CVE-2022-35987 (published 2022-09-16T22:15Z)

TensorFlow is an open source platform for machine learning. `DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bf4c14353c2328636a18bfad1e151052c81d5f43. 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: Sagun Bajra
author_date: 2022-07-13 16:13:13-07:00
msg: Fix security vulnerability with DenseBincountOp

PiperOrigin-RevId: 460826735
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: bincount_op.cc
change_type: ModificationType.MODIFY
diff: @@ -80,6 +80,17 @@ class DenseBincountOp : public XlaOpKernel {
     OP_REQUIRES_OK(ctx, weights_shape_or.status());
 
     auto weights_shape = weights_shape_or.ValueOrDie();
+    OP_REQUIRES(ctx,
+                xla::ShapeUtil::CompatibleIgnoringElementType(weights_shape,
+                                                              input_shape) ||
+                    (weights_shape.dimensions_size() > 0 &&
+                     weights_shape.dimensions(0) == 0),
+                errors::InvalidArgument(
+                    "`weights` must be the same shape as `arr` or a length-0 "
+                    "`Tensor`, in which case it acts as all weights equal to "
+                    "1. Received ",
+                    weights_shape.DebugString()));
+
     auto weights_size = weights_shape.dimensions(0);
     bool has_weights = false;
     if (weights_size) {

programming_language: C++
filename: bincount_op.cc
change_type: ModificationType.MODIFY
diff: @@ -280,6 +280,14 @@ class DenseBincountOp : public OpKernel {
     OP_REQUIRES(ctx, size_t.dims() == 0,
                 errors::InvalidArgument("Shape must be rank 0 but is rank ",
                                         size_t.dims()));
+    OP_REQUIRES(ctx,
+                weights.shape() == data.shape() || weights.NumElements() == 0,
+                errors::InvalidArgument(
+                    "`weights` must be the same shape as `arr` or a length-0 "
+                    "`Tensor`, in which case it acts as all weights equal to "
+                    "1. Received ",
+                    weights.shape().DebugString()));
+
     Tidx size = size_t.scalar<Tidx>()();
     OP_REQUIRES(
         ctx, size >= 0,

programming_language: C++
filename: bincount_op_test.py
change_type: ModificationType.MODIFY
diff: @@ -24,6 +24,7 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import bincount_ops
 from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops.ragged import ragged_factory_ops
 from tensorflow.python.ops.ragged import ragged_tensor
@@ -152,6 +153,31 @@ class BincountTest(test_util.TensorFlowTestCase):
       v2 = gen_math_ops.bincount([1, 2, 3, 1, 6, 8], s, [])
       self.assertAllEqual(v2.get_shape().as_list(), [None])
 
+  @test_util.run_in_graph_and_eager_modes
+  def test_invalid_inputs(self):
+    binary_output = True
+    inp = random_ops.random_uniform(
+        shape=[10, 10],
+        minval=-10000,
+        maxval=10000,
+        dtype=dtypes.int32,
+        seed=-2460)
+    size = random_ops.random_uniform(
+        shape=[], minval=-10000, maxval=10000, dtype=dtypes.int32, seed=-10000)
+    weights = random_ops.random_uniform(
+        shape=[],
+        minval=-10000,
+        maxval=10000,
+        dtype=dtypes.float32,
+        seed=-10000)
+    with self.assertRaises(errors.InvalidArgumentError):
+      self.evaluate(
+          gen_math_ops.dense_bincount(
+              input=inp,
+              size=size,
+              weights=weights,
+              binary_output=binary_output))
+
 
 class BincountOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
 

programming_language: Python