CVE-2022-35984 (published 2022-09-16T22:15Z)

TensorFlow is an open source platform for machine learning. `ParameterizedTruncatedNormal` assumes `shape` is of type `int32`. A valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 72180be03447a10810edca700cbc9af690dfeb51. 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: Antonio Sanchez
author_date: 2022-06-22 22:30:03-07:00
msg: Fix tensor shape dtype bug in parameterized_truncated_normal.

The original made the assumption that the dtype is int32 when it could
also be int64 - leading to a crash due to mismatched type.

PiperOrigin-RevId: 456685278
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: parameterized_truncated_normal_op.cc
change_type: ModificationType.MODIFY
diff: @@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_util.h"
 #include "tensorflow/core/kernels/stateless_random_ops.h"
 #include "tensorflow/core/lib/random/random_distributions.h"
 #include "tensorflow/core/platform/logging.h"
@@ -630,20 +631,18 @@ class ParameterizedTruncatedNormalOp : public OpKernel {
     OP_REQUIRES(ctx, shape_tensor.NumElements() > 0,
                 errors::InvalidArgument("Shape tensor must not be empty, got ",
                                         shape_tensor.DebugString()));
-    int32_t num_batches = shape_tensor.flat<int32>()(0);
+    TensorShape tensor_shape;
+    OP_REQUIRES_OK(ctx, tensor::MakeShape(shape_tensor, &tensor_shape));
 
+    int32_t num_batches = tensor_shape.dim_size(0);
     int32_t samples_per_batch = 1;
-    const int32_t num_dims = shape_tensor.dim_size(0);
+    const int32_t num_dims = tensor_shape.dims();
     for (int32_t i = 1; i < num_dims; i++) {
-      samples_per_batch *= shape_tensor.flat<int32>()(i);
+      samples_per_batch *= tensor_shape.dim_size(i);
     }
     const int32_t num_elements = num_batches * samples_per_batch;
 
     // Allocate the output before fudging num_batches and samples_per_batch.
-    auto shape_vec = shape_tensor.flat<int32>();
-    TensorShape tensor_shape;
-    OP_REQUIRES_OK(ctx, TensorShapeUtils::MakeShape(
-                            shape_vec.data(), shape_vec.size(), &tensor_shape));
     Tensor* samples_tensor;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, tensor_shape, &samples_tensor));
 

programming_language: C++
filename: parameterized_truncated_normal_op_test.py
change_type: ModificationType.MODIFY
diff: @@ -303,6 +303,29 @@ class ParameterizedTruncatedNormalTest(test.TestCase):
       self.assertAllGreater(samples, 0.)
       self.assertAllGreater(samples_stateless, 0.)
 
+  def testShapeTypes(self):
+    for shape_dtype in [np.int32, np.int64]:
+      shape = np.array([1000], dtype=shape_dtype)
+      sample_op = random_ops.parameterized_truncated_normal(
+          shape=shape, means=0.0, stddevs=0.1, minvals=-1., maxvals=1.)
+      new_seed = random_ops.random_uniform([2],
+                                           seed=1234,
+                                           minval=0,
+                                           maxval=(2**31 - 1),
+                                           dtype=np.int32)
+      sample_op_stateless = stateless.stateless_parameterized_truncated_normal(
+          shape=shape,
+          seed=new_seed,
+          means=0.0,
+          stddevs=0.1,
+          minvals=-1.,
+          maxvals=1.)
+
+      samples = self.evaluate(sample_op)
+      stateless_samples = self.evaluate(sample_op_stateless)
+      self.assertAllEqual(samples.shape, shape)
+      self.assertAllEqual(stateless_samples.shape, shape)
+
   def testStatelessParameterizedTruncatedNormalHasGrads(self):
     mean = variables.Variable(0.01)
     stddev = variables.Variable(1.)

programming_language: Python