
cwe_id: CWE-20
cwe_name: Improper Input Validation
description: The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.
extended_description: 
	   Input validation is a frequently-used technique
	   for checking potentially dangerous inputs in order to
	   ensure that the inputs are safe for processing within the
	   code, or when communicating with other components.  When
	   software does not validate input properly, an attacker is
	   able to craft the input in a form that is not expected by
	   the rest of the application. This will lead to parts of the
	   system receiving unintended input, which may result in
	   altered control flow, arbitrary control of a resource, or
	   arbitrary code execution.
	   Input validation is not the only technique for
	   processing input, however.  Other techniques attempt to
	   transform potentially-dangerous input into something safe, such
	   as filtering (CWE-790) - which attempts to remove dangerous
	   inputs - or encoding/escaping (CWE-116), which attempts to
	   ensure that the input is not misinterpreted when it is included
	   in output to another component. Other techniques exist as well
	   (see CWE-138 for more examples.)
	   Input validation can be applied to:
	   
	     raw data - strings, numbers, parameters, file contents, etc.
	     metadata - information about the raw data, such as headers or size
	   
	   Data can be simple or structured.  Structured data
	   can be composed of many nested layers, composed of
	   combinations of metadata and raw data, with other simple or
	   structured data.
	   Many properties of raw data or metadata may need
	   to be validated upon entry into the code, such
	   as:
	   
	     specified quantities such as size, length, frequency, price, rate, number of operations, time, etc.
	     implied or derived quantities, such as the actual size of a file instead of a specified size
	     indexes, offsets, or positions into more complex data structures
	     symbolic keys or other elements into hash tables, associative arrays, etc.
	     well-formedness, i.e. syntactic correctness - compliance with expected syntax 
	     lexical token correctness - compliance with rules for what is treated as a token
	     specified or derived type - the actual type of the input (or what the input appears to be)
	     consistency - between individual data elements, between raw data and metadata, between references, etc.
	     conformance to domain-specific rules, e.g. business logic 
	     equivalence - ensuring that equivalent inputs are treated the same
	     authenticity, ownership, or other attestations about the input, e.g. a cryptographic signature to prove the source of the data
	   
	   Implied or derived properties of data must often
	   be calculated or inferred by the code itself.  Errors in
	   deriving properties may be considered a contributing factor
	   to improper input validation.
	   
	   Note that "input validation" has very different
	   meanings to different people, or within different
	   classification schemes.  Caution must be used when
	   referencing this CWE entry or mapping to it.  For example,
	   some weaknesses might involve inadvertently giving control
	   to an attacker over an input when they should not be able
	   to provide an input at all, but sometimes this is referred
	   to as input validation.
	   Finally, it is important to emphasize that the
	   distinctions between input validation and output escaping
	   are often blurred, and developers must be careful to
	   understand the difference, including how input validation
	   is not always sufficient to prevent vulnerabilities,
	   especially when less stringent data types must be
	   supported, such as free-form text. Consider a SQL injection
	   scenario in which a person's last name is inserted into a
	   query. The name "O'Reilly" would likely pass the validation
	   step since it is a common last name in the English
	   language. However, this valid name cannot be directly
	   inserted into the database because it contains the "'"
	   apostrophe character, which would need to be escaped or
	   otherwise transformed. In this case, removing the
	   apostrophe might reduce the risk of SQL injection, but it
	   would produce incorrect behavior because the wrong name
	   would be recorded.
       
         
cwe_id: CWE-617
cwe_name: Reachable Assertion
description: The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
extended_description: 
            While assertion is good for catching logic errors and reducing the chances of reaching more serious vulnerability conditions, it can still lead to a denial of service.
            For example, if a server handles multiple simultaneous connections, and an assert() occurs in one single connection that causes all other connections to be dropped, this is a reachable assertion that leads to a denial of service.
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Mihai Maruseac
author_date: 2020-09-18 18:43:38-07:00
msg: Fix heap buffer overflow in `tf.raw_ops.SparseFillEmptyRowsGrad`.

Also add tests as they were lacking

PiperOrigin-RevId: 332566071
Change-Id: I44277578e26ff5fb3fdb0dcbba6e91b2ec3e7859
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: sparse_fill_empty_rows_op.cc
change_type: ModificationType.MODIFY
diff: @@ -236,6 +236,9 @@ class SparseFillEmptyRowsGradOp : public OpKernel {
         context, TensorShapeUtils::IsVector(reverse_index_map_t->shape()),
         errors::InvalidArgument("reverse_index_map must be a vector, saw: ",
                                 reverse_index_map_t->shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(grad_values_t->shape()),
+                errors::InvalidArgument("grad_values must be a vector, saw: ",
+                                        grad_values_t->shape().DebugString()));
 
     const auto reverse_index_map = reverse_index_map_t->vec<int64>();
     const auto grad_values = grad_values_t->vec<T>();
@@ -264,8 +267,13 @@ class SparseFillEmptyRowsGradOp : public OpKernel {
       // Locate the index of the output of the forward prop associated
       // with this location in the input of the forward prop.  Copy
       // the gradient into it.  Mark it as visited.
-      d_values(i) = grad_values(reverse_index_map(i));
-      visited(reverse_index_map(i)) = true;
+      int64 reverse_index = reverse_index_map(i);
+      OP_REQUIRES(
+          context, 0 <= reverse_index && reverse_index < N_full,
+          errors::InvalidArgument("Elements in reverse index must be in [0, ",
+                                  N_full, ") but got ", reverse_index));
+      d_values(i) = grad_values(reverse_index);
+      visited(reverse_index) = true;
     }
     for (int j = 0; j < N_full; ++j) {
       // The default value gradient gets the accumulated remainder of

programming_language: C++
filename: sparse_ops_test.py
change_type: ModificationType.MODIFY
diff: @@ -21,6 +21,7 @@ from __future__ import print_function
 from absl.testing import parameterized
 import numpy as np
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -30,6 +31,7 @@ from tensorflow.python.framework import test_util
 # Need array_grad to register gradient for Identity.
 from tensorflow.python.ops import array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import gradient_checker_v2 as gradient_checker
 from tensorflow.python.ops import math_ops
 # Need sparse_grad to register gradient for SparseToDense.
@@ -234,5 +236,57 @@ class SparseOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
     self.assertAllEqual([5], result.dense_shape)
 
 
+@test_util.run_all_in_graph_and_eager_modes
+class RawOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
+
+  def testSparseFillEmptyRowsGrad(self):
+    reverse_index_map = [2, 1]
+    grad_values = [0, 1, 2, 3]
+    d_values, d_default_value = self.evaluate(
+        gen_sparse_ops.SparseFillEmptyRowsGrad(
+            reverse_index_map=reverse_index_map, grad_values=grad_values))
+    self.assertAllEqual([2, 1], d_values)
+    self.assertEqual(3, d_default_value)
+
+  def testSparseFillEmptyRowsGradNegativeIndexMapValue(self):
+    reverse_index_map = [2, -1]
+    grad_values = [0, 1, 2, 3]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        r'Elements in reverse index must be in \[0, 4\)'):
+      self.evaluate(
+          gen_sparse_ops.SparseFillEmptyRowsGrad(
+              reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+  def testSparseFillEmptyRowsGradLargeIndexMapValue(self):
+    reverse_index_map = [2, 10]
+    grad_values = [0, 1, 2, 3]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        r'Elements in reverse index must be in \[0, 4\)'):
+      self.evaluate(
+          gen_sparse_ops.SparseFillEmptyRowsGrad(
+              reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+  def testSparseFillEmptyRowsGradMatrix(self):
+    reverse_index_map = [0, 1]
+    grad_values = [[0, 1], [2, 3]]
+    # Note: Eager mode and graph mode throw different errors here. Graph mode
+    # will fail with a ValueError from the shape checking logic, while Eager
+    # will fail with an InvalidArgumentError from the kernel itself.
+    if context.executing_eagerly():
+      with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                  r'grad_values must be a vector'):
+        self.evaluate(
+            gen_sparse_ops.SparseFillEmptyRowsGrad(
+                reverse_index_map=reverse_index_map, grad_values=grad_values))
+    else:
+      with self.assertRaisesRegex(ValueError,
+                                  r'Shape must be rank 1 but is rank 2'):
+        self.evaluate(
+            gen_sparse_ops.SparseFillEmptyRowsGrad(
+                reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+
 if __name__ == '__main__':
   googletest.main()

programming_language: Python