CVE-2022-35960 (published 2022-09-16T20:15Z)

TensorFlow is an open source platform for machine learning. In `core/kernels/list_kernels.cc's TensorListReserve`, `num_elements` is assumed to be a tensor of size 1. When a `num_elements` of more than 1 element is provided, then `tf.raw_ops.TensorListReserve` fails the `CHECK_EQ` in `CheckIsAlignedAndSingleElement`. We have patched the issue in GitHub commit b5f6fbfba76576202b72119897561e3bd4f179c7. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

CVSS v3 base score: 7.5, HIGH (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
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: Laura Pak
author_date: 2022-08-09 14:01:48-07:00
msg: Re-enable testTensorListReserveWithNonScalarNumElements to work with mlir as well.

PiperOrigin-RevId: 466460987
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: list_kernels.cc
change_type: ModificationType.MODIFY
diff: @@ -31,9 +31,11 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/framework/variant_op_registry.h"
+#include "tensorflow/core/platform/errors.h"
 
 namespace tensorflow {
 
@@ -322,6 +324,11 @@ class TensorListReserve : public OpKernel {
   void Compute(OpKernelContext* c) override {
     PartialTensorShape element_shape;
     OP_REQUIRES_OK(c, TensorShapeFromTensor(c->input(0), &element_shape));
+    OP_REQUIRES(
+        c, TensorShapeUtils::IsScalar(c->input(1).shape()),
+        errors::InvalidArgument(
+            "The num_elements to reserve must be a tensor size 1, but got ",
+            c->input(1).shape()));
     int32_t num_elements = c->input(1).scalar<int32>()();
     OP_REQUIRES(c, num_elements >= 0,
                 errors::InvalidArgument("The num_elements to reserve must be a "

programming_language: C++
filename: list_ops_test.py
change_type: ModificationType.MODIFY
diff: @@ -94,6 +94,16 @@ class ListOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
       l = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
       self.evaluate(l)
 
+  def testTensorListReserveWithNonScalarNumElements(self):
+    # list_kernels.cc in tf/core/kernels raises InvalidArgumentError, and
+    # tf_ops_n_z.cc in tf/compiler/mlir/tf/ir raises UnknownError.
+    with self.assertRaises((errors.InvalidArgumentError, errors.UnknownError)):
+      l = list_ops.tensor_list_reserve(
+          element_dtype=dtypes.float32,
+          element_shape=[2, 3],
+          num_elements=constant_op.constant([1, 1]))
+      self.evaluate(l)
+
   def testPopUninitializedTensorUseListElementShape(self):
     l = list_ops.tensor_list_reserve(
         element_dtype=dtypes.float32, element_shape=[2, 3], num_elements=3)

programming_language: Python