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Moderate severityNVD Advisory· Published Sep 16, 2022· Updated Apr 23, 2025

`CHECK` fail in `DenseBincount` in TensorFlow

CVE-2022-35987

Description

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.

Affected packages

Versions sourced from the GitHub Security Advisory.

PackageAffected versionsPatched versions
tensorflowPyPI
< 2.7.22.7.2
tensorflowPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflowPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-cpuPyPI
< 2.7.22.7.2
tensorflow-cpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-cpuPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-gpuPyPI
< 2.7.22.7.2
tensorflow-gpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-gpuPyPI
>= 2.9.0, < 2.9.12.9.1

Affected products

1

Patches

1
bf4c14353c23

Fix security vulnerability with DenseBincountOp

https://github.com/tensorflow/tensorflowSagun BajraJul 13, 2022via ghsa
commit
3 files changed · +45 −0
  • tensorflow/compiler/tf2xla/kernels/bincount_op.cc+11 −0 modified
    @@ -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) {
  • tensorflow/core/kernels/bincount_op.cc+8 −0 modified
    @@ -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,
  • tensorflow/python/kernel_tests/math_ops/bincount_op_test.py+26 −0 modified
    @@ -24,6 +24,7 @@
 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 @@ def test_shape_function(self):
       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):

Vulnerability mechanics

Generated by null/stub on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

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