Vulnerability: Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow ### Impact
The implementation of [`tf.histogram_fixed_width`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/histogram_op.cc) is vulnerable to a crash when the values array contain `NaN` elements:

```python
import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/histogram_op.cc#L35-L74) assumes that all floating point operations are defined and then converts a floating point result to an integer index:

```cc
index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();
```

If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash.

This only occurs on the CPU implementation.

### Patches
We have patched the issue in GitHub commit [e57fd691c7b0fd00ea3bfe43444f30c1969748b5](https://github.com/tensorflow/tensorflow/commit/e57fd691c7b0fd00ea3bfe43444f30c1969748b5).

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.

### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/45770).
Vulnerability ID: GHSA-xrp2-fhq4-4q3w
CWE: CWE-20
Severity score: 5.5
Project: https://github.com/tensorflow/tensorflow
Published: 2022-05-24
File: tensorflow/core/kernels/histogram_op.cc
Commit message: Prevent crash when histogram is called with NaN values.

Fixes #45770

PiperOrigin-RevId: 443149951
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 struct HistogramFixedWidthFunctor<CPUDevice, T, Tout> {                         static_cast<double>(nbins);     const double nbins_minus_1 = static_cast<double>(nbins - 1);      // The calculation is done by finding the slot of each value in `values`.     // With [a, b]:     //   step = (b - a) / nbins class HistogramFixedWidthOp : public OpKernel {     const auto nbins = nbins_tensor.scalar<int32>()();      OP_REQUIRES(         ctx, (value_range(0) < value_range(1)),         errors::InvalidArgument("value_range should satisfy value_range[0] < "                                 "value_range[1], but got '[",                                 value_range(0), ", ", value_range(1), "]'"));     OP_REQUIRES(         ctx, (nbins > 0),         errors::InvalidArgument("nbins should be a positive number, but got '",                                 nbins, "'")); 

Fixed version (after fix):
 struct HistogramFixedWidthFunctor<CPUDevice, T, Tout> {                         static_cast<double>(nbins);     const double nbins_minus_1 = static_cast<double>(nbins - 1);      // We cannot handle NANs in the algorithm below (due to the case to int32)     const Eigen::Tensor<int32, 1, 1> nans_tensor =         values.isnan().template cast<int32>();     const Eigen::Tensor<int32, 0, 1> reduced_tensor = nans_tensor.sum();     const int num_nans = reduced_tensor(0);     if (num_nans > 0) {       return errors::InvalidArgument("Histogram values must not contain NaN");     }      // The calculation is done by finding the slot of each value in `values`.     // With [a, b]:     //   step = (b - a) / nbins class HistogramFixedWidthOp : public OpKernel {     const auto nbins = nbins_tensor.scalar<int32>()();      OP_REQUIRES(         ctx, value_range(0) < value_range(1),         errors::InvalidArgument("value_range should satisfy value_range[0] < "                                 "value_range[1], but got '[",                                 value_range(0), ", ", value_range(1), "]'"));     OP_REQUIRES(         ctx, nbins > 0,         errors::InvalidArgument("nbins should be a positive number, but got '",                                 nbins, "'")); 

Patch:
patch: @@ -50,6 +50,15 @@ struct HistogramFixedWidthFunctor<CPUDevice, T, Tout> {
                         static_cast<double>(nbins);
     const double nbins_minus_1 = static_cast<double>(nbins - 1);
 
+    // We cannot handle NANs in the algorithm below (due to the case to int32)
+    const Eigen::Tensor<int32, 1, 1> nans_tensor =
+        values.isnan().template cast<int32>();
+    const Eigen::Tensor<int32, 0, 1> reduced_tensor = nans_tensor.sum();
+    const int num_nans = reduced_tensor(0);
+    if (num_nans > 0) {
+      return errors::InvalidArgument("Histogram values must not contain NaN");
+    }
+
     // The calculation is done by finding the slot of each value in `values`.
     // With [a, b]:
     //   step = (b - a) / nbins
@@ -98,12 +107,12 @@ class HistogramFixedWidthOp : public OpKernel {
     const auto nbins = nbins_tensor.scalar<int32>()();
 
     OP_REQUIRES(
-        ctx, (value_range(0) < value_range(1)),
+        ctx, value_range(0) < value_range(1),
         errors::InvalidArgument("value_range should satisfy value_range[0] < "
                                 "value_range[1], but got '[",
                                 value_range(0), ", ", value_range(1), "]'"));
     OP_REQUIRES(
-        ctx, (nbins > 0),
+        ctx, nbins > 0,
         errors::InvalidArgument("nbins should be a positive number, but got '",
                                 nbins, "'"));
path: tensorflow/core/kernels/histogram_op.cc